dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank the  Agence Nationale de la Recherche  for its financial support to  Luc Beaufort  and  Martin Tetard  under projects  ANR-12-BS06-0007-CALHIS  and  JPI Belmont ANR-15-JCLI-0003-05-PACMEDY. We thank  IODP France  for its financial support to  Martin Tetard. This work is a contribution to the  EU FP7 project MedSeA  (grant agreement no. 265103 ). Samples from the WNP were studied in the framework of the Shirshov Institute state assignment (theme no. 0128-2021-0006,  Ekaterina Ovsepyan ). Many thanks to the professional captains and crews of the research vessels  Marion Dufresne ,  Sonne , Meteor, and  Olaya. We would also like to thank  Franck Bassinot ,  Frans Jorissen , and  Helena Filipsson  for their valuable contributions to this paper. Ekaterina Ovsepyan  is thankful to  Elena Ivanova  for collecting core top samples from the WNP.
Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant nos. ANR-12-BS06-0007-CALHIS  and  ANR-15-JCLI-0003-05-PACMEDY ) and the  European Commission, Seventh Framework Programme (MedSeA  (grant no. 265103 )). Review statement. This paper was edited by  Hiroshi Kitazato  and reviewed by two anonymous referees.
Code availability. The code use for the morphometric analysis of images is available at https://doi. org/10. 5281/zenodo. 4740079  (Tetard, 2021)  and also in the Supplement of the present publication as a file named "MorFo_. ijm". Data availability. All the data used for calibration of the methods presented in this study are available in Tables  1  and 2. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-18-2827-2021-supplement.
According to  Shepherd (2018)  "to provide a more integrated approach to marine data, allowing the user to search for, visualize, and retrieve in a common way all measurements with one single command wherever the data are stored, the EU embarked upon the construction of a European Marine Observation and Data Network (EMODnet). To maximise innovation and minimise bureaucracy, marine data should be free of charge and free of restrictions on use". Continuous engagement across the diversity of stakeholders, societal actors, and economic sectors is the key to pursue sustainable management of the human activities in the ocean. While Ocean Literacy has been recognized as a key enabler of virtuous engagement, and relevant initiatives have been built targeting the Mediterranean, a consistent effort should be devoted to connecting experiences, exchanging practices, and supporting collaboration among the actors working on science communication and outreach, harmonized among the riparian countries.
). Offshore waters of the Mediterranean Sea are characterized as oligotrophic, but the highly populated coastal zone and the riverine input induce eutrophic trends in coastal areas  (Sammartino et al. , 2015). The main coastal areas in the Mediterranean which are historically known to be affected by eutrophication are the Gulf of Lion, the Gulf of Gabès, the Adriatic Sea, the North Aegean Sea and the South-East Mediterranean  (Colella et al, 2016). The assessment most often show that these areas do not reach GES (UNEP/MAP, 2017). Using satellite and model data from the European Commission's Copernicus Marine Environment and Monitoring Service (CMEMS, www. marine. copernicus. eu), UNEP MAP (2017) reported an overall increasing trend of chlorophyll concentration in the Mediterranean Sea over the past twenty years, and a clear increase in the western part of the basin.
As part of its Marine Knowledge 2020 strategy, the European Commission's Directorate-General for Maritime Affairs and Fisheries (DG MARE) launched the European Marine Observation and Data Network (EMODnet, https://emodnet. eu/en) initiative undertaken by partnership of over a hundred and fifty organizations working together to assemble marine data from diverse sources and resources and build gateways to national, regional or thematic repositories, essential contribution to the data and information sharing provisions in the MSFD and the Maritime Spatial Planning (MSP) Directive  (Martín Míguez et al. , 2019). For example, the EMODnet Chemistry portal for marine data is currently compiling at the European scale monitoring and research data on beach litter abundance, project data on benthic litter and microliter floating data (www.
emodnet-chemistry. eu/marinelitter) extensively used to set assessment values for the MSFD Descriptor 10 on 'Marine Litter'  (Molina Jack et al. , 2019). EMODnet helps ensure the integrity of in-situ data used by the Copernicus program. Data from both EMODnet and Copernicus are distributed according to INSPIRE protocols (https://inspire. ec. europa. eu/metadata-codelist/ProtocolValue/) and are thus interoperable. As far as concerns protection strategies, Marine Protected Areas (MPAs) set on coastal, shallow (24. 68%: 0-15 m depth) sites, organized in networks of connected MPAs  (Boero et al. , 2016)  are considered to be playing in the future a central role in the recovery of declining species, habitats and ecosystems  (Davidson and Dulvy, 2017). They could support inclusive economics, such as small-scale fisheries (SSF) and ecotourism  (Ban et al.
The European Union invested in policies and infrastructures to make knowledge of the ocean central to environmental and climate policies as well as the blue economy. Its Member States, together with neighbours, have created during the last 20 years an unrivalled marine data and forecasting infrastructure through the European Research Framework Programmes FP6, FP7 and Horizon 2020, the Marine Knowledge Strategy and Copernicus. Working together, the principles of free and open access, of FAIR (Findable, Accessible, Interoperable, and Reusable) data  (Wilkinson et al. , 2016) , and the "measure once, use many times" principle, largely promoted through, have demonstrated clear value. Adhering to the FAIR principles with free, timely, and unrestricted access to ocean observation data is beneficial for the originators, has obvious benefits for users, and is an essential foundation for the development of new services made possible with big data technologies  (Tanhua et al.
, 2019). SeaDataNet, the pan-European infrastructure for ocean and marine data management, is a unique example of the association of connected data centres collectively working to improve the quality and effectiveness in the management, the processing, and the dissemination of oceanographic data at national, regional and global levels, in line with FAIR principles. The EMODnet with its seven disciplinebased thematic portals expanded and extended the partnership to over a hundred and fifty organisations working together to assemble marine data from diverse sources and resources to make them more accessible and more interoperable. EMODnet adopted SeaDataNet tools and services and worked in synergy to adapt the existing data infrastructure to the new data types and technologies. The collaboration of EMODnet is providing a good example of how to build partnerships to fully exploit the marine data value chain and this could contribute to the decade objectives  (Martín Míguez et al.
, 2019). Collaborative working services (Virtual Research Environment) that provide open and free access to data and tools for research will contribute to enhance scientific and educational capabilities getting maximum value from data and, at the same time, to reduce the inequity between northern and southern countries, which is particularly noticeable in the Mediterranean region. The European Commission is providing data and information products and services to monitor our human activities, planet and environment. As far as the ocean is concerned, two Directorates-General of the European Commission, i. Nevertheless, there are still large omissions in the available data and monitoring gaps at the basin scale. The SeaDataNet database content shows more than 357,000 data sets (see Figure  6 ) in the Mediterranean Sea from different disciplines and parameters (bathymetry, physics and biogeochemistry), 79% of which are open access (Figure  6A ) while 21% are still restricted (Figure  6B ).
The Global Ocean Science Report (IOC, 2020) provides a baseline for the forthcoming Ocean Decade, assessing the status of the global ocean science economy in the broader context of valuation of ocean spaces (and its uses) and resources. Blue economy developments on the marine environment and spatial planning requires to have access to information that are rarely collected by the science community. To this end, data from many sea-based sources involving the private sector, which collects a relevant amount of monitoring data on both ante and post-operam phases, need to be made available as raw data and not only in summary reports, to complement sources, in particular land-based, adequately monitored by public authorities. These data need to be integrated in the present data sharing infrastructures.
, 2019). Finally, while relevant frameworks are in place to allow synergistic operations (e. g. , the Integrative Methodological Framework (IMF) produced by the Priority Actions Programme Regional Activity Centre (PAP/RAC) of UNEP/MAP, the Intergovernmental Hydrological Programme (IHP) of UNESCO, EC and Global Water Partnership-Mediterranean (GWP-Med)), appropriate governance is still needed, overcoming the lack of adequate and persistent political commitment, "silo" effects and fragmentation in administrations, overlapping mandates and difficulties in meaningfully transposing international commitments into national and local legislative frameworks and practices. A better interoperability and integration between existing data and databases (Programme for the Assessment and Control of Marine Pollution in the Mediterranean-MEDPOL, EMODnet, national databases, etc.
Better understanding of the interactions and interdependencies of the environmental conditions and processes, the use of resources, the economy function and sustainability at the ecosystem level (rather than at the species level) and of the inter-relationships between species. Data availability by exploiting the synergy of in situ and space observation and forecasting to predict Mediterranean coastal areas in support of multi-hazard early warning systems and of modelling the effects of the mutual interaction of various sources of hazards and environmental changes. Understanding of the seasonal and interannual variations of HABs events and the changes in their intensity and distribution at the local and regional scales. Adequate awareness and involvement of decision makers, communities, citizens, teachers, media, to influence behaviors and perceptions of different actors towards effective sea actions (sensu Ocean Literacy).
, 2015). The contribution of private companies to environmental data collection, rare in the past, is the key to support prediction and mitigate the climate change impacts on population and blue economy. For example, renewable energy installations are considered to play a major role in order to meet the commitments of Paris agreement on Climate Change. Maritime archeology and cultural heritage also represent an emerging opportunity that involves monitoring data to assess the level of conservation, a chance for fruition and study of the past. One main priority for the region is the necessity to move from hazard-to impact-based forecasting in order to better quantify and communicate economical and societal impacts of direct and triggered hazards. For a science-based risk reduction, encompassing uncertainty quantification and management along the entire warning chain is of uttermost importance as well as the improvement of the monitoring capabilities for uncertainty reduction (e.
To take advantage of these opportunities, available data infrastructures such as SeaDataNet and EMODnet need to be reinforced in the Mediterranean Sea, and the collaboration with CMEMS and Regional Activity Centre for Information and Communication (INFO/RAC) needs to be strengthened. An ecosystem of existing data systems should be built as a bottomup effort that, sustaining its network of interconnected data centers, strengthens its foundations. The roles of the data collectors and data providers are increasingly differentiating and specialising. The entire data processing chain (from the origin to the final aggregation on the data infrastructure) integrates and interoperates. In a transparent and accessible Mediterranean Sea open data quality need to be always ensured. To implement an overarching process to assure good quality of data (Quality Control/ Quality Assurance-QA/QC, provenance, transparency, full metadata description and allowing reassessing and reprocessing capabilities), it is fundamental that the validation process reports back to the data sources that are the main responsible for the "master" copy of the data and are responsible for updating and providing access to its best copy.
Uptake of data and prediction produced by the system, and delivered in agreement with the FAIR protocols, i. e. , findable, accessible, interoperable and reusable for anyone across the basin, should be maximized. Databases must prioritize the acquisition of records on productivity limits and availability of marine resources, but also including socio-economic data. Feedback from users should be considered in system continuous development. To prevent duplication, the e-ecosystem should aim at connecting data infrastructures, without copying data and creating multiple versions of the same data, disconnected from the original source. The SeaDataNet distributed network of professional data centres, providing on-line integrated access to databases of standardized quality, mark a substantially different approach from the centralised World Ocean Database and guarantee tight connection from the data originators to the final aggregation and delivery of the observations.
Additionally, SeaDataNet standards, the access and viewing services, the data formats, the metadata catalogues represent an international reference, reaching the widest adoption with the SeaDataNet Controlled Vocabulary, used in all the continents. To sustain a robust ecosystem of data infrastructures, national engagement such as commitments from nations to participate in and use the existing data infrastructures is highly expected and recommended. National funding should maintain and update the national contribution to SeaDataNet and EMODnet. A citation index for data sets (with persistent identifiers) to acknowledge scientists as well as institutes links directly to the repository so that users can quickly access the associated research data. Citations to data sets and studies are indexed so that their impact can be measured and their influence tracked.
The recent emergence of cloud processing platforms, such as the Google Earth Engine (GEE), which gathers multi-petabytes of geospatial datasets and satellite imagery, including all Landsat and Sentinel-2 archives, overcomes this constraint. To date, GEE is the largest free-to-use EO data library  [27]. It enables the fast processing of large amount of data directly online, using Google's computational infrastructure  [27]. Global scale assessment of ecosystems over decades were made possible with the GEE, for instance, for beaches  [28]  or water surface changes  [29]. In this paper, we aim to: (1) demonstrate the detectability of stranded green macroalgae on the foreshores using Landsat imagery; and (2) estimate green tide surfaces over  35 years (1984-2019)  at four affected beaches in Northern Brittany.
Imagery Selection In this study, we used Google Earth Engine (GEE) as the main satellite imagery processing tool for detecting macroalgae tides on the shores of the study sites. A script was created in GEE to filter Landsat imagery by date, location, cloud coverage and to import the geometries of the studied sites. The scripts are openly available. Multi-temporal analysis requires removing atmospheric effects. That is why surface reflectance (SR) Landsat products were preferred to Top-of-Atmosphere (TOA) scenes. We relied on the Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) datasets, which together enable an almost yearly continuous coverage of the time period ranging from 1984 to 2019. Landsat 5 and 8 data were obtained for the seven months of April, May, June, July, August, September, and October.
ysis requires removing atmospheric effects. That is why surface reflectance (SR) Landsat products were preferred to Top-of-Atmosphere (TOA) scenes. We relied on the Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) datasets, which together enable an almost yearly continuous coverage of the time period ranging from 1984 to 2019. Landsat 5 and 8 data were obtained for the seven months of April, May, June, July, August, September, and October. This choice was based on existing literature which highlights that the stranding of Ulva macroalgae can start by mid-April, and the accumulation on beaches begins to recede from September to October  [11, 12, [30] [31] [32]. It should be noted that, when on the foreshore, the macroalgae are not a living organism anymore  [12]. Thus, the stranding season might differ slightly from the growing season of the living green macroalgae, in the sense that a delay can occur between the growth peak of macroalgae and the days registering the largest green tides on the foreshore of beaches.
Additional Data CEVA's estimates of Ulva macroalgae coverage were used for comparison with the Landsat-based surface estimates. CEVA uses aerial photography to quantify the algae surface. The aerial surveys are conducted in low tide conditions over a seven-month period (from April to October) for 30 priority sites in Brittany. CEVA does not publicly detail its methodology for surface estimations, but reports suggest that the algae surfaces are manually digitized through visual inspection. Yearly averages were computed for comparison with the Landsat-based yearly estimates. Due to the relative coarse spatial resolution of Landsat imagery (30 × 30 m), we visually compared a selection of satellite scenes with high resolution aerial photography (0. 71 cm × 0. 71 cm). These aerial photography scenes were retrieved freely from the Institut National de Géographie (French National Institute of Geography).
Over the 35 years studied, only one exact match in dates was found, on 7 June 2005 at Saint-Michel-en-Grève, and is shown for visually checking the robustness of the algae mask. At other dates, a few days separated Landsat and aerial photography scenes. Time series data on nitrogen mean concentrations were also included in our study, given that nitrogen is the limiting factor for algae growth in Northern Brittany. Nitrogen mean concentrations were extracted for four water stations in Brittany. These water stations are located along the rivers draining the studied sites: the Roscoat (for Saint-Michel-en-Grève), the Douron (for Locquirec), Gouessant (for Morieux), and the Urne (for Yffiniac). The data was downloaded from the Environmental Observatory of Brittany (Observatoire de l'Environnement en Bretagne) website.
Over the 35 years studied, only one exact match in dates was found, on 7 June 2005 at Saint-Michel-en-Grève, and is shown for visually checking the robustness of the algae mask. At other dates, a few days separated Landsat and aerial photography scenes. Time series data on nitrogen mean concentrations were also included in our study, given that nitrogen is the limiting factor for algae growth in Northern Brittany. Nitrogen mean concentrations were extracted for four water stations in Brittany. These water stations are located along the rivers draining the studied sites: the Roscoat (for Saint-Michelen-Grève), the Douron (for Locquirec), Gouessant (for Morieux), and the Urne (for Yffiniac). The data was downloaded from the Environmental Observatory of Brittany (Observatoire de l'Environnement en Bretagne) website.
Comparison between Earth Observation and Aerial Photography Estimates Comparing the recent trends, from 2002 to 2019, of annual mean surfaces derived from Landsat with CEVA's data shows good agreement levels in terms of trends, order of magnitude of the areal estimates and general variability in the data, as shown by Figure  8. For Yffiniac and Morieux, significant and positive correlations were found between the two time series (respectively: Spearman's r = 0. 59, p = 0. 016; Spearman's r = 0. 69, p = 0. 0029). Such a clear relation was not found for the sites of Locquirec nor Saint-Michel-en-Grève. However, at Saint-Michel-en-Grève, the data shows good agreement levels, especially from 2009 onwards. At Locquirec, where discrepancies between the two datasets are more notable, green tide surface estimates remain within the same range, except for three years: 2003, 2005, and 2009.
Reconstructing Green Macroalgae Long-Term Time Series This study retrospectively estimated green macroalgae extents almost continuously over a 35-year period. Similar time-series analysis are not possible from in-situ measurements nor aerial photography due to the lack of long-term historical records. In Northern Brittany, the four studied sites have one of the most systematically and longest collected monitoring program, based on aerial photography of green tide coverage. Nonetheless, these time series are only available from 2002 onwards and the methodology for algae detection is not detailed. For these reasons, long-term EO mission, such as Landsat, are valuable tools even in well-monitored areas to show the patterns and dynamics of macroal-gae blooms over 35 years. Nevertheless, the collection of ground truth data remains a very valuable source to assess the accuracy of EO data and to refine the analysis of macroalgae on the foreshore by providing information on algae species and biomass density.
Moreover, the direct comparison of green tide surfaces between Landsat and CEVA datasets (for the 2002-2019 period) shows a good fit between satellite remotely obtained data and high-resolution aerial photography. The visual comparison between a highresolution imagery and Landsat scene also illustrates the robustness of estimating green tide abundance with Landsat imagery. Despite coarser spatial resolution, the algae mask based on NDVI and NDWI is able to detect areas with green macroalgae content on the beach.
Uncertainties and Next Steps Estimating green macroalgae surface through Earth Observation relies greatly on the availability of images in low tides conditions, and is hindered by cloud coverage. Given the low revisit time of both Landsat 5 and 8 satellites-16 days-and the frequent presence of clouds over the coasts of Northern Brittany, satellite data for some of the years considered in the study were not available. Thus, macroalgae surfaces could not be estimated. For many other years, only one or two scenes were available over an entire season. For these reasons, the annual mean surface estimates should be carefully interpreted. Sentinel-2A (since 2015) and Sentinel-2B (since 2017) have a combined revisit time of 2-5 days, allowing for a higher number of images to be made available over the areas of interest.
The higher spatial resolution-10 m for the blue, green, red, and infrared bands; and 20 m for other visible and near-infrared bands-could also play a role in improving surface estimates. If Sentinel-2 data were to be combined with Landsat 8, this would result in even higher number of available scenes. However, the cloud coverage remains a hinderance when working with optical remote sensing data. Since 2014, the Copernicus program has launched Sentinel-1, a mission acquiring radar imaging with a revisit time of 12 days. Radar microwave data has the capacity to acquire imagery day and night and does not heavily depend on weather conditions. The use of Sentinel-1 imagery in monitoring green algae stranded on the foreshores could also be a factor for improving green algae surface estimates  [46].