Matches in Nanopublications for { ?s <http://schema.org/description> ?o ?g. }
- pre-processing description "n maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transhipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) messages transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. The results show that the method can detect confirmed real-world intentional AIS shutdown operations." assertion.
- pre-processing description "n maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transhipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) messages transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. The results show that the method can detect confirmed real-world intentional AIS shutdown operations." assertion.
- vesseltype_identification_dae description "Private repository containing Anomaly Detection in Vessels Trajectories using Context-Aware Autoencoders" assertion.
- vesseltype_identification_dae description "Private repository containing Anomaly Detection in Vessels Trajectories using Context-Aware Autoencoders" assertion.
- 0AI6umItIl7BxUk9PVA description "Internally shared google drive with data and documents for T-SAR project" assertion.
- 0AI6umItIl7BxUk9PVA description "Internally shared google drive with data and documents for T-SAR project" assertion.
- apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf description "The surveillance of maritime traffic is confronted with very important difficulties in detecting illegal activities at sea. In this article, we present the first results of a self-supervised learning method which aims to detect voluntary disconnec- tions of the identification’ system of vessels. By processing data from four Norwegian surveillance satellites, our lear- ning model aims to identify vessels suspected of illegal acti- vities such as fishing in protected areas or crossing econo- mic exclusion zones in real time. In this article, we present an approach based on self-supervised learning techniques, and experienced from real data." assertion.
- apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf description "The surveillance of maritime traffic is confronted with very important difficulties in detecting illegal activities at sea. In this article, we present the first results of a self-supervised learning method which aims to detect voluntary disconnec- tions of the identification’ system of vessels. By processing data from four Norwegian surveillance satellites, our lear- ning model aims to identify vessels suspected of illegal acti- vities such as fishing in protected areas or crossing econo- mic exclusion zones in real time. In this article, we present an approach based on self-supervised learning techniques, and experienced from real data." assertion.
- 7998d851-41e8-4c51-aa06-deff6fd5f09a description "In transport infrastructures, vessel traffic services, air traffic management, and connected cars all rely on unauthenticated and unencrypted messages transfer that renders these services vulnerable to cyberattacks. Typical attacks such as False Data Injection Attacks (FDIA) are challenging to detect as they alter the semantics of the data (e.g., by adding/removing/multiplying elements on real-time control equipment), while preserving the syntactical correctness of the messages. Identifying these attacks and classifying them as serious threats or unintentional false data has become a significant challenge of traffic monitoring authorities. The TSAR project aims at demonstrating that recent advances in Artificial Intelligence (AI) can be leveraged in the automatic detection of FDIA in transport infrastructures. By combining realistic threat data generation based on constraint-based software testing techniques and automatic detection with deep reinforcement learning, TSAR will propose a new technology for automatic FDIA generation and detection. This technology will be empirically evaluated with end-users from the maritime domain and with open and accessible data in two other domains, namely air traffic control, and connected cars. By leveraging automatic detection of FDIA in traffic management systems, TSAR will also prepare the ground for the upcoming revolution in traffic management, which concerns, self-driving vessels, self-driving aircraft, and self-driving cars." assertion.
- 7998d851-41e8-4c51-aa06-deff6fd5f09a description "In transport infrastructures, vessel traffic services, air traffic management, and connected cars all rely on unauthenticated and unencrypted messages transfer that renders these services vulnerable to cyberattacks. Typical attacks such as False Data Injection Attacks (FDIA) are challenging to detect as they alter the semantics of the data (e.g., by adding/removing/multiplying elements on real-time control equipment), while preserving the syntactical correctness of the messages. Identifying these attacks and classifying them as serious threats or unintentional false data has become a significant challenge of traffic monitoring authorities. The TSAR project aims at demonstrating that recent advances in Artificial Intelligence (AI) can be leveraged in the automatic detection of FDIA in transport infrastructures. By combining realistic threat data generation based on constraint-based software testing techniques and automatic detection with deep reinforcement learning, TSAR will propose a new technology for automatic FDIA generation and detection. This technology will be empirically evaluated with end-users from the maritime domain and with open and accessible data in two other domains, namely air traffic control, and connected cars. By leveraging automatic detection of FDIA in traffic management systems, TSAR will also prepare the ground for the upcoming revolution in traffic management, which concerns, self-driving vessels, self-driving aircraft, and self-driving cars." assertion.
- 5bffb6c2-45e3-4a80-9242-5afd61c21063 description "papers, conference proceeding generated by the TSAR project." assertion.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 2759 description "Pangeo discourse announcement." assertion.
- 2759 description "Pangeo discourse announcement." assertion.
- environment.yml description "Conda environment for running DGGS notebook examples." assertion.
- environment.yml description "Conda environment for running DGGS notebook examples." 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.
- h3_intro.ipynb description "Jupyter Notebook demonstrating how to perform Spatial Data Analysis with H3." 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.
- 2274 description "Discussion from Pangeo Discourse on DGGS use with Pangeo." 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- notebook.html description "Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book" 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.
- 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.
- environment.yml description "Conda environment when user want to have the same libraries installed without concerns of package versions" 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-linux-64.lock description "Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" 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-osx-64.lock description "Lock conda file for osx-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.
- conda-win-64.lock description "Lock conda file for win-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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.README.html description "Admin 0 & Countries | Natural Earth" 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.VERSION.txt description "Version" 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.cpg description "cpg file from shapefile dataset." 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.
- ne_50m_admin_0_countries.prj description "Part of ne_50m_admin_0_countries shapefile (projection information)." assertion.
- ne_50m_admin_0_countries.prj description "Part of ne_50m_admin_0_countries shapefile (projection information)." assertion.