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The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at the measurement pole near the Zeebrugge harbour 3.65km from land, often called MOW1, in Belgium (M1BE). It is a subset of the complete data record which consists of the best quality M1BE measurements which could be used for satellite validation. The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:10.2307/3841124). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as: \(\rho_wnosc=\pi (Lu-\rho_FLd)/E_d\) where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). For the M1BE site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided: \(\rho_w=\pi (Lu-\rho_FLd)/E_d-\epsilon\) These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: 10.1109/IGARSS47720.2021.9553738; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included. To obtain this dataset, we start from the full M1BE data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation: 1. The coefficient of variation in water reflectance is below 10% in the 500-600 nm range 2. The water reflectance at 500 nm is below 0.1 The data consists of 73 spectra ranging from 20230226T1431 till 20230429T1502. Coordinates of the site are the following: site_latitude = 51.360548 site_longitude = 3.118246 The site is owned by Afdeling Kust (https://www.agentschapmdk.be/nl). HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Afdeling kust (www.agentschapmdk.be) is greatly acknowledged for the maintenance of the MOW1 measurement tower and their support for the HYPSTAR® deployment. {"references": ["Ruddick et al., 2006, DOI:10.2307/3841124", "Goyens et al. 2021, DOI: 10.1109/IGARSS47720.2021.9553738"]}

The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at the Gironde Estuary, MAGEST Network, in France (MAFR). It is a subset of the complete data record which consists of the best quality MAFR measurements which could be used for satellite validation. The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances without NIR Similarity Correction (see Ruddick et al., 2006, DOI:10.2307/3841124). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as: ρwnosc=π(Lu−ρFLd)/Ed where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: 10.1109/IGARSS47720.2021.9553738; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included. To obtain this dataset, we start from the full MAFR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation: 1. The coefficient of variation in water reflectance is below 10% in the 600-700 nm range {"references": ["Ruddick et al., 2006, DOI:10.2307/3841124", "Goyens et al. 2021, DOI: 10.1109/IGARSS47720.2021.9553738"]} HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993.

The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at Aqua Alta, Venice in Italy (VEIT). It is a subset of the complete data record which consists of the best quality VEIT measurements which could be used for satellite validation. The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:10.2307/3841124). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as: \(\rho_wnosc=\pi (Lu-\rho_FLd)/E_d\) where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). For the VEIT site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided: \(\rho_w=\pi (Lu-\rho_FLd)/E_d-\epsilon\) These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: 10.1109/IGARSS47720.2021.9553738; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included. To obtain this dataset, we start from the full VEIT data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation: 1. The coefficient of variation in water reflectance is below 10% in the 580-600 nm range 2. The water reflectance (after correction for the NIR similarity) above 800 nm is below 0.01 HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. If you intend to use the following data please consult the author(s) of the dataset and always cite the dataset as suggested by "Cite as". {"references": ["Ruddick et al., 2006, DOI:10.2307/3841124", "Goyens et al. 2021, DOI: 10.1109/IGARSS47720.2021.9553738"]}

The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Rio de La Plata, LPAR, in Argentina. It is a subset of the complete data record which consists of the best quality LPAR measurements which could be used for satellite validation. The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances (without NIR Similarity Correction, see Ruddick et al., 2006, DOI:10.2307/3841124). The reflectance in the L2A products is the Water Reflectance (referred to as reflectance_nosc in the file) defined as: ρwnosc=π(Lu−ρFLd)/Ed where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: 10.1109/IGARSS47720.2021.9553738; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included. To obtain this dataset, we start from the full LPAR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation: 1. The coefficient of variation in water reflectance is below 10% in the 400-900 nm range 2. The water reflectance at 500 nm is below 0.1 {"references": ["Goyens et al. 2021, DOI: 10.1109/IGARSS47720.2021.9553738", "Ruddick et al., 2006, DOI:10.2307/3841124"]} HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993.

The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at Etang de Berre in France (BEFR). It is a subset of the complete data record which consists of the best quality BEFR measurements which could be used for satellite validation. The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:10.2307/3841124). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as: ρwnosc=π(Lu−ρFLd)/Ed where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). For the BEFR site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided: ρw=π(Lu−ρFLd)/Ed−ϵ These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: 10.1109/IGARSS47720.2021.9553738; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included. To obtain this dataset, we start from the full BEFR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation: 1. The coefficient of variation in water reflectance is below 10% in the 500-600 nm range 2. The water reflectance (after correction for the NIR similarity) between 700-900 nm is below 0.01 {"references": ["Ruddick et al., 2006, DOI:10.2307/3841124", "Goyens et al. 2021, DOI: 10.1109/IGARSS47720.2021.9553738"]} HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993.

This repository contains data sets that were submitted to and analysed in the ocean branch of phase two of the REgional Carbon Cycle Assessment and Processes (RECCAP2-ocean) project. Protocols defining the criteria for data set submissions to RECCAP2-ocean are available through the project website: https://reccap2-ocean.github.io/protocols/ A detailed description of the submitted data sets is provided in three files contained in the supplementary.tar file. These are: An overview table of all regularly submitted data sets including contact details for each submission is provided in RECCAP2-ocean_data_products_overview.xlsx. The documentation of a quality control exercise is provided in QC_results_RECCAP2-ocean.pdf. All regular data sets have been quality controlled (i.e. checked for consistency with the submission protocol and general plausibility of results) by the participants of RECCAP2 (i.e. the users of the data sets). A documentation of remaining data set issues that were identified after the initial quality control is provided in Remaining_data_set_issues.pdf. These remaining issues have not been corrected. All originally submitted files were unpacked, compressed, and repacked into tar files before submission to Zenodo. The individual submitted data sets are broadly grouped into four classes indicated by the prefix of the tar files: models: Global and regional ocean biogeochemical hindcast models (GOBM/ROBM) as well as ocean data-assimilation models (data-assimilation models) surface_co2: Surface ocean pCO2-observation products (pCO2 products) ocean_interior: Ocean interior DIC-observation products (DIC products) atmospheric _inversions: Atmospheric inversion models Some additional files that were only used by individual chapters of RECCAP2-ocean and were not quality controlled are contained in the supplementary.tar file. Jens Daniel Müller, RECCAP2-ocean coordinator, May 2023 on behalf of the entire RECCAP2-ocean team {"references": ["Poulter, B., Bastos, A., Canadell, J., Ciais, P., Gruber, N., Hauck, J., et al. (2022). Inventorying Earth's Land and Ocean Greenhouse Gases. Eos, 103. https://doi.org/10.1029/2022eo179084", "Sarma, V. V. S. S., Sridevi, B., Metzl, N., Patra, P. K., Lachkar, Z., Chakraborty, K., et al. (2023). Air-Sea Fluxes of CO2 in the Indian Ocean Between 1985 and 2018: A Synthesis Based on Observation-Based Surface CO2, Hindcast and Atmospheric Inversion Models. Global Biogeochemical Cycles, 37(5), e2023GB007694. https://doi.org/10.1029/2023GB007694"]} We acknowledge the support by the Max Planck Institute for Biogeochemistry, namely A. Bastos and F. Gans, in hosting and curating a temporary repository for the RECCAP2-ocean data collection.

Dataset underlying the analysis in Hauck et al., 2023: Sparse observations induce large biases in estimates of the global ocean CO2 sink - an ocean model subsampling experiment, Philosophical Transactions A Surface ocean partial pressure of CO2 (pCO2) and air-sea CO2 flux reconstructions, using two mapping methods (MPI-SOM-FFN, CarboScope) three different sampling masks: SOCAT, SOCAT+SOCCOM, IDEAL (based on bgcArgo, Roemmich et al., 2019). Also, all FESOM-REcoM output fields that were used in the reconstructions are provided. We further provide the three masks that were used for subsampling: SOCAT, SOCAT+SOCCOM, IDEAL (bgcArgo). {"references": ["Hauck et al., 2023: Sparse observations induce large biases in estimates of the global ocean CO2 sink - an ocean model subsampling experiment, Phil. Trans. R. Soc. A 381: 20220063. https://doi.org/10.1098/rsta.2022.0063"]}

A comprehensive catalog of 19,791 marine prokaryotic isolates (WGS), single-amplified genomes (SAGs) and metagenomic-assembled genomes (MAGs) compiled from MarRef v4.0 (N=943, mostly high-quality WGS), MarDB v4.0 (N=12,963), and the aquatic representative genomes from the ProGenomes database v1.0 (N=566). This collection of well-documented genomes was complemented by 5,319 MAGs assembled from four distinct studies, namely: Parks et al. 2017 (DOI; N=1,765; downloaded from EBI), Tully et al. 2017/2018 (DOI and DOI; N=2,597; downloaded from EBI), and Delmont et al. 2018 (DOI; N=957; downloaded from FIGSHARE). The Parks et al. study contained genomes reconstructed from non-marine biomes. Thus, a selection of 1,765 genomes was extracted by searching for specific keywords: “tara|marine|sea|ocean|mediterranean” (case insensitive). Note that depending on their study of origin, included MAGs may have been reconstructed using different assembling and binning methods. The archive includes: a metadata file describing the quality and redundancy of the genomes named `EcoSysMic_metadata.tsv` sequences of the 19,791 (redundant) genomes in `All/WGS` companion files in `All/Data` and `dRep95/Data` (see Methods in the associated paper), including predicted CDS and EggNOG functional annotations predicted GTDB taxonomy CarveMe reconstructed metabolic models and their MEMOTE quality The 7,658 non-redundant species-level genomes (delineated by a 95% ANI threshold over 60% of genome length) that were used in the associated paper are defined by the column `is_drep95` in the metadata file.

There are several research infrastructures or other data services running in Europe that cover a multitude of marine-related sciences, providing specific datasets coming from observations collected with different methods. These infrastructures constitute a diverse world, each looking at a piece of the big picture, sometimes hindering collaboration and data sharing. Blue-Cloud aims to overcome fragmentation and build a bridge between thematic science clusters - such as marine, climate, food and agriculture sciences - and EOSC, creating a data federation and providing a common access to a so-called thematic EOSC for marine data. By connecting leading marine data management infrastructures with horizontal e-infrastructures, the project aims to maximise the exploitation of data resources available from different sources. The Blue-Cloud framework consists of two major technical components: (1) a Blue-Cloud Data Discovery and Access service, already presented in a previous EOSC in practice story, to serve federated discovery and access to blue data infrastructures, and (2) a Blue-Cloud Virtual Research Environment (VRE) to provide computing platforms and analytical services facilitating the collaboration between researchers, which is detailed hereafter. The Blue-Cloud VRE is powered by the D4Science Infrastructure. [M. Assante et al. (2019) Enacting open science by D4Science. Future Gener. Comput. Syst. 101: 555-563 10.1016/j.future.2019.05.063 ] The full list of EOSC in practice stories is available here