Matches in Nanopublications for { ?s <http://schema.org/description> ?o ?g. }
- 1a9af765-caf3-4053-bbe6-e7f4b7240f1a description "This case study targets the Mediterranean cold water coral habitats that develop under thermal conditions very close to 14°C. Maps of the current cold water coral distribution in the Mediterranean Sea will be correlated to physical parameters (i.e. Temperature) from field data and regional model Med-CORDEX. the effect of the increase of temperature and its consequence on cold water coral habitat loss." assertion.
- 7a1fc890-bfc1-41dc-a241-ce06901bd054 description "Literature on the distribution of CWC in the Mediterranean Sea" assertion.
- f7609946-b35f-42ea-914b-d74cbf6e8579 description "Data on temperature, salinity, dissolved oxygen, pH, and nutrient concentrations in seawater used to explore how environmental variables influence the distribution of CWC in the Mediterranean Sea" assertion.
- a443bee6-0bb2-43eb-9428-906af2273a19 description "The Secure Generative Data Exchange (SGDE) tool assists AI developers by facilitating the training, collection, and distribution of data generators, mitigating challenges related to data access, transfer, privacy, and cost. SGDE allows training of these generators on edge devices where data is collected, enabling sharing of generators, and helping to create unlimited synthetic samples for new machine learning tasks. The SGDE protocol initiates on the edge device, subsequently uploading the trained generator to a central server; clients can then download these generators and create synthetic samples, reducing distribution biases, due to the equal class representation of synthetic data." assertion.
- 1835 description "" assertion.
- 1c9bfc94-dbb9-475e-af50-601bff9f6c0c description "This RO provides the ADAM collection of the Sentinel-1 dataset over Etna volcano based on the LiCSAR catalogue." assertion.
- cf84e531-56d3-43ee-8362-c340d0addf30 description "This case study targets the Mediterranean cold water coral (CWC) habitats that develop under thermal conditions very close to 14°C. Maps of the current cold water coral distribution in the Mediterranean Sea will be correlated to physical parameters (i.e. Temperature) from field data and regional model Med-CORDEX. Considering that intermediate and deep water in the Mediterranean sea temperature is around 14°C, this suggests that most of the cold water coral species in the region thrive very close to their physiological threshold. Future scenarios are provided based on regional models (e.g. Med-CORDEX). the objective is to investigate future scenarios and predictions in case of a an increased of temperature and its consequents on CWC habitat loss." assertion.
- 049cc3f2-2b96-4ac7-8776-c095c522f492 description "Seawater temperature from MEDCORDEX output model: CNRM-CM5, variable: monthly seawater temperature (thetao) from (1995-2005)" assertion.
- 137b72a2-daac-4f87-8df9-df6204ff1ec4 description "environment" assertion.
- 19523e42-6e90-4500-a738-b75d0c9804f5 description "map of Cold water corals spatial distribution with 14°C isotherm depth predictions from RCP 8.5 (2070-2100) scenarios" assertion.
- 34af0c5f-8e14-4d77-83f6-2be63542cb14 description "Seawater temperature from MEDCORDEX output model: CNRM-CM5, variable: monthly seawater temperature (thetao) RCP 8.5 (2070-2100)scenarios" assertion.
- 359e6d79-d0fa-42c3-812a-588c42720d80 description "the coastline of the Mediterranean sea" assertion.
- 3b0ec1d0-a90f-40c8-a4f1-98df08b92c77 description "CWC spatial distribution color-coded with depth against the varibility of the Isotherm 14°C depth upper panel: historical data (1995-2005) and lower panel: RCP 8.5 scenario (2070-2100)" assertion.
- 6fd4cc21-2dc3-41d9-96e4-0ddac96fdda1 description "Med-CORDEX initiative has been proposed by the Mediterranean climate research community as a follow-up of previous and existing initiatives. Med-CORDEX takes advantage of new very high-resolution Regional Climate Models (RCM, up to 10 km) and of new fully coupled Regional Climate System Models (RCSMs), coupling the various components of the regional climate" assertion.
- 9e3b4fd3-ab98-4611-8b6c-277fdda86c50 description "Jupyter notebook to plot Cold Water Corals and 14°C isotherm depth scenarios" assertion.
- b05dac24-5771-42b9-8e3d-acf5d0e7b87c description "Cold water coral location: longitude, latitude and depths" assertion.
- c1410daa-1c5b-4a64-928a-80e5570bd638 description "This python notebook will download automatically the files you need from the model https://www.medcordex.eu/ and create directories to save them." assertion.
- f15ca50f-edbd-4f30-aaf0-e6256532849e description "code#1: reads netcdf data of seawater temperature from MEDCORDEX output model: CNRM-CM5 variable: monthly seawater temperature (thetao) code#2: This script reads netcdf data of seawater temperature from MEDCORDEX output model: CNRM-CM5 variable: monthly seawater temperature (thetao) code#3: This script reads in 14°C isotherm depth from historical (1995-2005) and RCP 8.5 (2070-2100)scenarios and plot data" assertion.
- 9a0df9ca-1970-4edf-9815-4a2f15702046 description "The eruption of Etna 28 February 2021 was seen by the MODIS sensor during the passage of the satellite NASA-Terra alle 09:40 UTC. In this research object a test to extract the plume pixel using AOT retrieval at 0.55 micron for both ocean and land is performed." assertion.
- 6a31b3fb-6a4d-4731-9cca-4f5d33574dd2 description "This is test of DMP creation.This is the data management plan (DMP) of RELIANCE H2020 project. It outlines data that will be collected or generated during the project and discusses how it will be handled during and after the project lifetime. As part of the Open Research Data Pilot (ORDP), RELIANCE delivers a first version of the DMP at an early stage of the project. This first version focusses on already identified research data to be collected, used and/or produced by the project, particularly by our scientific communities as part of the implementation of their use cases, but also the user data collected by the RELIANCE services. This DMP will be updated regularly to reflect significant changes, e.g., new data is used/produced." assertion.
- 1b8f1898-7ae4-4847-aeb3-3519a062d0c8 description "The research object refers to the Sea ice forecasting using IceNet notebook published in the Environmental Data Science book." assertion.
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- 770d97b4-b101-4af4-95d9-c94b72e16c1a description "Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
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- 780441ef-e2fc-4b9d-968a-e6878460a7e1 description "Contains input Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning' used in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- 7a1dc762-dcb5-4176-89c9-839be2492364 description "Contains outputs, (table and figures), generated in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- 8a99ffd1-faa4-4e15-9dd5-908551341d99 description "Related publication of the modelling presented in the Jupyter notebook" assertion.
- b574c292-7294-4e31-9247-1092bdbe2de4 description "Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- c9e0d4d8-8ea3-40bb-a655-29d06c09f9b2 description "Contains input Dataset for IceNet's demo notebook used in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- e458161e-0fdc-4b9d-be31-8fa388ba409c description "Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- fb3a8b1f-7132-4c0e-80c8-33ff294808da description "This RO provides the ADAM collection of the Sentinel-1 dataset over Iceland based on the LiCSAR catalogue." assertion.
- 3daef89c-f0cf-4d92-9dc1-045ae1442625 description "LICSAR interferograms dataset based on Sentinel-1 SAR data since 2016 over Iceland" assertion.
- 2b9f4a1a-d72d-4e02-bd58-8ee96e7a224d description "The research object refers to the Sea ice forecasting using IceNet notebook published in the Environmental Data Science book." assertion.
- 014e2516-2042-454e-8fa9-36c7e0f48fa5 description "Contains input Dataset for IceNet's demo notebook used in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- 0eea17b9-0aca-4d38-b4c6-ec89972f1b1c description "Contains input Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning' used in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- 49744ebb-2ee9-43ae-b19d-d5785c05050f description "Conda environment when user want to have the same libraries installed without concerns of package versions" assertion.
- 4d9a3542-997a-4c3f-b4ef-7f41c79cf950 description "Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- 6b333b2f-6744-41e0-9e89-1ccdda05828b description "Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- a112ec16-7272-43d3-ae40-f6731b16247b description "Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- c4857fe3-ec0b-4bed-b2a8-4cca7f0167db description "Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- ca55eaa8-5f4d-4737-a83e-bed520aaee00 description "Contains outputs, (table and figures), generated in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- e98edbc3-ddf4-432a-9632-662587d3c565 description "Related publication of the modelling presented in the Jupyter notebook" assertion.
- b7a61a55-7fcb-40ae-a2ae-595d346c02ab description "The field of Open Science has made scientists agree on the idea that data, workflows and services should be findable, accessible, interoperable, and thus optimally reusable (FAIR). These principles apply to Earth Science communities also, dealing with rapidly evolving natural phenomena. However, there is still a weakness regarding research sharing and re-use through the scientific community, due to lack of technological solutions and their long-term implementation. The H2020 Reliance project delivers a suite of innovative and interconnected services that extend European Open Science Cloud (EOSC) capabilities to support the management of the research lifecycle within Earth Science communities, Copernicus users, and beyond. The project has delivered three complementary technologies: Research Object, Data Cubes and AI-based Text Mining. ROHub (https://reliance.rohub.org/) is the Research Object management platform that implements these three technologies and enables researchers to collaboratively manage, share and preserve their research work. RoHub implements the full Research Object model and paradigm: resources associated to a particular research work are aggregated into a single FAIR digital object, and metadata relevant for understanding and interpreting the content is represented as semantic metadata that are user and machine readable." assertion.
- 03e3f3ce-db5f-4a59-a882-ad74fb8819cd description "sketch" assertion.
- afaa8f23-1d10-4fcf-8694-38e8f63873ad description "Abstract of the poster presented at EGU General assembly, Vienna, 25 April 2023" assertion.
- dafbc003-62e7-4ca4-949e-abb5b300cc2e description "poster" assertion.
- ab3f22c0-7006-4f8b-9a0c-f604654241d8 description "Demo app for state tagging approach for QA/QC of environmental data" assertion.
- 0894811f-c181-43d9-9f4d-934ee8aeec82 description "This R application is an implementation of state tagging approach for improved quality assurance of environmental data. The application returns state-dependent prediction intervals on input data. The states are determined based on clustering of auxiliary inputs (such as meteorological data) made on the same day. The method provides contextual information to assess the quality of observational data and is applicable to any point-based, daily time series observational data. To use this application, the user will need to input two separate csv files: one for state variables and the other for observations. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability." assertion.
- 0b51eade-52ce-4ab3-9001-7967562198c9 description "This RO is created as part of the mini workshop on RoHub during CW23." assertion.
- b5cf3c4b-4247-43ac-9f87-3780a5ad74cd description "CW23 Test fork from: The research object refers to the Tree crown delineation using detectreeRGB notebook published in the Environmental Data Science book." assertion.
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- 8ce94b7c-9e2d-4cef-b936-5055fb9c5721 description "Contains outputs, (vector, raster and figures), generated in the Jupyter notebook of Tree crown delineation using detectreeRGB" assertion.
- bef0788b-55ac-47d6-8ce5-2f730692a504 description "Contains input Datasets of detectreeRGB AI4ER MRes Project used in the Jupyter notebook of Tree crown delineation using detectreeRGB" assertion.
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- 3282 description "" assertion.
- b7481ff6-d1a1-4fd4-b205-c6e497e8d71c description "The research object refers to the Sea ice forecasting using IceNet notebook published in the Environmental Data Science book." assertion.
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- notebook.html description "Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
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- zenodo.5516869 description "Contains input Dataset for IceNet's demo notebook used in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- zenodo.5516869 description "Contains input Dataset for IceNet's demo notebook used in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- zenodo.5516869 description "Contains input Dataset for IceNet's demo notebook used in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- zenodo.5516869 description "Contains input Dataset for IceNet's demo notebook used in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- zenodo.5516869 description "Contains input Dataset for IceNet's demo notebook used in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
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- zenodo.6410246 description "Contains outputs, (table and figures), generated in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- zenodo.6410246 description "Contains outputs, (table and figures), generated in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- zenodo.6410246 description "Contains outputs, (table and figures), generated in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- zenodo.6410246 description "Contains outputs, (table and figures), generated in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- zenodo.6410246 description "Contains outputs, (table and figures), generated in the Jupyter notebook of Sea ice forecasting using IceNet." assertion.
- 71820e7d-c628-4e32-969f-464b7efb187c description "Contains input Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning' used in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- 71820e7d-c628-4e32-969f-464b7efb187c description "Contains input Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning' used in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- 71820e7d-c628-4e32-969f-464b7efb187c description "Contains input Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning' used in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- 71820e7d-c628-4e32-969f-464b7efb187c description "Contains input Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning' used in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- 71820e7d-c628-4e32-969f-464b7efb187c description "Contains input Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning' used in the Jupyter notebook of Sea ice forecasting using IceNet" assertion.
- 71820e7d-c628-4e32-969f-464b7efb187c description "Contains input Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning' used in the Jupyter notebook of Sea ice forecasting using IceNet." assertion.
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- environment.yml description "Conda environment when user want to have the same libraries installed without concerns of package versions" assertion.