cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21359745.v1
title: Supplementary Material for: Hospital-based study of risk factors associated with development of myopic macular neovascularization in highly myopic eyes

description: Introduction: Myopic macular neovascularization (MNV) is the most common cause of a reduction of central vision in eyes with pathologic myopia, and it can progress to macular atrophy in the long-term. The aim of this study was to determine the risk factors associated with the development of MNVs. Methods: There were 17,198 follow-up records from 5,409 eyes of 2,784 highly myopic patients that were reviewed. The general information and ophthalmic information in the records were studied. The significance of the correlations of factors associated with the development and predicting the development of myopic MNV were determined. Results: Being a woman (odds ratio [OR]: 0.727, P&lt;0.001), having a longer axial length (OR = 0.948, P&lt;0.001), a poorer baseline best-correct visual acuity (BCVA, OR = 2.098, P&lt;0.001), having severe myopic maculopathy (overall: P&lt;0.001), prior myopic MNV in the fellow eye (OR = 4.105, P&lt;0.001), presence of patchy atrophy (overall P&lt;0.001), lacquer cracks (OR = 1.718, P&lt;0.001), prior foveal retinal detachment (RD, OR = 3.269, P&lt;0.001), prior macular hole (MH, OR = 0.641, P &lt;0.001), prior macular retinoschisis (OR = 1.533, P&lt;0.001), and prior macular edema (OR = 1.508, P&lt;0.001) were significantly correlated with the development of myopic MNV. Eyes with MNV and patchy atrophy would require an intensive follow-up examination for myopic patients as the fellow eye would have a risk of &gt;70% for the development of myopic MNV in 3-years and nearly 80% in 5-years. Conclusions: Clinicians need to pay special attention to eyes with severe grades of myopic maculopathy, prior myopic MNV in the fellow eye, presence of patchy atrophy, and prior foveal retinal detachment to determine the onset of myopic MNV.
DOI: 10.6084/m9.figshare.21700937
title: e.vcf

description: <strong>Population structure, genetic diversity and prolificacy in sheep under an extreme desert environment</strong>
DOI: 10.6084/m9.figshare.21406935
title: Code for figures and statistics

description: Codes for Figures and statistics
DOI: 10.6084/m9.figshare.21406935.v2
title: Code for figures and statistics

description: Codes for Figures and statistics
DOI: 10.6084/m9.figshare.21406935.v1
title: Code for figures and statistics

description: Codes for Figures and statistics
DOI: 10.5281/zenodo.7416098
title: AIS data prepared by Statsat AS for September 2020

description: AIS data prepared and provided by Statsat AS (Norway) in the framework of the T-SAR project (<em>IKTPLUSS programme on reducing digital vulnerabilities, </em><strong>10 MNOK from the Research Council of Norway, Norway</strong>). The dataset contains AIS data (satellite + other) on a global coverage for 2020. There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. The csv files have one header line: mmsi;lon;lat;date_time_utc;sog;cog;true_heading;nav_status;rot;message_nr;source where: <strong>mmii</strong> integer MMSI number of the vessel (AIS identifier). All records belonging to the same vessel will have the same identifier. <strong>lon</strong> float Geographical longitude (WGS84) between -180 to 180 <strong>lat</strong> float Geographical latitude (WGS84) between -90 to 90 <strong>date_time_utc</strong> datetime Date and Time (in UTC) when position was recorded by AIS. It is represented as: YYYY-MM-DD HH:MM:SS (for instance 2020-01-01 00:00:00). <strong>sog</strong> float Speed over ground (knots) <strong>cog</strong> float Course over ground (degrees) <strong>true_heading</strong> integer Heading (degrees) of the vessel's hull. A value of 511 indicates there is no heading data. <strong>nav_status</strong> integer Navigation status according to <strong>AIS Specification</strong> <strong>rot</strong> integer rate of turn <strong>message_nr</strong> integer message number <strong>source</strong> integer source is the source of AIS data ('g' for ground or 's' for satellite). One row in the CSV file corresponds to one message.
description: This dataset is not public and has been provided by Statsat AS (Norway). It has been used for developing new machine learning algorithms for detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance in the framework of the T-SAR project (IKTPLUSS programme on reducing digital vulnerabilities, 10 MNOK from the Research Council of Norway, Norway) led by Simula Research Laboratory (Oslo, Norway).
DOI: 10.5281/zenodo.7416097
title: AIS data prepared by Statsat AS for September 2020

description: AIS data prepared and provided by Statsat AS (Norway) in the framework of the T-SAR project (<em>IKTPLUSS programme on reducing digital vulnerabilities, </em><strong>10 MNOK from the Research Council of Norway, Norway</strong>). The dataset contains AIS data (satellite + other) on a global coverage for 2020. There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. The csv files have one header line: mmsi;lon;lat;date_time_utc;sog;cog;true_heading;nav_status;rot;message_nr;source where: <strong>mmii</strong> integer MMSI number of the vessel (AIS identifier). All records belonging to the same vessel will have the same identifier. <strong>lon</strong> float Geographical longitude (WGS84) between -180 to 180 <strong>lat</strong> float Geographical latitude (WGS84) between -90 to 90 <strong>date_time_utc</strong> datetime Date and Time (in UTC) when position was recorded by AIS. It is represented as: YYYY-MM-DD HH:MM:SS (for instance 2020-01-01 00:00:00). <strong>sog</strong> float Speed over ground (knots) <strong>cog</strong> float Course over ground (degrees) <strong>true_heading</strong> integer Heading (degrees) of the vessel's hull. A value of 511 indicates there is no heading data. <strong>nav_status</strong> integer Navigation status according to <strong>AIS Specification</strong> <strong>rot</strong> integer rate of turn <strong>message_nr</strong> integer message number <strong>source</strong> integer source is the source of AIS data ('g' for ground or 's' for satellite). One row in the CSV file corresponds to one message.
description: This dataset is not public and has been provided by Statsat AS (Norway). It has been used for developing new machine learning algorithms for detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance in the framework of the T-SAR project (IKTPLUSS programme on reducing digital vulnerabilities, 10 MNOK from the Research Council of Norway, Norway) led by Simula Research Laboratory (Oslo, Norway).
DOI: 10.26106/jwn0-4064
title: The gut archeobiome - FASTQ files - concept
DOI: 10.6084/m9.figshare.15179720
title: Interrupted and uninterrupted lane changes: a microscopic outlook of lane-changing dynamics

description: Field observations suggest that the Lane-Changing (LC) processes are often interrupted by the surrounding vehicles. Understanding these intermediate interruptions and evasive behavior during an LC is necessary for developing realistic simulation tools, collision-warning systems, and human-like LC in self-driving vehicles. However, identifying and classifying LC into interrupted (ILC) and uninterrupted (ULC) is not straightforward. This study proposes a methodology for the automatic identification and classification of LC events. Further, we highlighted the characteristic differences between ILC and ULC by comparing their duration, crash likelihood, and driver discomfort. The likelihood, duration of exposure, and severity of LC crashes were captured using the surrogate safety measures such as Anticipated Collision Time (ACT), Time-Exposed ACT (TE-ACT), and Time-Integrated ACT (TI-ACT). The driver's discomfort was measured using acceleration noise and the number of sign inversions in the acceleration profile. The Kruskal–Wallis–ANOVA test on the above attributes and the sensitivity analysis of the duration models confirm that ILC and ULC are characteristically different.
DOI: 10.6084/m9.figshare.15179720.v1
title: Interrupted and uninterrupted lane changes: a microscopic outlook of lane-changing dynamics

description: Field observations suggest that the Lane-Changing (LC) processes are often interrupted by the surrounding vehicles. Understanding these intermediate interruptions and evasive behavior during an LC is necessary for developing realistic simulation tools, collision-warning systems, and human-like LC in self-driving vehicles. However, identifying and classifying LC into interrupted (ILC) and uninterrupted (ULC) is not straightforward. This study proposes a methodology for the automatic identification and classification of LC events. Further, we highlighted the characteristic differences between ILC and ULC by comparing their duration, crash likelihood, and driver discomfort. The likelihood, duration of exposure, and severity of LC crashes were captured using the surrogate safety measures such as Anticipated Collision Time (ACT), Time-Exposed ACT (TE-ACT), and Time-Integrated ACT (TI-ACT). The driver's discomfort was measured using acceleration noise and the number of sign inversions in the acceleration profile. The Kruskal–Wallis–ANOVA test on the above attributes and the sensitivity analysis of the duration models confirm that ILC and ULC are characteristically different.
DOI: 10.26106/b40d-1z28
title: The gut mycobiome - FASTQ files - concept
DOI: 10.5281/zenodo.7416092
title: AIS data prepared by Statsat AS for August 2020

description: AIS data prepared and provided by Statsat AS (Norway) in the framework of the T-SAR project (<em>IKTPLUSS programme on reducing digital vulnerabilities, </em><strong>10 MNOK from the Research Council of Norway, Norway</strong>). The dataset contains AIS data (satellite + other) on a global coverage for 2020. There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. The csv files have one header line: mmsi;lon;lat;date_time_utc;sog;cog;true_heading;nav_status;rot;message_nr;source where: <strong>mmii</strong> integer MMSI number of the vessel (AIS identifier). All records belonging to the same vessel will have the same identifier. <strong>lon</strong> float Geographical longitude (WGS84) between -180 to 180 <strong>lat</strong> float Geographical latitude (WGS84) between -90 to 90 <strong>date_time_utc</strong> datetime Date and Time (in UTC) when position was recorded by AIS. It is represented as: YYYY-MM-DD HH:MM:SS (for instance 2020-01-01 00:00:00). <strong>sog</strong> float Speed over ground (knots) <strong>cog</strong> float Course over ground (degrees) <strong>true_heading</strong> integer Heading (degrees) of the vessel's hull. A value of 511 indicates there is no heading data. <strong>nav_status</strong> integer Navigation status according to <strong>AIS Specification</strong> <strong>rot</strong> integer rate of turn <strong>message_nr</strong> integer message number <strong>source</strong> integer source is the source of AIS data ('g' for ground or 's' for satellite). One row in the CSV file corresponds to one message.
description: This dataset is not public and has been provided by Statsat AS (Norway). It has been used for developing new machine learning algorithms for detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance in the framework of the T-SAR project (IKTPLUSS programme on reducing digital vulnerabilities, 10 MNOK from the Research Council of Norway, Norway) led by Simula Research Laboratory (Oslo, Norway).
DOI: 10.5281/zenodo.7416091
title: AIS data prepared by Statsat AS for August 2020

description: AIS data prepared and provided by Statsat AS (Norway) in the framework of the T-SAR project (<em>IKTPLUSS programme on reducing digital vulnerabilities, </em><strong>10 MNOK from the Research Council of Norway, Norway</strong>). The dataset contains AIS data (satellite + other) on a global coverage for 2020. There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. The csv files have one header line: mmsi;lon;lat;date_time_utc;sog;cog;true_heading;nav_status;rot;message_nr;source where: <strong>mmii</strong> integer MMSI number of the vessel (AIS identifier). All records belonging to the same vessel will have the same identifier. <strong>lon</strong> float Geographical longitude (WGS84) between -180 to 180 <strong>lat</strong> float Geographical latitude (WGS84) between -90 to 90 <strong>date_time_utc</strong> datetime Date and Time (in UTC) when position was recorded by AIS. It is represented as: YYYY-MM-DD HH:MM:SS (for instance 2020-01-01 00:00:00). <strong>sog</strong> float Speed over ground (knots) <strong>cog</strong> float Course over ground (degrees) <strong>true_heading</strong> integer Heading (degrees) of the vessel's hull. A value of 511 indicates there is no heading data. <strong>nav_status</strong> integer Navigation status according to <strong>AIS Specification</strong> <strong>rot</strong> integer rate of turn <strong>message_nr</strong> integer message number <strong>source</strong> integer source is the source of AIS data ('g' for ground or 's' for satellite). One row in the CSV file corresponds to one message.
description: This dataset is not public and has been provided by Statsat AS (Norway). It has been used for developing new machine learning algorithms for detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance in the framework of the T-SAR project (IKTPLUSS programme on reducing digital vulnerabilities, 10 MNOK from the Research Council of Norway, Norway) led by Simula Research Laboratory (Oslo, Norway).
DOI: 10.4121/20438607.v1
title: Data underlying publication: Green turtles shape the seascape through grazing patch formation around habitat features: experimental evidence

description: This dataset contains the field data collected from field experiments studying the impact of habitat structure on turtle density, behavior and grazing impact
DOI: 10.4121/20438607
title: Data underlying publication: Green turtles shape the seascape through grazing patch formation around habitat features: experimental evidence

description: This dataset contains the data collected from field experiments studying the impact of habitat structure on green turtle density, behavior and grazing impact. In this study, we established large-scale (242m2) and small-scale arrays (9m2) with artificial structures in a a seagrass meadow in The Bahamas. Over time, within the large-scale array, we measured turtle density, turtle grazing behavior and grazing patch development using drone imagery. Additionally we measured Thalassia testudinum seagrass morphology (LAI, cover, shoot density and aboveground biomass) comparing seagrass in the grazing patch within cages and outside cages. To confirm that turtles select structure as foraging site, even at a small-scale, we measured grazing patch development around the structures in the small-scale arrays.
DOI: 10.4121/20438607.v2
title: Data underlying publication: Green turtles shape the seascape through grazing patch formation around habitat features: experimental evidence

description: This dataset contains the data collected from field experiments studying the impact of habitat structure on green turtle density, behavior and grazing impact. In this study, we established large-scale (242m2) and small-scale arrays (9m2) with artificial structures in a a seagrass meadow in The Bahamas. Over time, within the large-scale array, we measured turtle density, turtle grazing behavior and grazing patch development using drone imagery. Additionally we measured Thalassia testudinum seagrass morphology (LAI, cover, shoot density and aboveground biomass) comparing seagrass in the grazing patch within cages and outside cages. To confirm that turtles select structure as foraging site, even at a small-scale, we measured grazing patch development around the structures in the small-scale arrays.
DOI: 10.4121/19285106
title: Data underlying the publication “Feeding growing mushrooms”.

description: Button mushrooms (Agaricus bisporus) are cultivated on a substrate prepared of composted wheat straw, chicken and/or horst manure and gypsum. <br>This manuscript describes a number of experiments indicating that in a harvesting strategy towards quality mushrooms there is a fixed amount <br>of nutrients prepared/available to feed two flushes. The increase of mycelial biomass in the substrate after venting and especially during the <br>development of flush one indicates that likely a branching of mycelium occurs during this period, leading to a substantial number of growing <br>hyphal tips excreting lignocellulolytic enzymes that degrade substrate and make nutrients available during flush one which are utilised in <br>flush one and two. The presently used substrate seems to have reached its limits and there are hardly opportunities for further improvement. <br>Research on alternative, more energy dense, substrates are needed to improve the efficiency of the present production system.
DOI: 10.4121/19285106.v1
title: Data underlying the publication “Feeding growing mushrooms”.

description: Button mushrooms (Agaricus bisporus) are cultivated on a substrate prepared of composted wheat straw, chicken and/or horst manure and gypsum. <br>This manuscript describes a number of experiments indicating that in a harvesting strategy towards quality mushrooms there is a fixed amount <br>of nutrients prepared/available to feed two flushes. The increase of mycelial biomass in the substrate after venting and especially during the <br>development of flush one indicates that likely a branching of mycelium occurs during this period, leading to a substantial number of growing <br>hyphal tips excreting lignocellulolytic enzymes that degrade substrate and make nutrients available during flush one which are utilised in <br>flush one and two. The presently used substrate seems to have reached its limits and there are hardly opportunities for further improvement. <br>Research on alternative, more energy dense, substrates are needed to improve the efficiency of the present production system.
DOI: 10.5281/zenodo.7413807
title: Dataset: Automatic Derivation of Vulnerability Models for Software Architectures

description: Dataset for our publication "<em>Automatic Derivation of Vulnerability Models for Software Architectures</em>"
DOI: 10.6084/m9.figshare.21701015.v1
title: Supplementary Material for: Quantitative Evaluation of Changes in Three-Dimensional CT Density Distributions in Pulmonary Alveolar Proteinosis after GM-CSF Inhalation

description: <b><i>Background:</i></b> A previous clinical trial for autoimmune pulmonary alveolar proteinosis (APAP) demonstrated that granulocyte-macrophage colony-stimulating factor (GM-CSF) inhalation reduced the mean density of the lung field on computed tomography (CT) across 18 axial slice planes at a two-dimensional level. In contrast, in this study, we challenged three-dimensional analysis for changes in CT density distribution using the same datasets. <b><i>Methods:</i></b> As a sub-study of the trial, CT data of 31 and 27 patients who received GM-CSF and placebo, respectively, were analyzed. To overcome the difference between various shooting conditions, a newly developed automatic lung field segmentation algorithm was applied to CT data to extract the whole lung volume, and the accuracy of the segmentation was evaluated by five pulmonary physicians independently. For normalization, the percent pixel (PP) in a certain density range was calculated as a percentage of the total number of pixels from −1,000 to 0 HU. <b><i>Results:</i></b> The automatically segmented images revealed that the lung field was accurately extracted except for 7 patients with minor deletion or addition. Using the change in PP from baseline to week 25 (ΔPP) as the vertical axis, we created a histogram with 143 HU bins set for each patient. The most significant difference in ΔPP between GM-CSF and placebo groups was observed in two ranges: from −1,000 to −857 and −143 to 0 HU. <b><i>Conclusion:</i></b> Whole lung extraction followed by density histogram analysis of ΔPP may be an appropriate evaluation method for assessing CT improvement in APAP.
DOI: 10.2908/isoc_cismt
title: Social media use by type, internet advertising and size class of enterprise
title: Types de médias sociaux utilisés, publicité sur l'internet, par classe de taille d’entreprise
title: Arten genutzter sozialer Medien, Werbung im Internet, nach Unternehmensgrößenklassen