Matches in Nanopublications for { ?s <http://schema.org/description> ?o ?g. }
- 5bffb6c2-45e3-4a80-9242-5afd61c21063 description "papers, conference proceeding generated by the TSAR project." assertion.
- 636a64a4-ba87-4d16-8a67-455bfd74e7d6 description "Documentation and existing information about surveillance and detection of anomalies using Automatic Identification System data (ground and satellite)." assertion.
- bc6ab0f0-c443-4b11-a772-03fbcdb452c4 description "Bibliography collected on Automatic Identification System and detection of anomalies from AIS data (ground/satellite)." assertion.
- 3e6f07ae-3da5-43ab-a7f9-4334ee01b8d2 description "Distribution of samples on the surface of the globe." assertion.
- c7d9b7cb-7192-471b-82f4-13fe89dc6906 description "The NorSat-3 microsatellite will be launched into space during spring 2021 with a radar detector developed at the Norwegian Defence Research Establishment (FFI). It will provide improved surveillance capability of the shipping traffic in Norwegian national waters. File downloaded from the Norwegian Defence Research Establishment (https://publications.ffi.no/nb/item/asset/dspace:7059/FFI-Facts_NorSat_Engelsk_web_v2.pdf)." assertion.
- ed59cb0a-e359-4f95-932d-88375b08daa7 description "Major transportation surveillance protocols have not been specified with cyber securityin mind and therefore provide no encryption nor identification. These issues expose air and seatransport to false data injection attacks (FDIAs), in which an attacker modifies, blocks or emits fakesurveillance messages to dupe controllers and surveillance systems. There has been growing interestin conducting research on machine learning-based anomaly detection systems that address these newthreats. However, significant amounts of data are needed to achieve meaningful results with this typeof model. Raw, genuine data can be obtained from existing databases but need to be preprocessedbefore being fed to a model. Acquiring anomalous data is another challenge: such data is muchtoo scarce for both the Automatic Dependent Surveillance–Broadcast (ADS-B) and the AutomaticIdentification System (AIS). Crafting anomalous data by hand, which has been the sole methodapplied to date, is hardly suitable for broad detection model testing. This paper proposes an approachbuilt upon existing libraries and ideas that offers ML researchers the necessary tools to facilitatethe access and processing of genuine data as well as to automatically generate synthetic anomaloussurveillance data to constitute broad, elaborated test datasets. We demonstrate the usability of theapproach by discussing work in progress that includes the reproduction of related work, creation ofrelevant datasets and design of advanced anomaly" assertion.
- d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe description "This Research Object contains AIS data (raw and pre-processed by Statsat AS, Norway). It is not public and has been provided by Statsat AS (Norway). If you are working at Simula, information on where to find pre-processed data on the EX3 is given in the Data RO (README.txt in the metadata folder). This dataset 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)." assertion.
- jmse10010112 description "The automatic identification system (AIS) was introduced in the maritime domain to increase the safety of sea traffic. AIS messages are transmitted as broadcasts to nearby ships and contain, among others, information about the identification, position, speed, and course of the sending vessels. AIS can thus serve as a tool to avoid collisions and increase onboard situational awareness. In recent years, AIS has been utilized in more and more applications since it enables worldwide surveillance of virtually any larger vessel and has the potential to greatly support vessel traffic services and collision risk assessment. Anomalies in AIS tracks can indicate events that are relevant in terms of safety and also security. With a plethora of accessible AIS data nowadays, there is a growing need for the automatic detection of anomalous AIS data. In this paper, we survey 44 research articles on anomaly detection of maritime AIS tracks. We identify the tackled AIS anomaly types, assess their potential use cases, and closely examine the landscape of recent AIS anomaly research as well as their limitations." assertion.
- 2759 description "Pangeo discourse announcement." assertion.
- environment.yml description "Conda environment for running DGGS notebook examples." assertion.
- h3_intro.ipynb description "Jupyter Notebook demonstrating how to perform Spatial Data Analysis with H3." assertion.
- 2274 description "Discussion from Pangeo Discourse on DGGS use with Pangeo." assertion.
- bd43e723-e961-4558-9b20-68ebd4b34a9b description "A Discrete Global Grid Systems (DGGS) is a unique type of spatial reference system comprising of a hierarchy of uniquely identifiable discrete grid cells that span the globe at multiple resolutions. A DGGS can support efficient management, storage, integration, exploration, mining, and visualisation of large geospatial datasets, and several systems of tesselation and indexing schemes exist. The main topic of this session is to introduce the audience to the theoretical background of Discrete Global Grid Systems (DGGS), current real-world implementations and exemplary use cases. This includes grid generation, data indexing and sampling with DGGRID, and some spatial analysis with with H3 and rHealPix." assertion.
- 392f6daf-80e8-4691-a100-3a27db027fcc description "Slides for the presentation on DGGS given during Pangeo Show and Tell October 6, 2022 by Alex Kmoch." assertion.
- 8a387283-0d83-4b6a-9fda-f6aec378d7b5 description "A Discrete Global Grid System is a spatial reference system that uses a hierarchical tessellation of cells to partition and address the globe. OGC Abstract Specification, 2017" assertion.
- pangeo_dggs_2022 description "Github repository with examples used during the Pangeo Show and Tell - 06. Oct., 2022 on "DGGS and their potential impact in Geoscience and Geospatial" by Alexander Kmoch (Landscape Geoinformatics Lab, University of Tartu, Estonia). Twitter: @Lgeoinformatics │ @allixender" assertion.
- kkLRtyZtxs0 description "This YouTube video is part of the Pangeo Show & Tell series and was given on October 6 2022 by Alexander Kmoch, Department of Geography of the University of Tartu, (Estonia)." assertion.
- showandtell description "This is the shared document we use for all the Pangeo Show and Tell. We collect information, Q&A and feedback. Each Show and Tell has its own sub-section." assertion.
- showandtell description "This is the shared document we use for all the Pangeo Show and Tell. We collect information, Q&A and feedback. Each Show and Tell has its own sub-section." assertion.
- notebook.html description "Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- b128b282-dee7-44a7-bc21-f1fd21452a83 description "The research object refers to the Exploring Land Cover Data (Impact Observatory) notebook published in the Environmental Data Science book." assertion.
- environment.yml description "Conda environment when user want to have the same libraries installed without concerns of package versions" assertion.
- conda-linux-64.lock description "Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- conda-osx-64.lock description "Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- conda-win-64.lock description "Lock conda file for win-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- 0566d7df-d790-44bd-bcd1-fe89c0582a29 description "A carboxylic acid is an organic acid that contains a carboxyl group (C(=O)OH) attached to an R-group. The general formula of a carboxylic acid is R−COOH or R−CO2H, with R referring to the alkyl, alkenyl, aryl, or other group. Carboxylic acids occur widely. Important examples include the amino acids and fatty acids. Deprotonation of a carboxylic acid gives a carboxylate anion." assertion.
- 6aa4b4a0-c7dc-4762-aee1-e8dc94a1705c description "A carboxylic acid is an organic acid that contains a carboxyl group (C(=O)OH) attached to an R-group. The general formula of a carboxylic acid is R−COOH or R−CO2H, with R referring to the alkyl, alkenyl, aryl, or other group. Carboxylic acids occur widely. Important examples include the amino acids and fatty acids. Deprotonation of a carboxylic acid gives a carboxylate anion." assertion.
- b90bc0b8-2d26-4e0c-b255-c2399b52d45d description "A carboxylic acid is an organic acid that contains a carboxyl group (C(=O)OH) attached to an R-group. The general formula of a carboxylic acid is R−COOH or R−CO2H, with R referring to the alkyl, alkenyl, aryl, or other group. Carboxylic acids occur widely. Important examples include the amino acids and fatty acids. Deprotonation of a carboxylic acid gives a carboxylate anion." assertion.
- 07e76ee3-fe6c-4fb1-903d-3a8c5236e51b description "This Jupyter notebook is a tool that can load seabird (https://www.seabird.com/) (.cnv) and (.ros) files and plot discrete samples that where collected with the CTD rosette of the Niskin bottle at a predefined depth/pressure level. The tool is handy, when in-sea during a cruise, to compare and check the difference between the records of the dissolved oxygen bottle, the winkler oxygen, and the CTD profile of the water columns. ie. Winkler method is based on the titration to determine dissolved oxygen note: this is part of a series of notebooks to calibrate seabird oxygen sensor based on Winkler Derived Coefficients." assertion.
- dev description "YAXArrays.jl is another xarray-like Julia package. A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL. Package Features: - open datasets from a variety of sources (NetCDF, Zarr, ArchGDAL) - interoperability with other named axis packages through YAXArrayBase - efficient mapslices(x) operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets)" assertion.
- a802f7dc-f3f4-4eac-b69f-748fb90958fb description "This talk is part of the Pangeo Show & Tell series and was given on September 1st 2022 by Felix Cremer. Bio Felix Cremer received his diploma in mathematics from the University of Leipzig in 2014. In 2016 he started his PhD study on time series analysis of hypertemporal Sentinel-1 radar data. He currently works at the Max-Planck-Institute for Biogeochemistry on the development of the JuliaDataCubes ecosystem in the scope of the NFDI4Earth 5 project. Abstract The Earth Data Lab (EDL) is a data cube framework in Julia for the efficient handling of raster data. It is based on the YAXArrays.jl package. YAXArrays.jl provides functionality to deal with labelled arrays, similar to the xarray python package and it also provides efficient and easy multithreading and distributed computation of user defined functions along arbitrary slices of the data. EarthDataLab.jl uses DiskArrays.jl in the backend to deal with out of memory datasets. In this Show-and-Tell Felix is going to give a short introduction into the EarthDataLab.jl package for raster data handling in Julia." assertion.
- 2ada4d46-d001-4d7c-904b-d5f4667f4dd2 description "Plot from the Julia Jupyter notebook." assertion.
- ne_50m_admin_0_countries.README.html description "Admin 0 & Countries | Natural Earth" assertion.
- ne_50m_admin_0_countries.VERSION.txt description "Version" assertion.
- ne_50m_admin_0_countries.cpg description "cpg file from shapefile dataset." assertion.
- ne_50m_admin_0_countries.prj description "Part of ne_50m_admin_0_countries shapefile (projection information)." assertion.
- overallintro.ipynb description "Jupyter Notebook used by Felix during the Pangeo Show & Tell to demonstrate how to use EarthDataLab.jl to do large scale computations. To execute this Jupyter Notebook, data contained in the "input folder" is needed (please create a folder called "data" in the folder where you have stored the notebook)." assertion.
- ESDLTutorials description "This will become a selection of tutorials on the use of ESDL.jl and YAXArrays.jl julia packages for the handling of large scale out-of-core geospatial datasets." assertion.
- ne_50m_admin_0_countries.dbf description "Part of ne_50m_admin_0_countries shapefile." assertion.
- ne_50m_admin_0_countries.shp description "Part of ne_50m_admin_0_countries shapefile." assertion.
- ne_50m_admin_0_countries.shx description "Part of ne_50m_admin_0_countries shapefile." assertion.
- 18_e8wmI9Os description "This is the recorded talk from Felix Cremer during the Pangeo Show & Tell in September 1st, 2022. Felix is going through his Julia Notebook and explain us about handling large geo data with Julia." assertion.
- 2656 description "You will find here all the information published to advertise the Pangeo Show & Tell Talk frm Felix Cremer on "Handling large geo data with Julia "." assertion.
- b7f139b2-b89b-494a-8687-8f3fc4aaae83 description "A dedicated workflow in ArcGIS was developed to identify targets from the bathymetry within the MAELSTROM Project - Smart technology for MArinE Litter SusTainable RemOval and Management" assertion.
- 33180bf8-3307-4e46-97cb-5efcfaaa583d description "Here some results" assertion.
- 348a403c-6567-4905-90f8-186f7fb3a558 description "Related documents and resources" assertion.
- 53880215-19eb-4373-8319-fdc74c7f7508 description "Data input" assertion.
- bcc4d42f-ac3e-40d4-a5fe-8f26bf7e6b8d description "It contains Jupyter notebook" assertion.
- 21c5bfb9-d2c8-4c02-9c90-5e845d8f75e9 description "Shape file with targets detected from bathymetry" assertion.
- ae49cef0-be33-4dd7-87f5-f57d8a6bab8e description "This paper presents a semi-automated method to recognize, spatially delineate and characterise morphometrically pockmarks at the seabed" assertion.
- b14d905d-32b8-43af-91b9-9f413262aaa6 description "This Notebook provides a workflow of ArcGis toolboxes to identify ML targets from bathynetry." assertion.
- c0a4c2a7-2d85-4cce-8672-7427e724b37a description "Bathymetry metadata description" assertion.
- c8f3ab93-383e-46e8-8c31-d3f496497b44 description "Track of a net from water coloumn data" assertion.
- d9974021-2fde-4a67-a231-b6c372eaac5b description "ArcGis workflow output metadata" assertion.
- ed2cffa7-9560-408a-853a-6a1ddc315f3c description "Bathymetric data from Sacca Fisola 2021 survey" assertion.
- fa46d31f-220e-4363-87d8-74de8afcf73a description "calculates the sink velocity of a net floating in water starting from water column data" assertion.
- prate.sfc.mon.mean.nc description "Contains input of the Jupyter Notebook - Concatenating a gridded rainfall reanalysis dataset into a time series used in the Jupyter notebook of Concatenating a gridded rainfall reanalysis dataset into a time series" assertion.
- 1520-0477(1996)077%3C0437:TNYRP%3E2.0.CO;2 description "Related publication of the exploration presented in the Jupyter notebook" assertion.
- ea34568e-d86e-4720-be2f-3f826f66a26c description "The research object refers to the Concatenating a gridded rainfall reanalysis dataset into a time series notebook published in the Environmental Data Science book." assertion.
- BAMS-D-20-0117.1 description "Related publication of the exploration presented in the Jupyter notebook" assertion.
- zenodo.6824189 description "Contains outputs, (figures), generated in the Jupyter notebook of Concatenating a gridded rainfall reanalysis dataset into a time series" assertion.
- environment.yml description "Conda environment when user want to have the same libraries installed without concerns of package versions" assertion.
- notebook.ipynb description "Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- conda-osx-64.lock description "Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- requirements.txt description "Pip requirements file containing libraries to install after conda lock" assertion.
- notebook.html description "Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- 095cd4b8-7027-4b97-bf9d-511fc5351d6d description "Data on beach litter" assertion.
- 0d96ad94-c73d-4ac8-b781-d9d481aebffe description "experiment" assertion.
- 6447cf9d-8185-40fb-b70e-06a5733655c6 description "Jupyter Notebook to analyse the changes in NO2 during the Covid-19 Lockdown in the Venice Lagoon. Datasets are from Copernicus Atmosphere Monitoring Forecasts and in-situ measurement for water quality." assertion.
- 903fc676-99d5-4277-be11-e83883e70fdd description "In the framework of the MAELSTROM Project, scientists and local managers are monitoring marine litter on the beaches of the Venice Lagoon using a web app. The results of this monitoring activities are visible through this Research Object which automatically transform the data in graphs." assertion.
- 675e7351-fe5b-4af8-9ae5-28021a10d105 description "The folder contains the data produced by the Jupyter notebook" assertion.
- 72c4635a-a6b0-4f4a-99a2-eb7b560b7911 description "The folder contains the data used as input by the Jupyter notebook" assertion.
- 38ea97e8-0dc1-4a13-a789-799294024fa9 description "Marine litter recorded along the Alberoni beach of the Lagoon Venice monitored by WWF in the framework of the MAELTROM Project" assertion.
- 706c3bc2-a04d-4e34-b547-2f6cf0011481 description "Graph generated by the Jupyter notebook showing all the litter occurrences recorded along the Alberoni beach (Venice Lagoon) in 2022" assertion.
- content description "Jupyter notebook building the graphs starting from the data collected by the web app for marine liter monitoring" assertion.
- 1be0f190-6a64-4696-89ba-3509748d84fa description "The goal is to generate automatically a RO from a DMP using RDA DMP Common Standard for Machine-actionable DMP." assertion.
- 747252 description "Monitoring Santorini volcano (Greece) breathing from space - by M. Foumelis, E. Trasatti, E. Papageorgiou, S. Stramondo, I. Parcharidis Geophys. J. Int., 2013. https://doi.org/10.1093/gji/ggs135" assertion.
- 747252 description "Monitoring Santorini volcano (Greece) breathing from space - by M. Foumelis, E. Trasatti, E. Papageorgiou, S. Stramondo, I. Parcharidis Geophys. J. Int., 2013. https://doi.org/10.1093/gji/ggs135" assertion.
- full description "Volcanic and Seismic Source Modeling: An Open Tool for Geodetic Data Modeling - by E. Trasatti Frontiers in Earth Science, 2022. https://doi.org/10.3389/feart.2022.917222" assertion.
- 52c0fa97-af5b-4227-ac70-c19b3fd612cc description "This Research Object has been created using the Reliance services during the demo of 7th July 2022. It contains results from the run of the VSM code, related to the modelling of the inflation phase at Santorni during 2011-2012." assertion.
- 87734470-1fce-4d70-8e30-981508cfc325 description "Requirements for the environment tu run the notebook" assertion.
- 882ea3c5-595d-400a-bb6d-5cabe7fda52c description "Data - Model - Residuals with InSAR descending data" assertion.
- 8bb59f59-798b-424c-956d-2c47820e42bb description "This notebook contains the instructions to create this RO. Also, it contains the code to access the EGI Datahub, generate the link for resources and add it to the RO." assertion.
- e78c73b5-e49d-415c-b466-12331990906c description "Image of the deformation monitored in Santorini (Greece)" assertion.
- content description "Jupyter Notebook for running the VSM code with geodetic data" assertion.
- d939573f74a3b7c797f620b5e37d8c7cch9233 description "GNSS data from 2011-2012 at Santorini (Greece)" assertion.
- da79e5451d8999784e657defb5f27d7achade2 description "Subsampled descending ENVISAT data from 2011-2012 at Santorini (Greece)" assertion.
- 7b86ece5-b588-416b-9c98-30bb63a5b9bc description "This RO provides the Jupyter notebook used to process the Sound Pressure Levels, SPL, data obtained within the Soundscape Project - SOUNDSCAPES IN THE NORTH ADRIATIC SEA AND THEIR IMPACT ON MARINE BIOLOGICAL RESOURCES (https://www.italy-croatia.eu/web/soundscape) where more of 1 year of continuos underwater noise data (march 2020 - june 2021) were recorded. SPL data were calculated from wav data recorded by Develogic SonoVault Hydrophones (https://w3id.org/ro-id/6640422d-57ed-4814-b0d0-8eb4ee85f501)." assertion.
- 0739c5c9-ee01-4ed1-93b1-56717f5c33cb description "data input" assertion.
- 17ed3654-c098-435c-a59e-0a766fb7ed0e description "Jupyter notebooks here" assertion.
- 2c8425a2-4854-4e8e-afbd-0218c3ac0b44 description "Here some information" assertion.
- 71f95fcf-a1d5-45ae-9abf-1977bc9aa966 description "Here some results" assertion.
- 63076219-d95a-4ffa-be04-e8c9ead051b7 description "Jupyter notebook for processing SPL data." assertion.
- 912d02f1-4c08-46ce-8d75-ea2f16520385 description "Example of SPL input file. HDF5 format, according to to ICES (International Council for the Exploration of the Sea) continuous noise data portal specification (https://www.ices.dk/data/data-portals/Pages/Continuous-Noise.aspx)." assertion.
- a124318c-b696-4515-a1a1-6b37818b6db0 description "Map of stations with their coordinates" assertion.
- b0fadc73-e542-4e0e-be88-473cd883bbf9 description "Some examples of output files" assertion.
- zenodo.6653258 description "Full SPL dataset is located in Zenodo" assertion.
- 3a5459f8-8145-4062-b51b-86e7edfb8208 description "Sentinel-5P: SO2 total column (OFFL) - time range: 2022-06-26T19:57:45Z/2018-11-28T12:46:38Z - min/max Value: -10/60 - DataType: Float32 - Resolution: 0 -" assertion.
- 14349e56-1829-41de-b721-f66467217b00 description "desc" assertion.
- 23d1da17-c362-4cc6-984b-97e7564b5902 description "desc8" assertion.