cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7415523
title: AIS data prepared by Statsat AS for February 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.7415522
title: AIS data prepared by Statsat AS for February 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: 10.6073/pasta/6db82a1d0e04142fbb4d74813b2ae28e
title: Long-term fish size data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012 (Reformatted to the ecocomDP Design Pattern)

description: This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/357/2. The abstract below was extracted from the Level 0 data package and is included for context: This dataset describes long-term (1944-2012) variations in individual fish total lengths from Wisconsin lakes. The dataset includes information on 1.9 million individual fish, representing 19 species. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
DOI: 10.18112/openneuro.ds004253.v1.0.7
title: HCP Unrelated 100 Subset (HCP-30)
DOI: 10.18112/openneuro.ds004253.v1.0.8
title: HCP Unrelated 100 Subset (HCP-30)
DOI: 10.5281/zenodo.7415591
title: Masks for hands in X-Ray images

description: Semantic segmentation masks for hands on scanned X-Rays from the RSNA Bone Age dataset. Mask were obtained manually using thresholding and edge detection and all masks were quality checked. Based on this two models (Tensormask and Efficient-UNet) were trained to obtain the masks on the full RSNA Bone Age dataset.
DOI: 10.5281/zenodo.7415590
title: Masks for hands in X-Ray images

description: Semantic segmentation masks for hands on scanned X-Rays from the RSNA Bone Age dataset. Mask were obtained manually using thresholding and edge detection and all masks were quality checked. Based on this two models (Tensormask and Efficient-UNet) were trained to obtain the masks on the full RSNA Bone Age dataset.
DOI: 10.18112/openneuro.ds004288.v1.0.5
title: Open Access Series of Imaging Studies Subset (AFIDs-OASIS)
DOI: 10.5281/zenodo.7415565
title: AIS data prepared by Statsat AS for March 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.7415564
title: AIS data prepared by Statsat AS for March 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: 10.3886/e183466
title: Sputnik_ 01.07.2022 - 01.10.2022 preiod

description: All Russia research Sputnik
DOI: 10.3886/e183466v1
title: Sputnik_ 01.07.2022 - 01.10.2022 preiod

description: All Russia research Sputnik
DOI: 10.17637/rh.21657083
title: Cassava Diversity datasets

description: Datasets of untargeted metabolomics analysis of cassava leaf and root tissue of Latin American cassava germplasm collection. Genotyping RADseq dataset of Latin American cassava germplasm collection.
DOI: 10.17637/rh.21657083.v1
title: Cassava Diversity datasets

description: Datasets of untargeted metabolomics analysis of cassava leaf and root tissue of Latin American cassava germplasm collection. Genotyping RADseq dataset of Latin American cassava germplasm collection.
DOI: 10.5281/zenodo.5536985
title: Syndecan-4 proteoglycan modulates glucose uptake and insulin sensitivity in myoblasts

description: Skeletal muscle glucose uptake is required for cellular metabolism and normalization of postprandial blood glucose levels. Transmembrane heparan sulfate proteoglycan syndecan-4 (SDC4) is important for muscle regeneration, cell interaction and structure, and it affects whole body metabolism, although the mechanism remains unclear. In this study, we demonstrated that SDC4 knock down increases glucose uptake by mammalian myoblasts (i.e., activated satellite cells). Silencing SDC4 increased the phosphorylation AS160 (Akt substrate of 160 kDa or TBC1D4), an important element of glucose transporter type 4 (GLUT4) translocation, and GLUT4 levels. These changes increased 2–deoxy-2–(<sup>18</sup>F)fluoro-D-glucose uptake by C2C12 myoblasts independent of insulin treatment. Mitochondrial routine respiration, electron transport system, reserve capacity, and oxidative phosphorylation were also increased following SDC4 silencing. SDC4 knockdown cells exhibited a higher GLUT4 density along the plasma membrane compared with behind it. Super-resolution direct stochastic optical reconstruction microscopy (dSTORM) imaging revealed an increased number of skeletons of the actin network following insulin treatment, but not in SDC4-silenced cells. Notably, knocking down SDC4 increased the insulin sensitivity of the cells. Our results suggest that this new mechanism can increase glucose uptake through insulin-independent signal transduction. Proteomics data.
description: Chromatogram libraries in .elib format can be viewed in Encyclopedia proteomics software.
DOI: 10.5281/zenodo.5536986
title: Syndecan-4 proteoglycan modulates glucose uptake and insulin sensitivity in myoblasts

description: Skeletal muscle glucose uptake is required for cellular metabolism and normalization of postprandial blood glucose levels. Transmembrane heparan sulfate proteoglycan syndecan-4 (SDC4) is important for muscle regeneration, cell interaction and structure, and it affects whole body metabolism, although the mechanism remains unclear. In this study, we demonstrated that SDC4 knock down increases glucose uptake by mammalian myoblasts (i.e., activated satellite cells). Silencing SDC4 increased the phosphorylation AS160 (Akt substrate of 160 kDa or TBC1D4), an important element of glucose transporter type 4 (GLUT4) translocation, and GLUT4 levels. These changes increased 2–deoxy-2–(<sup>18</sup>F)fluoro-D-glucose uptake by C2C12 myoblasts independent of insulin treatment. Mitochondrial routine respiration, electron transport system, reserve capacity, and oxidative phosphorylation were also increased following SDC4 silencing. SDC4 knockdown cells exhibited a higher GLUT4 density along the plasma membrane compared with behind it. Super-resolution direct stochastic optical reconstruction microscopy (dSTORM) imaging revealed an increased number of skeletons of the actin network following insulin treatment, but not in SDC4-silenced cells. Notably, knocking down SDC4 increased the insulin sensitivity of the cells. Our results suggest that this new mechanism can increase glucose uptake through insulin-independent signal transduction. Proteomics data.
description: Chromatogram libraries in .elib format can be viewed in Encyclopedia proteomics software.
DOI: 10.6084/m9.figshare.12071430
title: A Hierarchical Max-Infinitely Divisible Spatial Model for Extreme Precipitation

description: Understanding the spatial extent of extreme precipitation is necessary for determining flood risk and adequately designing infrastructure (e.g., stormwater pipes) to withstand such hazards. While environmental phenomena typically exhibit weakening spatial dependence at increasingly extreme levels, limiting max-stable process models for block maxima have a rigid dependence structure that does not capture this type of behavior. We propose a flexible Bayesian model from a broader family of (conditionally) max-infinitely divisible processes that allows for weakening spatial dependence at increasingly extreme levels, and due to a hierarchical representation of the likelihood in terms of random effects, our inference approach scales to large datasets. Therefore, our model not only has a flexible dependence structure, but it also allows for fast, fully Bayesian inference, prediction and conditional simulation in high dimensions. The proposed model is constructed using flexible random basis functions that are estimated from the data, allowing for straightforward inspection of the predominant spatial patterns of extremes. In addition, the described process possesses (conditional) max-stability as a special case, making inference on the tail dependence class possible. We apply our model to extreme precipitation in North-Eastern America, and show that the proposed model adequately captures the extremal behavior of the data. Interestingly, we find that the principal modes of spatial variation estimated from our model resemble observed patterns in extreme precipitation events occurring along the coast (e.g., with localized tropical cyclones and convective storms) and mountain range borders. Our model, which can easily be adapted to other types of environmental datasets, is therefore useful to identify extreme weather patterns and regions at risk. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.12071430.v4
title: A Hierarchical Max-Infinitely Divisible Spatial Model for Extreme Precipitation

description: Understanding the spatial extent of extreme precipitation is necessary for determining flood risk and adequately designing infrastructure (e.g., stormwater pipes) to withstand such hazards. While environmental phenomena typically exhibit weakening spatial dependence at increasingly extreme levels, limiting max-stable process models for block maxima have a rigid dependence structure that does not capture this type of behavior. We propose a flexible Bayesian model from a broader family of (conditionally) max-infinitely divisible processes that allows for weakening spatial dependence at increasingly extreme levels, and due to a hierarchical representation of the likelihood in terms of random effects, our inference approach scales to large datasets. Therefore, our model not only has a flexible dependence structure, but it also allows for fast, fully Bayesian inference, prediction and conditional simulation in high dimensions. The proposed model is constructed using flexible random basis functions that are estimated from the data, allowing for straightforward inspection of the predominant spatial patterns of extremes. In addition, the described process possesses (conditional) max-stability as a special case, making inference on the tail dependence class possible. We apply our model to extreme precipitation in North-Eastern America, and show that the proposed model adequately captures the extremal behavior of the data. Interestingly, we find that the principal modes of spatial variation estimated from our model resemble observed patterns in extreme precipitation events occurring along the coast (e.g., with localized tropical cyclones and convective storms) and mountain range borders. Our model, which can easily be adapted to other types of environmental datasets, is therefore useful to identify extreme weather patterns and regions at risk. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.7298/hck0-e345
title: Data from: Rates of ultrasonic vocalizations are more strongly related than acoustic features to non-vocal behaviors in mouse pups
DOI: 10.22002/e6ha1-cg439
title: Dataset: Cold shock protein RBM3 is upregulated in the autophagy-deficient brain

description: Relative gene expression in the cerebral cortices (Nestin-Cre; Atg7+/+ mice vs Nestin-Cre; Atg7f/f mice )
DOI: 10.5281/zenodo.7330382
title: Vaximap's home vaccination dataset (October 2022)

description: Dataset used in preparation for the publication "Vaximap: route optimisation for housebound vaccination", Nature Digital Health, 2022. DOI: 10.1038/s41746-022-00726-2. <strong>Please reference this publication when using this dataset. </strong> This dataset comprises requests made to the Vaximap service between Jan 2021 and Oct 2022 for planning vaccinations of housebound patients. Patient locations have been anonymised at the cost of some geographic precision. Data is stored in HDF5 format generated via python and pandas.to_hdf().
description: Work partly funded by EPSRC ICASE studentship (18000064) to RS.
description: {"references": ["Kirk T. F, Barker A. J, Bodossian A, Staruch R, (2022), Vaximap: route optimisation for housebound vaccination (dataset)"]}
DOI: 10.5281/zenodo.7330381
title: Vaximap's home vaccination dataset (October 2022)

description: Dataset used in preparation for the publication "Vaximap: route optimisation for housebound vaccination", Nature Digital Health, 2022. DOI: 10.1038/s41746-022-00726-2. <strong>Please reference this publication when using this dataset. </strong> This dataset comprises requests made to the Vaximap service between Jan 2021 and Oct 2022 for planning vaccinations of housebound patients. Patient locations have been anonymised at the cost of some geographic precision. Data is stored in HDF5 format generated via python and pandas.to_hdf().
description: Work partly funded by EPSRC ICASE studentship (18000064) to RS.
description: {"references": ["Kirk T. F, Barker A. J, Bodossian A, Staruch R, (2022), Vaximap: route optimisation for housebound vaccination (dataset)"]}
DOI: 10.6084/m9.figshare.21696584
title: Evaluation of Extracts from Sida acuta, Phyllanthus amarus, Parkia biglobosa and their Herbal Ointment for Therapeutic and Biological Activities

description: The data is on antibiotic sensitivity test showing various zones of inhibitions in duplicates with averages and standard deviations.
DOI: 10.6084/m9.figshare.21696584.v1
title: Evaluation of Extracts from Sida acuta, Phyllanthus amarus, Parkia biglobosa and their Herbal Ointment for Therapeutic and Biological Activities

description: The data is on antibiotic sensitivity test showing various zones of inhibitions in duplicates with averages and standard deviations.