Nanopublications LDF server

Matches in Nanopublications for { ?s <http://schema.org/description> ?o ?g. }

• ba53e480-17bb-466f-b789-3533246d7b43 description "Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom." assertion.
• de0b3951-0fa7-4b03-a1fa-d5c4da93a476 description "Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom." assertion.
• 2f432569-3648-4885-bb84-bc9507c5187a description "**Session at the European Climate Change Adaptation Conference, Rimini - Italy** *16-18 July 2025* This session was part of the HEurope FAIR2Adapt and CLIMATE-ADAPT4EOSC projects that intend to improve the efficiency of the data-to-knowledge supply chain in the field of climate change adaptation (CCA). In line with ECCA’s theme ‘Managing cities to be fit for the future’, the main goal of this session is to demonstrate how an ecosystem of FAIR (Findable, Accessible, Interoperable, and Reusable) technologies and services can support CCA stakeholders (e.g., researchers and practitioners) in their decision-making processes. The panel presentations will feature concrete examples of how FAIR tools and approaches can help CCA researchers and practitioners overcome barriers to accessing, using, and reproducing data and generating interoperable services. In addition, this session aims to gather information on the breadth of knowledge CCA stakeholders work with and how this knowledge is produced, providing key insights for FAIR experts who need a clear understanding of user requirements to design effective FAIR tooling." assertion.
• 447465f2-4083-4d02-a034-ef45bc9455cd description "Session presentation at the European Climate Change Conference 2025, in Rimini - Italy" assertion.
• 05729783-a960-4fbd-b1f6-f83fb23eb44c description "# Improving data availability for climate risk assessments under the EU taxonomy for sustainable activities ## Vision & Ambitions In this case study, we want to: 1) Provide insights into data used and needed by businesses and consultants to perform climate risk assessments for reporting under the EU taxonomy for sustainable activities; 2) Collect local climate hazard data that is being used in such analyses; 3) improve accessibility to key datasets for climate risk analyses under the EU taxonomy. ## Description Climate risk analyses under the EU taxonomy for sustainable activities (Pan-European) More and more companies in the EU are required to report on how their economic activities are contributing to the six environmental objectives of the EU taxonomy for sustainable activities. In this context, the Commission Delegated Regulation (EU) 2021/2178 requires companies to perform climate risk assessment for two possible purposes: to show that an economic activity is contributing to climate change adaptation (environmental objective #2) or to check whether an activity that contributes to another objective will do so even under a changing climate (do-no-significant-harm check). The regulation has detailed requirements that a climate risk analysis needs to fulfil, e.g. climate projections at the smallest appropriate scale and at least 28 different climate hazards need to be considered. Experience shows that business as well as consultants are struggling with the very demanding requirements and the very parcelled climate hazard data landscape. While there is a lot of data available on the 28 climate-related hazards that businesses need to analyse, data is not tagged / structured in a way that users can easily find it. Many businesses have operations across different EU countries which makes it even more challenging to conduct a harmonised assessment for all relevant locations and at the most appropriate local scale. Furthermore, many businesses will be required to prepare an adaptation plan that includes adaptation measures and is in line with local, regional and national adaptation strategies. ## FAIR2Adapt Contribution By accompanying two businesses in preparing climate risk assessments as part of their EU taxonomy reporting, will provide an overview on the sources that are being used for the assessments, from local to national to European sources. Furthermore, we shall document important data gaps and highlight possible difficulties when combining the different datasets during the assessments, e.g. problems because of different temporal or spatial scales. Guided by approaches from WP 3 to 5, strategies to make the most valuable data FAIR, using a vocabulary that is consistent with the requirements in the EU taxonomy, will be developed. ## Lead Partner ADELPHI ## External Stakeholders involved in CCA Two businesses that have operations in multiple EU countries, and have to report under the EU taxonomy." assertion.
• 5e53ec13-7cdb-4e41-95aa-f48b4206ead4 description "Research Object about the FAIR2Adapt Case Study on Improving data availability for climate risk assessments under the EU taxonomy for sustainable activities" assertion.
• 5954ce8e-2bbc-469a-b5ba-0d4d1e93195f description "The FAIR2Adapt Data Management Plan will evolve during the lifetime of the project in order to present the status of the project's reflections on data management. Our DMP will be made publicly available on ROHub so that the up to date version can be consulted at any time by everyone." assertion.
• 3afecf07-51dc-430a-8bce-8e3120f8489b description "This folder contains the links to the FIPs." assertion.
• f152dc94-e6c8-4232-b301-951ce2fc37f2 description "Snapshot of the DMP questionnaire (FIP Wizard) from the 29 June 2025." assertion.
• 7d647ed2-6698-4be3-ac81-502329066b3c description "FAIR2Adapt DMP generated from DSW questionnaire." assertion.
• HFR-TT_workshop_Lerici_2025_training_JupyterNotebook description "This Python-based Jupyter notebook (EU_HFR_NODE_Lerici2025.ipynb) focuses on providing basic routines for inspecting the HFR-derived surface current dataset produced by the operational Near Real Time (NRT) workflow of the European HFR Node. The inspection is useful for assessing the datasets and modify the processing and QC parameters, if needed." assertion.
• 5de51258-491e-407c-9e2c-1d68720ee92c description "The EuroGOOS HF Radar Task Team helps coordinate the European activities around the development and use of High Frequency Radar (HFR) technology. The Task Team is providing a European HF Radar operational network delivering data and products for science, environmental management, and operational needs. As all EuroGOOS operational task teams, the HF Radar Task Team plays an important role in identifying research gaps, delivering common standards and promoting synergy, towards an integrated European Ocean Observing System (EOOS). The EuroGOOS HF Radar Task Team contributes to improving administrative procedures, promotes scientific synergies and complementarity with other technologies as well as modeling products. The Task Team’s broad network allows sharing success stories and discuss common challenges, to allow a stronger joint progress. The EuroGOOS HFR Task Team is coordinating the European High Frequency Radar Node (EU HFR Node) as the focal point and operational asset in Europe for HFR data management and dissemination, also promoting networking between EU infrastructures and the Global HFR network. The EuroGOOS HFR Task Team is used to organize two progress meetings per year, one online and one in person. On September 3 2024, the 2024 in person meeting was held at the University of Plymouth as a side event of the International Radiowave Oceanography Workshop (ROW). The main points discussed during the meeting were: * review of the Terms of Reference of the Task Team * presentation of the main achievements of 2024 * analysis of the main remaining challenges * completion of the list of prioritized objectives for 2024-2026 A dedicated hands-on session about the assessment of the processing and Quality Control (QC) parameters used in the operational workflow of the European HFR Node." assertion.
• adeca79d-4bd5-4e9b-a433-81855f6a3dd9 description "Hands-on session on the assessment of the processing and QC parameters used in the EU HFR Node NRT operational workflow" assertion.
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• saarland-flooding.git description "This Github repository contains the latest version of the jupyter notebooks showcasing JupyterGIS." assertion.
• saarland-flooding.git description "This Github repository contains the latest version of the jupyter notebooks showcasing JupyterGIS." assertion.
• zenodo.15470110 description "Saarland flooding (Galaxy History) with outputs and executed notebooks." assertion.
• zenodo.15470110 description "Saarland flooding (Galaxy History) with outputs and executed notebooks." assertion.
• environment.lock.yml description "Python environment for executing the associated Jupyter notebook." assertion.
• environment.lock.yml description "Python environment for executing the associated Jupyter notebook." assertion.
• environment.yml description "environment.yml (Conda environment for Python JupyterGIS)" assertion.
• environment.yml description "environment.yml (Conda environment for Python JupyterGIS)" assertion.
• get_typename_from_WFS.ipynb description "## Get data layer names from WFS service URL **Learn how to get the typename (e.g. data layers) which are requested for querying WFS services**" assertion.
• get_typename_from_WFS.ipynb description "## Get data layer names from WFS service URL **Learn how to get the typename (e.g. data layers) which are requested for querying WFS services**" assertion.
• saarland-flooding description "Jupyter Book (HTML rendered version) to showcase JupyterGiS in action using flooding event data from Saarland, Germany, available via the Urban Data Portal." assertion.
• saarland-flooding description "Jupyter Book (HTML rendered version) to showcase JupyterGiS in action using flooding event data from Saarland, Germany, available via the Urban Data Portal." assertion.
• document description "As a contribution to the development of new techniques to remove marine litter from the seabed of sees and oceans, the Robotic Seabed Cleaning Platform has been designed, built and experimented in the framework of the European Union project MAELSTROM. It es sentially consists of a oating platform that supports the base elements of a 6 degree-of-freedom cable-driven parallel robot actuated by eight winches. The mobile platform of this robot can work underwater and is equipped with sensors to control its underwater motions and to detect & identify marine litter. To achieve e cient and selective litter removal, an aspiration system and a gripper are installed on the CDPR underwater mobile platform." assertion.
• document description "The multiplication of publicly available datasets makes it possible to develop Deep Learning models for many real-world applica tions. However, some domains are still poorly explored, and their related datasets are often small or inconsistent. In addition, some biases linked to the dataset construction or labeling may give the impression that a model is particularly efficient. Therefore, evaluating a model requires a clear understanding of the database. Moreover, a model often reflects a given dataset’s performance and may deteriorate if a shift exists between the training dataset and real-world data. In this paper, we derive a more consistent and balanced version of the TrashCan [6] image dataset, called UNO, to evaluate models for de tecting non-natural objects in the underwater environment. We pro pose a method to balance the number of annotations and images for cross-evaluation. We then compare the performance of a SOTA object detection model when using TrashCAN and UNO datasets. Addition ally, we assess covariate shift by testing the model on an image dataset for real-world application. Experimental results show significantly better and more consistent performance using the UNO dataset. The UNO database and the code are publicly av https://github.com/CBarrelet/balanced_kfold" assertion.
• 5c2b7b95-a99c-4489-b0ac-f656a65b90f7 description "This objective supports the implementation of the UN SDG 14 Life Below Water, by addressing two major challenges: (i)Surface and Upper Water Column litter: Floating plastics carried by rivers and harbours currents must be intercepted and captured before they enter the oceans and either sink to the seabed or create gyres MAELSTROM will deploy an automated air-bubbles barrier installed in optimized locations to retrieve these floating items, and also contribute to the re-oxygenation of the rivers, harbours and lagoons. (ii)Seabed and Lower Water Column: For ML hotspots in coastal waters, MAELSTROM will provide a robotized cable robot capable of high efficiency removal of small and large debris on seabed and in the lower water column." assertion.
• 13952176 description "Three underwater macro-litter image datasets for object detection in YOLO format: UNO dataset: All details are available in From TrashCan to UNO: Deriving an Underwater Image Dataset to Get a More Consistent and Balanced Version, C.Barrelet et al, ICPR 2022 MORGANE dataset: Images taken in shallow water in the harbors near Montpellier, France VENICE dataset: Images taken during the MAELSTROM experiments in Venice, Italy" assertion.
• 14929590 description "Petrizzo, A., MOSCHINO, V., Madricardo, F., Ghezzo, M., Galvez, D., Rodriguez, M., & ferrari, . nicola . (2022). D5.1 Report on site identification and installation of the Seabed Cleaning System in Venice. MAELSTROM Project. https://doi.org/10.5281/zenodo.14929590" assertion.
• 14929678 description "Gouttefarde, M., & Barrelet, C. (2024). D3.3 Preliminary report on the Cable robot autonomous control using Machine learning for litter identification. MAELSTROM Project. https://doi.org/10.5281/zenodo.14929678" assertion.
• 14930072 description "Ehrhorn, P., Iglesias, I., Vieira, L., & Sousa Pinto, I. (2021). D5.3 Definition of location for surface/water column removal device in the Porto region, Portugal. MAELSTROM Project. https://doi.org/10.5281/zenodo.14930072" assertion.
• 14931001 description "Rodriguez, M., & Gouttefarde, M. (2022). D3.1 Report on the cable robot design and teleoperated control. MAELSTROM Project. https://doi.org/10.5281/zenodo.14931001" assertion.
• 14931140 description "Gouttefarde, M., & Barrelet, C. (2024). D3.2 Report and videos about the cable robot control with shared autonomy. MAELSTROM Project. https://doi.org/10.5281/zenodo.14931140" assertion.
• 15544106 description "Iglesias, I., Vieira, L., Antunes, S., Kett, G., Fantinati, D. del O. A., Nogueira, S., Bio, A., Sousa-Pinto, I., Buschman, F. A., Mira Veiga, J., Pessoa, A., Zingariello, D., & Mule'Stagno, L. (2025). D5.4 Final Report on operation of the surface and water column removal technology in the Porto region. MAELSTROM Project. https://doi.org/10.5281/zenodo.15544106" assertion.
• 15546289 description "Ferrari, N., Fantin, A., Rodríguez Mijangos, M., Sallé, D., Herve, P.-E., Gorrotxategi, J., Oyarzabal, A., Culla, D., Gouttefarde, M., Creuze, V., Barrelet, C., Temperini, H. O., Petrizzo, A., MOSCHINO, V., Mesghez, S., Lahami, T., Lorenzetti, G., & Madricardo, F. (2025). D5.2 Final report on operation of the Seabed Cleaning System in Venice. MAELSTROM Project. https://doi.org/10.5281/zenodo.15546289" assertion.
• cdd7e89f-7013-45cd-9d15-00c4d7fe19fa description "The robotic seabed cleaning platform developed by TECNALIA, CNRS- LIRMM and “Servizi Tecnici”, consists in a floating platform which, through cables and winches, the seabed cleaning robot is attached. The structure is equipped with a set of sensors for underwater perception to control the robot and detect & identify the marine litter to be removed. Moreover, the robotic platform is characterized by two different tools that allow to collect the ML on the seabed: a drudge to suck up smaller litter and a gripper to grasps larger items like tires, parts of boats, fishing nets etc." assertion.
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• 5784402a-b47e-4560-b6ef-c9fecfef2115 description "The research object refers to the Livestock detection using DeepForest notebook published in the Environmental Data Science book." assertion.
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• d5430aa5-7a8b-44fe-8d21-6a7c80ac36d4 description "# Galaxy Workflow Rerun Information **Workflow:** Climate Stripes **Execution Status:** scheduled **Executed:** 2025-05-24 11:15:41.585048 ## Workflow Inputs ### Formal Input Definitions - **ts_cities.csv** (File) ### Actual Input Files Used - **ts_cities.csv** - Format: `text/plain` - Path: `datasets/ts_cities.csv_31e7840b5aedca433fb349714141a239.tabular` ## Workflow Parameters - **input:** - __class__: `NoReplacement` - **adv:** - colormap: `RdBu_r` - format_date: `` - format_plot: `` - nxsplit: `None` - xname: `` - **ifilename:** - __class__: `ConnectedValue` - **title:** `My ScienceLive Stripes` - **variable:** `tg_anomalies_freiburg` ## Workflow Outputs ### Formal Output Definitions - **stripes.png** (File) ### Actual Output Files Generated - **stripes.png** - Format: `application/octet-stream` - Path: `datasets/stripes.png_31e7840b5aedca43c0a4f330c3d24460.png` ## Rerun Template To rerun this workflow: 1. **Workflow:** Climate Stripes 2. **Required inputs:** - ts_cities.csv (type: `File`) 3. **Parameters to set:** - input: - __class__: `NoReplacement` - adv: - colormap: `RdBu_r` - format_date: `` - format_plot: `` - nxsplit: `None` - xname: `` - ifilename: - __class__: `ConnectedValue` - title: `My ScienceLive Stripes` - variable: `tg_anomalies_freiburg` 4. **Expected outputs:** - stripes.png (type: `File`)" assertion.
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