dataesr/scientific-paragraphs-categorization huggingface dataset:
It also recalls the split in the SDSS colour-colour diagram  (Harris et al. 2003). While Gaia identifies white dwarfs based on their location on the HRD, SDSS white dwarfs were identified spectroscopically, providing further information on the spectral type, T eff , and log g as well as a classification. Therefore we cross-matched the two data sets to better understand the features observed in Fig. 14. We obtained a catalogue of spectroscopically identified SDSS white dwarfs from the Montreal White Dwarf Database 4   (Dufour et al. 2017 ) by downloading the whole catalogue and then filtering for SDSS identifier, which yielded 28,797 objects. Using the SDSS cross-match provided in the Gaia archive (Marrese et al. 2018), we found that there are 22,802 objects in common and 5,237 satisfying all the filters described in Sect.
Their study complements the field population studies that are based on Galactic surveys. Globular clusters are fundamental tools for studying the properties of low-mass stars and the early chemical evolution of the Galaxy. Now Gaia DR2 data bring us into a completely new domain. High-accuracy parallaxes and exquisite photometry make the comparison with theoretical isochrones very fruitful, based on which, stellar properties can be defined. A detailed discussion of the uncertainties of stellar models is beyond the scope of this paper. Here we would like to recall that effects such as convection in the stellar core, mass loss, rotation, and magnetic fields are still poorly constrained and are often only parametrised in stellar models  (Bell 2016; Pasetto et al. 2016; Weiss & Heners 2013). Although very significant, seismic predictions depend on our poor knowledge of the relevant physics  (Miglio et al.
Gaia identifies 1526 members. It was observed by almost all the most relevant spectroscopic surveys (Gaia-ESO, APOGEE, WIYN, etc. ). Astero-seismologic data are available from the Kepler 2 mission  (Stello et al. 2016). M67 is a cornerstone of stellar astrophysics, and it is a calibrator of age determination via gyrochronology  (Barnes et al. 2016). Its turn-off mass is very close to the critical mass for the onset of core convection. For this reason, the cluster is especially interesting for this specific regime of stellar models and their dependence on different parameters such as nuclear reaction rate and solar abundances. The main-sequence termination presents a distinctive hook and a gap just above it. These features are used to distinguish between diffusive and non-diffusive evolutionary models.
Acknowledgements. We thank  Pierre Bergeron  for providing the WD tracks in the Gaia DR2 passbands. This work presents results from the  European Space Agency (ESA) space mission Gaia. Gaia data are being processed by the  Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC is provided by national institutions, in particular the institutions partic-ipating in the  Gaia MultiLateral Agreement (MLA). The Gaia mission website is https://www. cosmos. esa. int/gaia. The Gaia archive website is https://archives. esac. esa. int/gaia. The Gaia mission and data processing have financially been supported by, in alphabetical order by country: the  Algerian Centre de Recherche en Astronomie, Astrophysique et Géophysique of Bouzareah Observatory ; the  Austrian Fonds zur Förderung der wissenschaftlichen Forschung (FWF) Hertha Firnberg Programme  through grants  T359 ,  P20046 , and  P23737 ; the  BELgian federal Science Policy Office (BEL-SPO)  through various  PROgramme de Développement d'Expériences scientifiques (PRODEX)  grants and the  Polish Academy of Sciences -Fonds Wetenschappelijk Onderzoek  through grant  VS.
cosmos. esa. int/web/gaia/ elsa-rtn-programmeELSA) Marie Curie Research Training Network  ( MRTN-CT-2006-033481 ), through  Marie Curie  project  PIOF-GA-2009-255267  (Space AsteroSeismology & RR Lyrae stars, SAS-RRL), and through a  Marie Curie Transfer-of-Knowledge (ToK) fellowship  ( MTKD-CT-2004-014188 ); the  European Commission's Seventh Framework Programme  through grant  FP7-606740  ( FP7-SPACE-2013-1 ) for the  Gaia European Network for Improved data User Services (GENIUS)  and through grant  264895  for the Gaia Research for European Astronomy Training (https://www. cosmos. esa. int/web/gaia/great-programmeGREAT-ITN) network; the  European Research Council (ERC)  through grants  320360  and  647208  and through the  European Union 's  Horizon 2020 research and innovation programme  through grants  670519  (Mixing and  Angular Momentum  tranSport of massIvE stars -MAMSIE) and 687378 (Small Bodies: Near and Far); the  European Science Foundation (ESF) , in the framework of the  Gaia Research for European Astronomy Training Research Network Programme  (https://www.
cosmos. esa. int/web/gaia/great-programmeGREAT-ESF); the  European Space Agency (ESA)  in the framework of the  Gaia  project, through the  Plan for European Cooperating States (PECS)  programme through grants for Slovenia, through contracts  C98090  and  4000106398/12/NL/KML  for Hungary, and through contract  4000115263/15/NL/IB  for Germany; the  European Union (EU)  through a  European Regional Development Fund (ERDF) for Galicia, Spain ; the  Academy  of Finland and the  Magnus Ehrnrooth Foundation ; the  French Centre National de la Recherche Scientifique (CNRS)  through action ' Défi MASTODONS' , the  Centre National d'Etudes Spatiales (CNES) , the  L'Agence Nationale de la Recherche (ANR) 'Investissements d'avenir' Initiatives D'EXcellence (IDEX)  programme  Paris Sciences et Lettres (PSL * )  through grant  ANR-10-IDEX-0001-02 , the  ANR  ' Défi de tous les savoirs ' ( DS10 ) programme through grant  ANR-15-CE31-0007  for project 'Modelling the  Milky Way in the Gaia era' (MOD4Gaia) , the  Région Aquitaine , the  Université de Bordeaux , and the  Utinam Institute of the Université de Franche-Comté , supported by the  Région de Franche-Comté  and the  Institut des Sciences de l'Univers (INSU) ; the  German Aerospace Agency (Deutsches Zentrum für Luft-und Raumfahrt e.
Following the analysis of  Lamarque et al. (2013)  we have taken the average sulfate deposition fluxes from 1850 to 1860 (a non-volcanic period) in several ice cores from Antarctica and Greenland and compared the ice core fluxes to the mod-elled polar sulfate deposition fluxes in the control simulations (Fig. 2 ). Ice core meta-data are included in the Supplement (Table  S2 ). Overall, CESM1(WACCM), MAECHAM5-HAM and UM-UKCA simulate similar background polar sulfate deposition patterns and magnitudes and compare well to preindustrial ice core sulfate fluxes. Scatter plots of the ice core fluxes vs. those simulated by each model are shown in Fig. 3. SOCOL-AER simulates slightly higher deposition with reduced regional variability compared to the other models (Fig. 2 ). However, compared to the ice cores, all models capture the lower sulfate deposition in the interior of Antarctica and higher sulfate deposition toward the coast.
Acknowledgements. Lauren Marshall  is supported by the  Natural Environment Research Council (NERC) ,  UK , through the  Leeds-York NERC Doctoral Training Partnership. Anja Schmidt  was funded by an  Academic   Research Fellowship  from the  University of Leeds. Alan Robock  is supported by  US National Science Foundation  grant  AGS-1430051. Claudia Timmreck  received funding from the  German Federal Ministry of Education and Research (BMBF) ,  research program  " MiKliP " (FKZ:  01LP1517B ). Ulrike Niemeier ,  Claudia Timmreck  and  Slimane Bekki  acknowledge support from the  European Union   FP7 project  " STRATOCLIM " ( FP7-ENV. 2013. 6. 1-2 ;project  603557 ). MAECHAM5-HAM simulations were performed at the  German climate Computer Centre (DKRZ). Fiona Tummon  was funded by  Swiss National Science Foundation  grant  20F121_138017.
This study benefited from the support of the Labex L-IPSL, which is funded by the  ANR  (grant no. ANR-10-LABX-0018 ). Matthew Toohey  acknowledges support by the  Deutsche Forschungsgemeinschaft (DFG)  in the framework of the priority programme " Antarctic Research with comparative investigations in Arctic ice areas " through grant  TO 967/1-1. Eugene Rozanov  and  Timofei Sukhodolov  acknowledge support from the  Swiss National Science Foundation  under grant  200021_169241  (VEC). William T. Ball  was funded by the  SNSF  project  163206  (SIMA). James Pope was funded by  NERC  grant  NEK/K012150/1. The  National Center for Atmospheric Research  is funded by the  National Science Foundation. We thank  Chaochao Gao  and an anonymous reviewer for their comments and insight on the manuscript, which greatly helped to improve the paper.
Acknowledgements We gratefully acknowledge discussions with  David Luitz  and  Gabriel Lemarié , and useful suggestions by  François Huveneers ,  Cécile Monthus ,  Antonello Scardicchio  and  Romain Vasseur. This work was supported by the  French National Research Agency (ANR)  under projects THERMOLOC  ANR-16-CE30-0023-02 ,  BOLODISS ANR-14-CE32-0018 , and by  Programme Investissements d'Avenir under the program   ANR-11-IDEX-0002-02 , reference  ANR-10-LABX-0037-NEXT.
It is a major and growing trend in museums and education. Numerous recent digital heritage projects have demonstrated the usefulness of the information and communications technologies  (Boile et al. , 2014; Antlej, 2014). The new technologies enable the creation of virtual databases using virtual globes -e. g. , Google Earth -and other personal-use geomatics applications (smartphones, tablets, PDAs) for accessing geological heritage information in "real time" for scientific, educational, and cultural purposes via a virtual geological itinerary. With these technologies, Geoparks and other relevant institutions can create mapped and georeferenced geosites  (Martínez-Graña et al. , 2013). The development of the Geoparks goes hand in hand with the vision of Interpret Europe (European Association for Heritage Interpretation).
Each of the participating Geoparks addressed one geological presentation challenge (problem) that is common, well investigated and presented in the partner territory. At the same time, all selected challenges are typical for the Danube region Geoparks. 4. Data collection in the frame of the joint geo-presentation training -While one of the main ideas of the Danube GeoTour project is learning interaction between project partners, the joint geo-interpretation training was organised for key Danube Geopark personnel responsible for geoguide service and/or presentation (fig. 2 ). The training covered the following 3 topics: familiarizing with newest developments, methods and best practiceslearning from others; exchange of different personal experiences and practices in the presentation of the selected top 8 interpretation challenges -this was implemented in the form of a workshop with a goal to learn from each other; modes of communicating complex geological facts in easy-to-understand language.
This will significantly open perspectives as well as strengthen the competences of individual parks management and the Danube Geopark Tour partnership as a whole and in comparison to other more advanced Geoparks within the EGN and GGN Network. In the frame of the joint geo-interpretation training, all participants (Geopark Idrija, Geopark Papuk, Geopark Hateg, Geopark Styrian Eisenwurzen, Balaton-Bakony Geopark, Bohemian Paradise Geopark, Geopark Karavanke) shared  The aim of the joint geo-interpretation training was that all participating Geoparks became familiarized with the modern methods and new trends in geo-presentation and with the best practices from all over the world. This transnational dimension of the training was very useful; each Geopark found out new approaches for geo-presentation through learning from other participants and also got new ideas for heritage presentation -some of the given approaches/ideas are presented below: • Objects that can be weighed, touched, kept in hand are memorable elements of the presentation; • gestures, tone of voice and expressive words, its magnitude and the events that accompanied it can make the explanation much more interesting, more fun and more memorable for the visitors; • use not too much information and facts on the panels; a mix of maps, graphics and pictures with a good short information text is the best way; • avoiding the overloading visitors with many scientific data in short period of time; • it is important to connect/compare information with some familiar facts/events in the real life; • active participation of visitors can not only enhance their experience but also helps them to remember important facts more easily; • involve children and you will automatically involve adults; • combination of personal (guided visits) and non-personal (use of illustrations, publications, audio-visual equipment, multi-media, etc.
RTO algorithm An enhanced RTO algorithm is being standardized by the IETF CoRE WG for CoAP, as part of the CoAP Congestion Control/Advanced (CoCoA) specification  [18]. CoCoA has been designed for IoT scenarios. It is based on the TCP RTO algorithm  [17] ; however, it also uses weak RTTs, an aging mechanism, a variable backoff factor, and dithering. CoCoA outperforms state-of-art TCP RTO variants such as Linux RTO or Peak Hopper, at the expense of a complexity increase that does not pose a problem for IoT devices  [18]. Therefore, we recommend using CoCoA for TCP.
Acknowledgments Carles Gomez has been funded in part by the  Spanish Government  and by the  ERDF  through the  Jose Castillejo grant   CAS15/00336 , and through project  TEC2016-79988-P. His contribution to this work has been carried out in part during his stay as a visiting scholar at the  Computer Laboratory of the University of Cambridge. Andrés Arcia-Moret  has been funded by the project  Networks as a Service  ( EP/K031724/2 ).
e. the minimally conscious state) and those who are entirely unconscious [i. e. the vegetative state, also known as unresponsive wakefulness syndrome (UWS)]. These markers may be task-based changes in the EEG that occur in response to stimuli, such as sounds or verbal instructions, or task-free features of the patient's EEG at rest. Engemann et al. (2018) apply advances in machine learning to the largest ever dataset of PDOC brain data and demonstrate that a combination of theoretically-and empirically-motivated EEG markers can accurately diagnose patients' levels of consciousness. To test the efficacy of a machine learning method, researchers first require a set of training data for which they and their algorithm know the 'truth'-i. e. which data correspond to patients who are conscious by standardized assessment, and which belong to those who are not conscious.
In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value. K e y w o r d s: ICOS, GHG exchange, carbon cycle, standardised monitoring, observational network.
Long-term atmospheric, biogeochemical and ecological datasets are crucial to better understand the spatio-temporal scales of environmental variability, to attribute changes to a particular forcing process and to identify temporally shifted or gradually changing ecological responses  (Bonan et al. , 2012; Baldocchi et al. , 2012). Long-term observations reveal susceptibilities and critical shifts in ecosystem functioning and services, and identify ecosystem responses to short-term anomalies and extreme events  (Reichstein et al. , 2007; Chu et al. , 2017). With strong environmental changes expected in the next few decades, such long-term datasets should cover a relevant timeframe (≥ 20 years). Continuous monitoring of GHG concentrations and fluxes is also crucial for GHG projections and to foresee climate-related scenarios such as Representative  Concentration Pathways (RCPs; van Vuuren et al.
Varying measurement techniques and data processing procedures resulted in limited comparability, and consequently increased the uncertainty in analysis outcomes and model predictions. Data precision conventions were also mostly absent, adding to modelling uncertainties which depend both on model structure (representing our understanding of the processes) and on the quality of parameterisation data. Furthermore, data were often stored in different formats and archived separately for each project/network, limiting the potential benefits of data sharing and integra-tive model assimilation. However, the scientific outcome of these early network initiatives was often of the highest quality and reflected the state-of-the-art methodology available at the time. While frequent scientific reorientation and short project durations limited the confident extrapolation of short-term datasets to long-term trends, these early initiatives still raised important questions about our understanding of C and GHG dynamics within and across ecosystems.
The urgent need for a thorough, consistent and long-term data collection and analysis approach integrating the three Earth system domains, initiated the transition from a short-term, project-based framework to ICOS as a highly integrated RI over the last 15 years. A2  and A3. The white boxes on the right indicate recent ICOS projects. GHG observation system. With this future growth in mind, ICOS is developing a comprehensive cross-domain array of atmosphere, terrestrial ecosystem and ocean observations, as required for an in-depth understanding and conceptualisation of biogeochemical cycles evolving under a changing climate. This approach will enable ICOS to significantly contribute to a number of scientific network programmes such as the World Meteorological Organisation (WMO)driven Integrated Global Greenhouse Gases Information System (IG 3 IS), the Group on Earth Observation (GEO)   A3 ), yielding synergies from joint measurement strategies, common standards, and the co-location of sites or common data life cycles.
The pan-European research infrastructure ICOS ICOS is a distributed European RI providing in-situ standardised, traceable and verifiable, high-precision observations of lower atmosphere GHG concentrations as well as biosphere-atmosphere GHG fluxes, where measurements are intended to last for ≥ 20 years. The observations are complemented by a large set of multi-disciplinary data required to document changes in ecosystem composition, structure and functioning and for the interpretation and modelling of the observed GHG concentrations and fluxes (Table  A1 ). The overarching goal of ICOS is to facilitate high-quality research on the status, future responses and driving forces of GHG and C cycle dynamics, as well as the role of atmospheric, terrestrial and oceanic systems in the development of, and response to, future climate change.
Furthermore, ICOS implements the attributes that were identified to be important for effective observation networks, including an integrated data management structure, facilitating effective data sharing and assimilation, and a scientific network engaged with frequent communication, dissemination and joint scientific development  (Baldocchi et al. , 2012). ICOS follows the monitoring principles of the GCOS and the measurement recommendations from the Global Earth Observation System of Systems (GEOSS). Data collection, processing and archiving are harmonised and standardised. Furthermore, ICOS aims to ensure comparability with the set of ancillary measurements made within existing networks such as the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) to facilitate upscaling.
icos-cp. eu). The ICOS database represents a common, openaccess archive with a data set identifier system and clearly defined data usage policies (https://www. icos-ri. eu/sites/ default/files/cmis/ICOS%20RI%20Data%20Policy. pdf). This ensures verifiable, reproducible and transparent science, and accelerates scientific progress while returning scientific impact for the data producers  (Nosek et al. , 2015; Dai et al. , 2018).
A growing observational network and associated database will therefore continuously improve our understanding of the interactions between terrestrial ecosystems and the climate system  (Valentini et al. , 2000; Baldocchi, 2008). For example, recent findings have highlighted the relationship between climatic extremes and the associated climate feedbacks of the terrestrial C cycle  (Ciais et al. , 2005; Reichstein et al. , 2007; 2013 , Frank et al. , 2015;; Xiao et al. , 2016). Since the 1990s the routine use of EC technique for tower-based C, water and heat flux measurements has provided continuous observations over multiple years at a variety of sites and biomes in different climatic zones. Such measurements were identified as critical tools for the quantification of global and regional GHG dynamics, especially when conducted in combination with remote sensing observations and ESMs  (Baldocchi, 2014).
This then allows for the derivation of GHG budgets, inter-annual and inter-site comparisons as well as the spatial scaling of GHG exchange  (Baldocchi, 2003 (Baldocchi, , 2014;; Baldocchi et al. , 2017). Since the early 2000s standardisation in GHG flux data acquisition, processing and provision has been improved with the development of FLUXNET  (Baldocchi et al. , 2001) , a global network of regional flux tower networks, such as CarboEurope IP, AmeriFlux, Fluxnet-Canada, Asiaflux, CarboAfrica (Sub-Saharan Africa) and OzFlux (Australia and New Zealand, Table  A3 ). The FLUXNET database has already provided important new insights into the course and consequences of environmental change  (Baldocchi, 2005 (Baldocchi, , 2008;; Falge et al. , 2002; Law et al. , 2002; Schwalm et al. , 2009 Schwalm et al.
, , 2017;; Richardson et al. , 2010; Migliavacca et al. , 2011; Keenan et al. , 2013; Wolf et al. , 2016)  and will further benefit in the future from the inclusion of ICOS data facilitated by its open-access policy. In addition, the rapid development of new measurement tech-niques including infrared gas analysers and laser absorption spectrometers, and an increasing number of modelling approaches at various spatio-temporal scales have resulted in considerable progress in our understanding of the processes and impacts of environmental change, e. g. , for the European continent  (Ciais et al. , 2005; Jung et al. , 2007). Long-term EC datasets (> 20 years) are currently available for particular forest sites such as the Harvard Forest (Massachusetts, US, since 1991;  Urbanski et al. , 2007) , the Takayama Forest (Japan, since 1993;  Saigusa et al.
Sampling design at ecosystem stations: a balancing act between a comprehensive setup and practical feasibility A major challenge during the design phase of any ecosystem observation network is the identification of the essential environmental variables to be measured, their temporal frequency and spatial representativeness. The answer represents a trade-off between an ideal dataset that enhances our current process understanding and addresses key knowledge gaps, and the practical feasibility of an ambitious measurement plan in terms of budget, human resources and the sustainability of long-term infrastructure deployment. The ICOS Ecosystem Monitoring Station Assembly (MSA) and the Ecosystem Thematic Centre (ETC) have devised a trade-off that is built on the expe-riences of previous monitoring and experimental research networks.