dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This paper is partially based on SDSS data. Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U. S. Department of Energy Office of Science , and the Participating Institutions. SDSS acknowledges support and resources from the  Center for High-Performance Computing at the University of Utah. The SDSS website is www. sdss. org. SDSS is managed by the  Astrophysical Research Consortium  for the Participating Institutions of the SDSS Collaboration including the  Brazilian Participation Group , the  Carnegie Institution for Science, Carnegie Mellon University , the  Chilean Participation Group , the  French Participation Group, Harvard-Smithsonian Center for Astrophysics ,  Instituto de Astrofísica de Canarias , The  Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo , the  Korean Participation Group ,  Lawrence Berkeley National Laboratory, Leibniz Institut fur Astrophysik Potsdam (AIP), Max-Planck-Institut fur Astronomie (MPIA Heidelberg), Max-Planck-Institut fur Astrophysik (MPA Garching), Max-Planck-Institut fur Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University ,  New York University, University of Notre Dame, Observatorio Nacional / MCTI , The  Ohio State University, Pennsylvania State University ,  Shanghai Astronomical Observatory, United Kingdom Participation Group ,  Universidad Nacional Autonoma de Mexico ,  University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University , and  Yale University.
Thus, the Demonstrator was designed with multiple entry points -also recognising that decision making is not a linear process. At the same time, one of the greatest strengths of the ECEM team was its capacity to produce consistent and high-quality data for all three timescales, for both energy and climate, and then to integrate all into a single interface to enable seamless and transparent decision making across a business. The Demonstrator can therefore also be viewed as a 'shop window', which may attract a user in search of a particular product -but who may then explore other products on offer. A key element in ECEM has been the compilation of a robust energy database, consisting of energy demand as well as generation from wind, solar and hydro power, together with information on installed generation capacity, for all EU countries.
Such a database has recently been made available by the European Network of Transmission System Operators for Electricity (ENTSO-E) under an EU regulation, but these data present some issues and inconsistencies, which made their use difficult in ECEM. The point here is not to criticize the ENTSO-E data, rather to emphasize that significant progress needs to be made on energy data completeness and homogenization in order to develop operational, fit for purpose climate services for the energy sector. In practice, it was not so easy to treat seasonal forecasting information, which requires displaying more frequently updated data, in the same way as the historical or projection information. Issues relating to the generally limited and specific skill over Europe, probabilistic formats and user expectations and use for operational decision making also emerged.
Flexible methodologies for data and Demonstrator development were however devised which would make it relatively easy to move to higher spatial (and temporal) resolution in the future, should the necessary energy data be available. Some sub-country information, for example, was eventually produced by ECEM -see Section 3. The lack of a European-wide, homogeneous and sufficiently long energy dataset was indeed a major challenge in ECEM. These issues relating to scope and focus were important in terms of managing user expectations throughout the project and in the identification of appropriate target users. Considering all the above issues, one of the early tasks of ECEM was to use the strong existing networks and expertise of team members accrued over many years to identify the target categories of external stakeholders (i.
The six two-page Key Messages are intended to provide critical information about historical and projected changes in energy and climate variables as well as examples of the types of analyses and interpretations that can be drawn from the ECEM Demonstrator data and plots. They follow a standard template with three or four 'Key message' bullet points on the first page, a final question on 'What does this mean for the energy sector?' and a single core question where the data are presented and analysed. The six Key Messages are: • How do we know Europe is warming? • How do we know the future will be warmer? • How do we know energy demand will change? • Is Europe likely to become drier or wetter? • Can changes in radiation be identified? • What datasets are available? The three user-motivated Event Case Studies illustrate the type of more detailed analyses that can be undertaken using the ECEM Demonstrator and the underpinning data, focusing on extreme weather events that have a major impact on the European energy sector.
Tutorials Text-based map and graph tutorials available at start-up and from Help menu. Link to webinar videos and slides. On the other hand, the number of choices available to users may be daunting. Thus good guidance, such as the 'Getting started' part of the Demonstrator help menu, is essential. The ability to download data in text or NetCDF format extends the power of the Demonstrator beyond a 'simple' visualisation tool. It gives the user flexibility to integrate the ECEM data sets with their own datasets and statistical/modelling tools. This is something else which emerged strongly from the ECEM workshops, webinars and Advisory Board discussions. Generation data, for example, is provided as capacity factors as well as in terms of power or energy, also depending on a prescribed future energy mix taken from the five eHighway2050 scenarios (http://www.
e-highway2050. eu/). A user can, however, download the capacity factors and apply them to their own or any alternative energy mix scenarios. From the early stages of engagement with potential users of the ECEM Demonstrator it was evident that access to high-quality climate and energy data was a priority. Thus it was important to assess the quality of the ECEM outputs, focusing on the Demonstrator and the  credibility, legitimacy and salience of its embedded data. These issues are all important to build trust in the data so that decisions can be made in the business context. With respect to salience, the majority of user requirements in terms of climate and energy variables were largely met (see Table  3 ), with only a few exceptions (see Section 4). The main aspect that users would like to see improved relates to higher temporal and spatial resolution.
As well as evaluation and checking by the individual data providers, all ECEM team members were able to review and assess datasets in a 'test' version of the Demonstrator before being made public. A number of issues were thus identified, resolved and updated data sets provided. Where specific issues could not be resolved they were documented in the 'Known issues' section of the Demonstrator help menu. A question on data quality was asked as part of the final short user survey (see Table  2 ). This question was deliberately left somewhat ambiguous and it was evident that some respondents interpreted quality as referring to the spatial and temporal resolution and the types of variables provided (salience) -whereas others highlighted the reliability and provenance of the data (credibility and legitimacy).
A few respondents wanted data at the city or catchment scale, for examplesee the end of Section 4 for a brief discussion on 'unmet' needs. Nonetheless it is encouraging that two-thirds of the 75 respondents 'agreed' or 'strongly agreed' that the quality was appropriate for their needs. Rather than this ad hoc and post hoc approach to assessing data quality, ECEM could, for example, have developed a specific data quality matrix of energy industry requirements -which might be different from those of the scientific community.
, 2015). It is therefore concluded that ECEM moved a long way towards co-production within a relatively short period even though it takes time for people to appreciate and uptake climate information, and to grow a strong stakeholder base. Another objective of this post-hoc assessment is to evaluate the 'quality of relationship' between the ECEM developers and users  (Gardiner et al. , 2018) , and to consider whether the process of stakeholder engagement led to higher quality outputs and outcomes from ECEM than if a purely consultative approach  (Meadow et al. , 2015)  had been followed. The issue of data quality (i. e. quality of outputs/products) is discussed in Section 3. Here, the issue of quality of outcomes  (Schuck-Zöller et al. , 2017)  is considered, including evaluation of the Demonstrator functionality, usability and usefulness.
The 76 respondents to the short user survey (Table  2 ) undertaken during the preoperational evaluation period (Fig. 2 ) were generally positive in this respect, i. e. they 'agreed' or 'strongly agreed' with the survey statements. Over 80% of respondents were positive that the Demonstrator is 'easy to use' and 73% were positive that the documentation and guidance provided 'will help you to make best use of the C3S ECEM Demonstrator'. It was particularly encouraging that 70% of respondents had already 'identified some specific features of the C3S ECEM Demonstrator and datasets which will facilitate my work'. Feedback from this survey and the other feedback and evaluation mechanisms used in ECEM (Table  2 ) support the conclusion that ECEM has successfully narrowed the usability gap  (Lemos et al.
Box 1: Working with commercial providers -the example of Transvalor Transvalor is a commercial energy service provider (Table  1b ), based in the south of France. Transvalor and ARMINES (an ECEM partner) had had some previous contact and interaction due to a shared interest in solar radiation and their close geographical location. A representative from Transvalor attended the fourth stakeholder workshop and the ECEM-athon in 2017 (Table  2 ). For the ECEM team, it was very informative to have time for detailed discussion with a new user, particularly with respect to climate/ energy projections, including terminology and the complexity of information and uncertainty. It was agreed that it was more appropriate for Transvalor's customers to access ECEM outputs including the Demonstrator and data sets via Transvalor, after appropriate processing and repackaging, rather than directly from ECEM.
A strategy by which Transvalor could gradually introduce customers to the concepts of climate projections was devised, based around the existing SoDa website (http://www. soda-pro. com/). Transvalor are now receiving more inquiries from customers concerning future conditions. Transvalor also reviewed and substantially improved the Key Message on solar radiation changes (see Section 3). During the ECEM-athon discussions, Transvalor identified a requirement for additional processing of the ECEM radiation data sets for input to in-house modelling tools, and this work was subsequently undertaken by a MINES ParisTech PhD student. This example (others include that of Naturgy and wind/hydro generation) illustrates how commercial products and services can be built upon a 'public good' free service such as C3S (see Section 5).
The goal of ECEM -consistent with the objectives of the C3S SIS (see Section 1) -was to develop a pre-operational tool (the Demonstrator) and to implement a proof-of-concept service, i. e. , one bringing together climate and energy data in a useful and usable way for the energy sector  (Troccoli et al. , 2018). This goal was successfully achieved (Fig. 1 ). Inevitably the C3S ECEM Demonstrator lacks some of the characteristics of an operational service. In particular, real-time seasonal forecasts are not yet embodied -only information about historical performance of the forecasting systems. Although the team continues to be responsive to feedback (Table  2 ) there is not an active 'help desk', typical of an operational system, responding to inquiries within service level agreement time frames.
These challenges included bias adjustment and validation of historical ERA-interim solar radiation and 10 m/ 100 m wind data  (Jones et al. , 2017)  and calibration/validation of generation models particularly for wind power  (Dubus et al. , 2019) , solar power  (Saint-Drenan et al. , 2018)  and hydro power  (De Felice et al. , 2018). Sourcing appropriate data for the energy models was also challenging  (Dubus et al. , 2019). Availability of energy data was an issue for calculating generation and demand, together with issues about how to incorporate non-climatic influences such as day of the week and technological/social trends  (Dubus et al. , 2019). As a proof-of-concept climate service, one of the challenges of ECEM was to link meteorology/climate expertise to energy expertise. At times, this introduced some tension between the kinds of data that are most useful to (a) the end user and (b) the researcher.
The choice was made, for example, to account for evolving trends and detailed factors such as weekends and public holidays when modelling demand data. This makes the data more directly relevant and familiar to end users, but harder to work with from a research perspective even when the research itself seeks to inform end users. Whereas the meteorological community has adopted a data sharing and common format approach since its infancy, the energy sector situation regarding data is very different and less coordinated. Little regulation and few standards exist, which make it extremely difficult to gather a harmonized dataset at the European level. EU regulation 543/ 2013 imposed a new paradigm, but is very recent, and much work is still needed to reach the necessary level, even though considerable progress has already been made.
Good quality and accessible data are essential to both build and validate the energy models. The major challenges in energy data are: • The lack of standards (data formats, accuracy, quality, availability of metadata) • The relatively short duration of available time series (for instance, hourly energy generation from wind, solar and hydropower for all EU countries are only available since 2015) • Inconsistencies in datasets (for instance, the net generation capacity reported on the ENTSO-E Transparency Portal (https:// transparency. entsoe. eu/) is not coherent with the generation data for some countries for solar and hydropower) The compilation and preparation of the energy data used in ECEM required a very significant amount of time and effort. In order to improve this important aspect of energy climate services development, it is essential that the above challenges are addressed in a long-term approach.
This certainly requires a concerted effort, involving EU and national regulation authorities, network operators, energy companies and climate services developers. On top of ENTSO-E's Transparency Portal after EU Regulation 543/2013, several initiatives have developed to progress on data issues. These include for instance the Open Power System Data Platform (https://open-power-system-data. org) and the World Energy and Meteorology Council Special Interest Group on Data Exchange, Access and Standards; but a higher level commitment is needed, as research groups do not have the legitimacy to impose rules on the whole sector. Addressing these challenges has substantially increased the technical capacity of the scientific team. At the same time, ECEM has helped to build the capacity of external stakeholders through ongoing exposure to relevant terminology and data, and participation in ECEM workshops and webinars.
1 ). Members of this community of practice are now recombining in a number of different groupings to work on new initiatives, building on the ECEM and CLIM4ENERGY expertise. These include Horizon2020 research and innovation projects such as SECLI-FIRM (http://www. secli-firm. eu/) and S2S4E (https://s2s4e. eu/) which are assessing the value of seasonal forecasts for the energy sector, MEDSCOPE (https:// www. medscope-project. eu/) which is focused on the Mediterranean, and Plan4Res (https://www. plan4res. eu/) which is using the ECEM historical and projections data to assess the impacts of climate change on EU energy systems. Having developed the proof-of-concept, selected partners from ECEM and CLIM4ENERGY have since come together in C3S Energy to work towards delivery of an operational service (https:// climate.
copernicus. eu/operational-service-energy-sector), which will be fully and transparently integrated with the Copernicus Climate Data Store (https://cds. climate. copernicus. eu). Reflecting this next step towards operationalisation, C3S Energy is co-ordinated through the World Energy and Meteorology Council (see Section 2) rather than an academic institution. It is expected that C3S Energy will raise the TRL to 7 (system prototype demonstration in an operational environment) or 8 (system complete and qualified) (Fig. 1 ). The C3S-funded operational service will provide a free and public service, i. e. it will be for the 'public good'. It will still leave considerable scope for service companies from both the private (e. g. consultancies) and public (e. g. National Meteorological and Hydrological Services) sectors to use C3S Energy outputs for the development of commercial products and services.
Data Access All CMEMS products (NetCDF format) are freely accessible through a single internet interface (http://marine. copernicus. eu/ getting-started/). The interactive catalog (http://marine. copernicus. eu/servicesportfolio/access-to-products/) allows users to select products according to geographical area, parameter, time span, and vertical coverage. Users can also select a product by using a key-word search. Once a product has been selected, the user can view it without registration. Once a product has been selected, CMEMS offers three different authenticated download mechanisms: • Subsetter (HTTP protocol, subset the files) to extract and download only a part of a product (per area, per variable, for a period of time, some depths). • Direct Get File (HTTP protocol) to download large dataset (according to a period of time).
The DFS represent the equivalent number of independent observations that constrain the model analysis at the observation point. Comparing the DFS with the number of observations indicates the information content. Computation of DFS is simple in theory, but the practical implementation depends on the data assimilation scheme. DFS monitoring is progressively being implemented in the CMEMS MFCs and TACs. Even though the DFS values inherently have no physical meaning, providing them for each assimilation cycle has various practical uses: • It serves as an internal diagnostic to verify that the relative impact of each assimilated data type or subset is balanced: no individual data stream is out-competing the other data sources. • It provides a no-cost indication of how changes in upstream data (typically their frequency, location or accuracy) affect the relative balance between assimilated datasets.
g. , western boundary currents), and (ii) provide robust estimates of deep ocean climatic signals  (Gasparin, personal communication) , which are of critical interest for CMEMS reanalyses. In addition to these experiments for physical parameters, numerical experiments have shown that assimilating BGC-Argo data complements surface ocean color data by improving model estimates of oxygen, nutrients, carbon and chlorophyll throughout the water column  (Wood et al. , 2018). The impact of assimilating BGC Argo data jointly with satellite chlorophyll observations in the Mediterranean Sea has been analyzed as part of the CMEMS Service Evolution MASSIMILI project  (Cossarini et al. , 2019). Results show that, when a dataset is assimilated, the model performance computed on the same dataset improves on the order of 50-70%.
Surface Carbon Observations OSSEs have also been conducted in the framework of the AtlantOS project for surface ocean carbon products (pCO2) using CMEMS multi-observation platform for ocean carbon and a statistical model  (Denvil-Sommer et al. , 2018). The aim of the work was to identify an optimal observational network for pCO2 for the Atlantic Ocean using simulated observations and output from the NEMO-PISCES model. Tests highlighted the need for data in the South Atlantic Ocean, the Surface Ocean CO2 Atlas (SOCAT) having sparse coverage in the Southern Hemisphere. The main results  (Sommer et al. , 2018)  show that the combination of (1) the SOCAT data base (integrating data from multiple platforms) with (2) a network of BGC Argo floats at around one quarter of current physical Argo resolution, and (3) existing moorings provides an optimal solution, which probably could be implemented with the least cost.
Waves Satellite wave observations are used to improve the wave products provided by CMEMS MFCs, ultimately yielding better surface fluxes needed for coupling with ocean circulation models. Currently, significant wave heights (SWH) from five altimeters (Jason-2 and 3, Saral/Altika, Cryosat-2 and Sentinel-3A) are routinely assimilated every 3 h in the global CMEMS MFC wave system, leading to very accurate integrated wave parameters. For example, the normalized scatter index (given by the ratio between the standard deviation of the difference between modeled and observed parameter and the mean of the observed parameter) of SWH is below 9% in high and intermediate latitudes and is even > 8% for tropical regions  (Aouf et al. , 2018a). The assimilation of satellite wave data in wave models is the most efficient way to correct the uncertainties related to the wind forcing, in particular for storm cases.
The case of Campbell Island, south of New Zealand is highlighted. Significant wave heights of more than 12 m were observed there by altimeters from 7 to 10 May 2018 (Figure  7A ). At 12:00 (UTC) during the storm on 8 May 2018, the Metocean Solutions buoy at Campbell Island recorded the peak of SWH of 14 m, which clearly agrees with the analysis provided by the global CMEMS-MFC (Figure  7B ). At the regional level, the impact of assimilating satellite observations on the Med-Wave system has been evaluated considering the Med-Waves-V4 system, assimilating along-track significant wave height observations from Jason-2 and Saral satellites at 3-hourly intervals, vs. the Med-Waves-V3. 2 system without data assimilation  (Ravdas et al. , 2018). Data assimilation improves results along satellite tracks, as well as at the great majority of the wave buoy locations.
Satellite wave data concern not only altimeter significant wave heights, but also directional wave spectra provided by Synthetic Aperture Radar (SAR). Because of the SAR's inability to image short and steep waves (with a wavelength less than about 200 m) in the azimuth direction due to their incoherent nature, SAR wave measurements are often referred to as swell measurements (hereinafter referred to as "swell spectra"). SAR swell spectra are currently provided by two Copernicus satellites, Sentinel-1A and 1B. The combined assimilation of SAR swell spectra and altimeters wave heights corrects both the wind-sea and the swell, which is independent of the wind and can propagate freely over long distances for many days. The most striking example of the impact of assimilating SAR directional swell spectra is the correction of swells generated by storms in high latitudes of the southern Pacific Ocean, which propagate to French Polynesia, the United States' west coast and western Central and South America.