dataesr/scientific-paragraphs-categorization huggingface dataset:
The soil is supposed to be dry to slightly wet according to expertise based mainly on meteorological data (precipitation, temperature). Soil moisture values are assumed to range from 2 to 25 vol. %.  Case 2: A priori information is available for mv. The soil is supposed to be very wet according to the expertise of the meteorological data. The mv values are assumed to vary between 25 and 40 vol. %. An overlap of 10 vol. % on the mv is considered between the databases used for the training of the neural networks. For a priori information that soil is dry to slightly wet (case 1), the NN was developed using the training database elements with the mv between 2 and 30 vol. %. For a priori information that soil is very wet (case 2), the NN was developed using the training database elements with the mv between 20 and 40 vol.
COMPARISON BETWEEN S1-SSM AND PRECIPITATION Rainfall measurements with 30 min revisit time obtained from the GPM (Global Precipitation Measurements) sensor were considered in this study. From the GPM mission the IMERG (Integrated Multi-satellite Retrievals for GPM) data product was selected to perform our analysis. The spatial resolution of IMERG maps is 0. 1 °× 0. 1 °. IMERG data at different spatial and temporal resolutions are available via the NASA Precipitation Measurements Mission (PMM) website (https://pmm. nasa. gov/data-access). For each S1-SSM map date, one cumulative rainfall map for the 6 days prior to the S1-SSM map date was computed by summing the 30 min time interval of the Late IMERG GPM product. Then, each S1-derived SSM map was overlaid with the corresponding 6 days of cumulative GPM rainfall.
ACKNOWLEDGMENT This research was supported by  IRSTEA (National Research Institute of Science and Technology for Environment and Agriculture)  and the  French Space Study Center (CNES ,  DAR 2018 TOSCA ). The authors wish to thank the  ESA (European Commission  and the  European Space Agency)  for providing the S1 images. We used Copernicus level 2A S2 data processed by the  French  land data service center (Theia: https://www. theia-land. fr).
Two species of the clawed frog family, Xenopus laevis and X. tropicalis, are widely used as tools to investigate both normal and disease-state biochemistry, genetics, cell biology, and developmental biology. To support both frog specialist and non-specialist scientists needing access to these models for their research, a number of centralized resources exist around the world. These include centers that hold live and frozen stocks of transgenic, inbred and mutant animals and centers that hold molecular resources. This infrastructure is supported by a model organism database. Here, we describe much of this infrastructure and encourage the community to make the best use of it and to guide the resource centers in developing new lines and libraries.
INTEGRATION AND SUPPORT ON XENBASE Xenopus research around the world is supported by Xenbase 4  the Xenopus model organism database. Xenbase maintains, curates, and freely disseminates all of the diverse genomic, genetic, expression, and functional data for Xenopus and interrelates these data to human and other model organisms  (Karimi et al. , 2018). In addition, Xenbase allows researchers to identify Xenopus resources and reagents, including the transgenic and mutant lines (as detailed in this paper), as well as clones (ORFeome, plasmid, fosmid, and BAC) which are supplied by the EXRC. Xenbase links to all the major Xenopus stock center websites (EXRC, NXR, XLRRI, NBRP, and CRB) from the Xenbase home page and from resource pages where appropriate. A fully searchable "Lines and Strains" module includes all of the mutant and transgenic lines and wild-type strains that are, or have been, available from NXR, EXRC and NBRP.
Each Xenopus line has a dedicated page which summarizes targeted gene (s) or transgenic construct details, phenotype, genetic background and provenance, and provides direct links to the relevant stock centre (s) from which the line may be ordered. In the database, wild-type strains are also given "XB-LINE" identifiers so that they can be assigned as background to transgenic and mutant lines. Stable research identifiers (RRIDs) are recorded for all Xenopus stocks, a measure taken to promote reproducibility, rigor and transparency. Lines and strains shared between stock centers have been given unique RRIDs, as these colonies are now genetically isolated, and may be affected by genetic drift. Importantly, Xenbase staff developed the transgenic nomenclature guidelines in consultation with the Xenopus stock centers and following best practice used by all other model organism databases.
Several global and regional initiatives are underway to change this situation. This paper presents a selection of these initiatives as best practice examples for bathymetry data collection, compilation and open data sharing as well as the Nippon Foundation-GEBCO (The General Bathymetric Chart of the Oceans) Seabed 2030 Project that complements and leverages these initiatives and promotes international collaboration and partnership. Several non-traditional data collection opportunities are looked at that are currently gaining momentum as well as new and innovative technologies that can increase the efficiency of collecting bathymetric data. Finally, recommendations are given toward a possible way forward into the future of seafloor mapping and toward achieving the goal of a truly global ocean bathymetry.
Then a selection of repositories and syntheses is presented as best practice examples for bathymetry data compilation, archiving of source data, data discoverability and availability. All these initiatives require a strategy that can combine the efforts to accomplish the task of mapping the world's oceans. The Nippon Foundation-GEBCO Seabed 2030 Project aspires to facilitate this through global coordination and capacity building, and is briefly introduced in this context. The challenge of mapping the gaps will be discussed and the seafloor community network with its main linkages illustrated. Finally, an outlook is given toward the future of seafloor mapping, including key recommendations.
Current Developments and Future Plans In order to convert depth soundings into a bathymetric surface, several steps need to be taken. There is a trend toward the development of effective automation routines that include data acquisition, vessel-to-shore data transmission and data processing. The Shell Ocean Discovery XPRIZE challenge (2015-2019) -Discovering the Mysteries of the Deep Seadesigned to accelerate innovation for the rapid and unmanned exploration of the seafloor, is one example that addresses a need for new technologies in order to meet the goals of various ocean initiatives.
As part of this procedure, the TPU (Total Propagated Uncertainty) is calculated for each sounding, which combines information about positional accuracy, environmental conditions and system performance into one value. The TPU is used to weight the contributions of each sounding to the estimate of depth at a defined position (grid node). Apart from this, other automatic methods for reliably reducing the volume of the bathymetric data have been proposed (e. g. ,  Rezvani et al. , 2015). Generally speaking, the automated processing of multibeam datasets, while potentially offering ways to minimize processing and other resources associated with acquisition, may in some cases result in loss of information and propagation of errors. Furthermore, in order to ensure fit-for-purpose bathymetric data, a quality assurance (QA) process is needed.
WHY DO WE NEED BATHYMETRIC DATA? Knowledge of bathymetry is important for a wide variety of uses starting with the fundamental understanding of geological and oceanographic processes affecting our planet. Early echosounding profiles across the Atlantic Ocean for instance enabled Bruce Heezen and Marie Tharp to understand the relationship between mid-ocean ridges and earthquake seismicity and played an important part in the recognition of one of the most significant paradigm shifts in science -the development of the hypothesis of seafloor spreading and plate tectonics  (Hess, 1962). A seabed mapping user survey conducted in 2018 by Geoscience Australia and FrontierSI captured information from national and international stakeholders across all sectors. It revealed that habitat mapping and hydrographic charting were the most common applications for the use of high-resolution bathymetric data  (Amirebrahimi et al.
MH370 The recent loss of Malaysia Airlines flight MH370 has highlighted the lack of detailed bathymetry in large areas of the world's oceans. The existing data in the search area were based on a bathymetric model derived from marine gravity information estimated from satellite-altimetry combined with sonar soundings  (Smith and Sandwell, 1997). At the time of the search for the fuselage, single and multibeam data coverage in the area was insufficient to deploy deep-water instruments to provide a detailed inspection of the seafloor  (Picard et al. , 2017)  and so ship-based bathymetric data had to be collected. By comparing this newly acquired high-resolution data with the modeled data (Figure  2 ), it was found that 38% of the grid cells differed vertically from the high-resolution data by more than 100 m with maximum differences of 1900 m  (Picard et al.
Hazard Studies Marine geohazards are not only of concern to coastal communities, but also to industries dealing with marine infrastructure. Geohazard assessments in the marine realm are mainly based on bathymetric data. Although only a snapshot in time, bathymetric data deepens the understanding of the seafloor fabric and helps to identify potential risks linked to hazardous processes, such as slope failures or turbidity currents, and with repeat surveys, can be used to monitor seafloor changes over time  (Chiocci et al. , 2011). Clearly, bathymetric resolution appropriate to the target features is required. The morphology of the seafloor is also linked to the formation and propagation of tsunamis and is of vital importance in the context of tsunami forecasting. Generally, bathymetric data represent a fundamental dataset for addressing the growing challenges associated with climate change  (Stocker et al.
BATHYMETRIC DATA SOURCES One large source, by area, of high-quality bathymetric data is from research cruises, undertaken by a range of research and government institutions across the globe. Data are traditionally held by the host institutions and used for specific research purposes. Exceptions are individual agreements with, for example, funding agencies that oblige institutions to make their data publicly available after a certain amount of time. With more widely accepted data sharing policies, institutions are encouraged to archive their data on central open-access repositories and portals, where data can be easily discovered and freely accessed for wider purposes. This way data are collected once and can be used many times. Sharing these data for re-use ensures that new data acquisition efforts can focus on unmapped regions and maximizes return on public investment.
Transit Data -Making the Most of Every Nautical Mile Research vessels that operate internationally usually have long transit routes, whether transiting from port to study area or between different study areas. Often these routes lead them through international waters, where data recording is unrestricted. Several nations have procedures in place to collect such data and make them publicly available. In the United States, the Rolling Deck to Repository (R2R) Program was initiated in 2009 to ensure that all underway data acquired aboard the United States Academic Research Fleet is documented and archived in public repositories. Data from each cruise are routinely submitted by the vessel operator to R2R which ensures delivery to the appropriate national repository. This project has resulted in a significant increase of multibeam data made available at the International Hydrographic Organization Data Centre for Digital Bathymetry (IHO DCDB).
In 2011, the Multibeam Advisory Committee, was formed to help coordinate fleet-wide multibeam calibrations and system monitoring for the United States Academic Research Fleet and to develop and make publicly available tools and best practices for operational procedures that promote the acquisition of highquality multibeam data. These projects share the common goal of promoting high-quality publicly available data and encourage the acquisition of transit data. In 2015, three German research vessels (RV Maria S. Merian, RV Meteor, and RV Sonne) started to collect multibeam data on their transit routes, mapping approximately 200,000 km 2 every year. Recently, RV Polarstern, a fourth German research vessel and the Dutch RV Pelagia have announced an intention to join this approach. The transit data are sent to GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany, where the data are processed and several data products created.
The data are integrated into the international Pangaea Data Publisher for Earth and Environmental Science as well as into the IHO DCDB. Data collection is actively supported by the ship's crew and usually by the chief scientists. Of key importance for getting this support has been the commitment to make the data freely available for all. Another supporting example that transit matters is the search for flight MH370. Seabed mapping contractors were asked and agreed very early on to acquire data (at no cost) during transit between the search area and the port-of-call, and where possible, to build on the coverage. After over 3 years of operation, transit data accounted for ∼432,000 km 2 , the equivalent of 1. 5 time the search area (238,000 km 2 ). This data was of similar resolution to the search area, however, it is of lesser quality and density due to the nature of transit acquisition.
An IHO CSB Working Group, comprising international scientific, governmental and commercial hydrographic experts, was tasked by the IHO to draft a guidance document meant to empower as many mariners as possible to map the gaps in the bathymetric coverage of the world's ocean. The document, which will become an adopted IHO publication in 2019, describes what constitutes CSB, the installation and use of data loggers, preferred data formats, and instructions for submitting data to the IHO DCDB. The document also provides information about data uncertainty to help data collectors and data users better understand quality and accuracy issues with CSB. The working group is now focusing on developing an outreach plan covering the "why, what, where, and how" to encourage all vessels at sea to collect bathymetric data as part of a mariner's routine operations.
Under the guidance of the working group, NOAA's national centers for environmental information (NCEI) has implemented the ability to archive, discover, display and retrieve global crowdsourced bathymetric data contributed from mariners around the world. These data reside in the IHO DCDB which offers access to archives of oceanic, atmospheric, geophysical, and coastal data (Jencks et al. "Citizen-Science for the Future: Advisory Case Studies from Around the Globe, " this issue).
The availability of bathymetric data is regulated by each country's national legislation. If a country decides to make its data available, the question remains of how to make the data discoverable and accessible to the interested user. Several bathymetric syntheses, some of which are described below, are addressing this question. Another collaborative approach has been started by the EU Horizon 2020 research and innovation project AtlantOS Optimizing and Enhancing the Integrated Atlantic Ocean Observing System. The project covers various disciplines, including seafloor mapping, with the objective of enhancing ship-based observing networks. Within this approach, several European data centers are working together to trace deep-sea bathymetric data and integrate them to the IHO DCDB in order to make them accessible and usable for the specialist and non-specialist user.
By covering the cost of data acquisition, organizations often consider the data their intellectual property and accordingly, are not willing to easily share them with others. Additional barriers for releasing data may include but are not limited to security considerations and confidentiality of data. The perceived sensitivity of the data is sometimes so high that organizations are not even willing to publish the coverage or the metadata of the survey data. GEBCO is working to build relationships with survey companies and their customers to release the data they hold or own. The first agreement was made in early 2018 with Fugro, a large offshore company providing geotechnical and survey services, who acquire vast quantities of bathymetric data with a global fleet of ocean-going survey vessels.
As they move vessels from project to project, they also have the opportunity to collect data during transits as a form of CSB contribution. Since commencing the program, over 167,000 km 2 of multibeam bathymetric data have been contributed to the IHO DCDB. In addition, Fugro has begun to make their customers aware of this approach and has begun to explore if there are any terms under which they might consider donations of data. In many cases, as mentioned above, these datasets may contain marketsensitive information and when this is the case, it is determined if a reduction in resolution and/or a delay in release may mitigate any data sensitivity concerns. In the first instance, provision of simple metadata to allow identification of the area of data coverage and data characteristics is a step forward in identifying the areas of seafloor that have already been surveyed, even if the data cannot yet be released.
Following the success of the Fugro initiative, further collaborations are now being developed with other commercial partners. The best-practice being developed, promoting public access to the transit data through IHO DCDB combined with potentially limited release of commercially sensitive data, is now being encouraged across the marine survey industry. It is expected that this approach will not eliminate the need for marine site characterization services, but rather increase its demand. Only through a comprehensive mapping of the ocean will areas of interest become known. The expectation is that within those areas of interest, high-resolution mapping services will still be required to support marine projects and activities.
The IHO DCDB The International Hydrographic Organization Data Centre for Digital Bathymetry (IHO DCDB) was established in 1990 to steward the worldwide collection of bathymetric data. The intent was for the center to archive and share, freely and without restrictions, raw unedited single-multibeam bathymetric data acquired by hydrographic, oceanographic and other vessels. In the original proposal, the former NOAA national geophysical data center (NGDC), now the NCEI, agreed to "operate a worldwide digital data bank of oceanic bathymetry on behalf of the Member States of the IHO. " Almost 30 years later, NCEI and the DCDB remain committed to providing easy, open access to the wealth of data from a variety of sectors (e. g. , industry, government, academia, crowdsource efforts) for long-term archive, stewardship, and public use.
Enabling users to locate and access the data they need is critical in maximizing the re-use of data. NCEI accomplishes this with quality standard metadata registered in catalogs to support search and discovery, map services that can be used by anyone as building blocks in custom web applications, and by developing and hosting web map applications that provide an intuitive interface to display, select, and download many different types of data. The public can discover bathymetric data from the IHO DCDB Digital Bathymetry viewer (Figure  3 ). There is also the option to display a multibeam bathymetry mosaic of NCEI's bathymetry holdings with elevation values and color shaded relief visualizations as well as the single-beam sounding density. The data sets are freely accessible and the majority can be directly downloaded.
Along with displaying the DCDB's bathymetric data holdings, the viewer also shows the locations of data accessible from other repositories (e. g. , AusSeabed, Canadian Hydrographic Service, EMODnet Bathymetry, MAREANO) through the ingestion of their web services. Global seafloor mapping campaigns, such as The Galway Initiative and Seabed 2030, can use this viewer as a tool for identifying where data already exist to reduce costly, duplicative surveying efforts. In addition to encouraging countries, organizations, academia, industry, and individuals to contribute their data, the DCDB also strongly encourages other repositories to make their web services available so that their data holdings can be more broadly shared.
EMODnet Bathymetry An example for a regional bathymetric synthesis for Europe is the European marine observation and data network (EMODnet). This initiative aims at assembling and granting access to European marine data, data products and metadata from diverse sources originating from organizations in countries around European seas (Miguez et al. "EMODnet: Roles and Visions, " this issue). The EMODnet Bathymetry Project is an example of a regional approach that develops and provides a bathymetric DTM for the European seas. The DTM is made publicly available for downloading, whereas access to the source data might be restricted. User access to source data, generally at higher resolution than the DTM, might be granted by the data provider directly upon request, depending on the national and/or distribution policy of the hosting organization.
With this respect, licenses detailing simple acknowledgment of the source data (more than often through DOI identification) tend to generalize. The grid resolution of the model has increased since the early stages of the project from ∼500 m in 2010, ∼250 m in 2015 to ∼115 m in 2018. Each grid cell has a reference to the source data -bathymetric survey via Common Data Index, composite DTM via the Sextant catalog, and GEBCO in case of gaps -used for determining the water depth. The model is produced from aggregated surveys, collated by a network of contributors from marine research institutes, hydrographic services, government agencies and private companies. In 2018, over 27,000 survey data sets from 42 providers and 140 composite DTMs from 28 providers were included. The overall EMODnet DTM is generated from the compilation of the data sources available through a commonly adopted methodology  (Emodnet Bathymetry, 2009).
Data providers provide metadata and make sure their data are processed for erratic soundings and remaining bias. They sample and pre-grid their datasets with a common software tool into data files which are handed over to so-called basin coordinators. The task of the coordinators consists of selecting and then merging selected datasets for their basin, and building the most realistic and accurate regional basin DTM by ensuring a coherent and smooth transition between data sources. Finally, basin coordinators provide their regional DTM to an integrator for composing the full DTM (Figure  4 ).
A core principle in the design of GMRT is to make elevation data products accessible to specialist and non-specialist users alike while providing full attribution to data sources, and access to source data for advanced users. Access to GMRT is provided through a web application called GMRT MapTool, several web services, and the java-based GeoMapApp desktop application. All of these tools and applications allow access to gridded elevation data in the form of grids, points, and profiles, as well as images and metadata information. Data can be extracted and downloaded from GMRT at user-defined resolutions in a variety of formats. Terrestrial and bathymetric elevation components combined into GMRT are managed independently, which enables updating content on different schedules. New versions of GMRT are released twice each year and typically ∼2 million km 2 of new multibeam data coverage is added annually.