dataesr/scientific-paragraphs-categorization huggingface dataset:
Data provided at the scale of the whole Seine basin were grouped by water body units according to the lithology and stratigraphy  [34, 59]. In-river CO 2 concentrations simulated with Riverstrahler match the CO 2 estimated with the Water Agency database  [34, 79]  (Fig. 8 ). The values in the Marne River, a major tributary of the Seine River upstream of Paris, indicate an upstream "background level" of 1. 2 mgC L À1 , which is above the CO 2 saturation value of around 0. 2 mgC L À1 (393 ppmv) at 10 C, whereas WWTPs outflows lead to a supersaturation of >2 mgC L À1 in the lower Seine River from Paris to the outlet. Based on these simulations, an annual average budget (2010-2013) of the Seine River is calculated for inorganic (IC) and organic (OC) carbon (Table  2 ). Inorganic and organic carbon budgets differ by one order of magnitude, with total inputs to the river system of 17,300 and 1,600 kg C km À2 year À1 , respectively.
As a result, changes in fish communities over the past three or four decades can be analysed in detail. To go back further in time, it is necessary to use other data sources such as historical written sources. These historical written sources are of multiple types (early scientific and naturalist literature, fishery laws, administrative surveys on fish stocks, catch records, river engineering projects, observations by fishermen and anglers, etc. ), each type of source having its own advantages and limitations. For the Seine River basin, a database called CHIPS was developed to gather historical written sources on freshwater fish in order to improve knowledge about past species distribution and composition of communities  [7]. While the oldest data stored in this database date back to the sixteenth century, most of the observations relate to the period from the nineteenth to the first half of the twentieth century and are therefore complementary to the more recent electrofishing data set.
Estimates of the impact of WWTPs were computed from the median concentrations discussed in Sect. 5, from the nominal daily volumes treated by WWTPs and river discharges at the low stage (monthly average with a 2-year return period, data from the French national hydrological database). The expected concentration increases after dilution in the river were 1,080, 130, 1,810 and 1,390 E. coli per 100 mL for the SAM, SEC, SAV, and MAV WWTPs, respectively. The strong increase of median concentration and flux at Ivry is twice as high as the discharge of the SAM WWTP only. This suggests the existence of additional sources. During summer 2017, contamination levels were analyzed in the Seine and Marne rivers during well-established dry weather periods  [50]. Samples were taken every kilometer, near each bank, and in the center of the flow.
Several families of numerical models have been generated. Their common characteristic is their continuous evolution: More processes are taken into account, their resolution is finer, and they are extended temporally (backcasting, scenarios of the basin futures), as well as spatially (to the estuary, to other basins). Sub-models are added and gradually connected to the original ones (as presented in Box 3). Models work with data sets acquired at different time stages. They also provide an opportunity to check data consistency with the partners. 'The models appeared also very useful for confronting the various databases gathered by Water Authorities on human pressures and those on water quality. Many incoherent data could be detected during the process of informing the models, resulting in a significant improvement of the databases and hypotheses that have been hotly debated.
Scientific Knowledge and Action Scientific knowledge and action are linked but in a complex way. Action can be taken without the production of knowledge, and knowledge does not always trigger action. Actors must be regularly informed about scientific progress in a suitable way to generate action. Scenarios built up by scientists and their partners must be discussed by members of the river basin committee (the deliberative level). Many water quality issues have not been, or could not be adequately, addressed in the past by scientists due to a lack of fundamental research (e. g. in the field of ecotoxicology and long-term effects of low-level contamination on human health); technical means (lack or limitation of analytical means for most micropollutants before the 1990s; lack of GIS before the 1990s); or available databases (e.
Etude expérimentale et éléments de modélisation. PhD thesis, Ecole Nationale Supérieure Agronomique de Montpellier, p 270 76. Morvan X, Mouvet C, Baran N et al (2006) Pesticides in the groundwater of a spring draining a sandy aquifer: temporal variability of concentrations and uxes. J Contam Hydrol 87 (3-4):176-190 77. HYDRO database http://www. hydro. eaufrance. fr/78. Viennot P, Gallois N (2017) Modélisation de la pollution diffuse d'origine agricole des grands aquifères du bassin de Seine-Normandie: Scénarios d'évolution climatique -Impacts et incertitudes. Technical report, ARMINES/MINES ParisTech 79. Mattei A (2017) Eléments de prospective de la contamination des cours d'eau d'Ile-de-France par les Pesticides. Confidential technical report, p 50 80. R Core Team (2014) R: a language and environment for statistical computing.
R Foundation for Statistical Computing, Vienna. http://www. R-project. org/81. SOeS database https://www. statistiques. developpement-durable. gouv. fr/82. Anglade J, Billen G, Garnier J, Makridis T, Puech T, Tittel C (2015) Nitrogen soil surface balance of organic vs conventional cash crop farming in the Seine watershed. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material.
3. 1. 1Emissions to the AtmosphereUrban and industrial PAH emissions to the atmosphere were estimated based on the INERIS (French National Institute for Environmental Technology and Hazards) database. A recurrent spatial inventory of atmospheric pollutant emissions including PAHs has been made since 2003 by the INERIS. It includes emissions from all reported sources, whether anthropogenic or natural. Since source types were specified, the PAH emissions related to household heating, road traffic and industrial activities were estimated independently. Thus, household heating and road traffic, respectively, amounted to E5a ¼ 11 tons year À1 (Σ16, naphthalene included) and E5b ¼ 4 tons year À1 (Σ16) of PAHs, leading to total urban emissions of E5 ¼ 15 tons year À1 (Σ16). At the same time, industries emitted E6 ¼ 130 kg year À1.
Acknowledgements This work is a contribution to the  PIREN-Seine  research programme (www. piren-seine. fr),which belongs to the  Zone Atelier Seine part of the international Long-Term Socio-Economic and Ecosystem Research (LTSER) network. Acknowledgements This work is a contribution to the  PIREN-Seine  research programme (www. piren-seine. fr),which belongs to the  Zone Atelier Seine part of the international Long-Term Socio-Ecological Research (LTSER) network. ArchiSeine is the result of a work carried out within a convention between the PIREN-Seine research programme and the  French National Archives. Acknowledgements This study is a contribution to the  PIREN-Seine  research programme (www. piren-seine. fr),which belongs to the  Zone Atelier Seine part of the international Long-Term Socio-Ecological Research (LTSER) network.
Acknowledgements This work was carried out in the scope of the  PIREN-Seine  programme, supported by the  Seine-Normandie Water Agency  and several other partners, and could benefit from the scientific context of the LTSER Zone Atelier Seine managed by the  French National Center for Scientific Research (CNRS). Acknowledgments The authors would like to thank all partners of the OCAPI programme (www. leesu. fr/ocapi)and the  PIREN-Seine  programme for their technical and financial support to this chapter. The PIREN-Seine research programme (www. piren-seine. fr)belongs to the  Zone Atelier Seine part of the international Long-Term Socio-Ecological Research (LTSER) network. Acknowledgements This work was initiated by the  CNRS  project  EC2CO  Phyt'Oracle in 2008 and has since then been extended as part of the  PIREN-Seine research programme , a component of the  Zone Atelier Seine  within the international  Long-Term Socio-Ecological Research (LTSER)  network (www.
piren-seine. fr). Acknowledgements This work was conducted in the framework of the  PIREN-Seine research programme  (www. piren-seine. fr),a component of the  Zone Atelier Seine  within the international  Long Term Socio-Ecological Research (LTSER) network. Acknowledgements This work is a contribution to the  PIREN Seine research program  (www. piren-seine. fr),within the framework of the  Zone Atelier Seine , a site of the international  Long Term Socio Ecological Research (LTSER) network. This work was partly funded by several other projects (Thresholds -EU  FP6-, Aware -EU FP7-Seas-Era Emosem -EU FP7 ERA-NET-, Liteau FLAM -French Ministry of Ecology and Water Agency-, ITN C-Cascades -EU Horizon 2020 -). Acknowledgements We sincerely thank  Dr. Benoit Pernet-Coudrier , assistant professor at the  University of Western Brittany , who passed away accidentally in 2016.
Acknowledgements This work is a contribution to the  PIREN Seine research program  (www. piren-seine. fr),which belongs to the  Zone Atelier Seine , a site of the international  Long Term Socio Ecological Research (LTSER) network. This work was partly funded by the  French Agency for Biodiversity (AFB). Acknowledgments The authors thank  Paris STEA  and the  Syndicat Marne Vive  who provided a substantial amount of the FIB data used in this study. This research was conducted within the framework of the  PIREN-Seine program , a component of the  LTSER "Zone Atelier Seine. " Acknowledgments The authors would like to acknowledge the support of  EPHE  and  R2DS   Ilede-France  (i. e. Paris regional research network on sustainable development), which both provided a PhD grant. The authors would also like to thank the  Aquitaine Region  and the  European Union  ( CPER A2E  project) for their financial support, as well as the  French National Research Agency (ANR)  for its funding through  IdEx Bordeaux  ( ANR-10-IDEX-03-02 , PhD grant), the Investments for the  Future Program (Cluster of Excellence COTE ,  ANR-10-LABX-45 ) and the  SEQUADAPT  project (headed by  L.
g. , accessing shared mobility stations, tracking household energy consumption, or tracking local renewable energy production, supporting citizens in their daily lives. Cities intend to increase the availability of meaningful datasets. Local authorities strive to establish data platforms for monitoring and automated data transfer between project partners and to stimulate an understanding of how the related infrastructure can include participation by various municipal stakeholders. Lyon, Munich, and Vienna, as lighthouse cities (LHCs) of the Smarter Together (ST) project, illustrate different SDP implementations for new data-based services visualizing building energy performance indicators and to offer new data-based municipal services improving urban quality of life. If invisible data is at the heart of this process, it is significant to remember that digital solutions rely above all on physical infrastructures.
The Evolution of the Grand Lyon Data Platform The Grand Lyon Data Platform has been in operation since 2013, offering 99% publicly accessible data. Authentication mechanisms protect access to sensitive semi-private and private data on the platform based on Open-Source software standards. The smart data-platform successfully stores private data securely. The Confluence monitoring system (CMS) is the first digital dashboard developed by the Grand Lyon (the Greater Lyon Region, a. k. a. Métropole de Lyon) to collect and visualize energy data. The CMS addresses four use-cases defined by the Lyon La Confluence Team for the ST project. The use cases ensure compliance with design objectives and specifications, evaluate building performance for efficient building systems operation, provide individual residential energy consumption profiles for individual households of refurbished buildings, monitor overall energy production from renewable sources, and monitor total energy consumption in the Lighthouse Quarter.
Data with 10-minute, 30-minute, or hourly time steps are uploaded daily to the data platform. CMS development and connection to the Data Platform is one of the replication projects in ST  [17]. SPL Lyon Confluence (SPL) created the first data platform for La Confluence in parallel to a larger metropolitan platform initiated by Grand Lyon for the Greater Lyon Area. Since 2010, data platforms in several major French cities within the Greater Lyon Region (France) analyze municipal data sets to offer new citizen services. Local authorities partnered with external companies to develop new public services using open data. The data from La Confluence provides inputs for the Grand Lyon SDP, Figure  1. Today, the building energy data of the building stock, including both new construction and the thermally refurbished buildings, is automatically uploaded to the data platform.
Data from both building energy consumption and on-site electricity generation are included, enabling the energy performance of the La Confluence building stock to be monitored and evaluated over time. In Lyon, as holistic building refurbishment and energy performance were paramount in the La Confluence refurbishment, SPL and Grand Lyon sought an overall "energy view". Therefore, data sent to the Grand Lyon data platform originates from smart power meters of the electricity utility, smart district heating heat meters, building energy management systems (BEMS), and photovoltaic production. Weather and mobility data are also a source for the urban data platform.
The SDP is synchronized with the Data Gatekeeper (DGK) presenting a comprehensive blueprint about data handling including data privacy and data security aspects in a Smart City context  [20]. The Data Gatekeeper (DGK) describes the main structures to design the data platform. The platform and the Gatekeeper were designed and implemented in parallel, taking into consideration the different iterations that were made during this phase. For that reason, the Data Gatekeeper also includes a comprehensive set of golden rules that could serve other cities to get a better understanding of the potential traps and hurdles potentially appearing when dealing with Smart-City data. The aim was to set the rules and learnings from the project to use them in other projects in the future. Figure  2  shows a schematic of the DGK.
Development of a New Data Platform for Vienna Vienna developed a completely new data platform for the ST demonstration projects in the Enkplatz and Geiselberg districts based upon future internet ware (FIWARE). Vienna focuses on the energy, ICT, and mobility solutions in a smart neighborhood emphasizing citizen engagement and governance. The Vienna DMP collects, transfers, stores, exchanges, and visualizes data from various demonstration projects. Customized models are developed to derive key performance indicators (KPIs) to assist in the evaluation and interpretation of gathered data. Specific applications utilize the smart infrastructure to facilitate interaction with citizens through web and mobile applications. Figure  3  illustrates the schematic overview of the digital platform. Vienna developed the Smart City Wien (SCW) Platform based on the Supervisory Control and Data Acquisition (SCADA) system architecture operated by the Municipal Department for Construction and Building Management, MA34.
The SCADA platform uses automated data processing and was developed with an open-source structure and was temporarily implemented using an external European cloud solution with the support from the FIWARE Foundation. However, FIWARE is only equivalent to a base-operating system consisting of several modules. The City of Vienna, therefore, needed to develop its application or user interface, either using all necessary FIWARE modules or adapting a ready-made solution. The VM9 FIWARE application was chosen due to time and cost restraints.
The purpose of building energy monitoring through the data platform is to document and verify building energy consumption pre-and post-renovation. Digital infrastructure for traffic supports the reduction of fossil-fuel vehicle uses by coordinating key data about new cycling, and e-mobility infrastructure for both transport operators and local citizens as transport users. The combined actions improve the quality of life for all inhabitants and their involvement in the district redevelopment process through cocreation of customized new citizen services. The characteristics of the platforms in each city are illustrated in Table  1. All cities use open government data (OGD) as common inputs. However, the chosen technologies, security concepts, visualization tools, and data formats are unique to each city.
Data Provided The data gathered and stored within a city's urban platform vary from city to city. Four types of data are normally collected: 1. Power and heating generation, and distribution; 2. Building energy consumption; 3. E-mobility energy management and traffic; 4. Local climate and pollution data. Energy and heat flow, pollution, local weather, traffic, and geographic location data are gathered and sent to the platforms to provide static, periodical, and real-time information. The data is collected from the monitoring infrastructure in the LHCs. Roles and responsibilities in the monitoring process are defined. Data-sharing agreements have been signed with providers with data flowing from the various collection sites. The ST project enabled collection of a wide range of data sets to establish new municipal services for the Lyon La Confluence district, the Lyon project district in ST, by building upon a district data platform created within the Franco-Japanese Lyon Smart Community Program from 2011 to 2016.
Most of the datasets in Lyon are open government data, accessible without restriction to citizens. The data sets are used by private companies, such as Transit, Citymapper, and architectural and engineering firms. Datasets are integrated into private companies' databases for further development of new citizen services. No personal data is collected in Munich and Vienna in standard use cases, as the citizens who participated in cocreation workshops rejected the idea. Real-time building data is used to improve facility and energy management, error detection, and system calibration in Vienna. Demonstration applications in the LHCs included data management from building energy refurbishment, car-sharing, e-mobility solutions (including e-bikes, e-buses, evans, and e-forklifts), neighborhood renewable energy supply, lamppost sensor data, district heating networks, and street lighting.
Data providers designed a wide range of APIs for the smart data platform. A diverse array of technical interfaces and different data types can integrate into one system for crosstopic analyses and user cases. The central purpose was to apply and establish proven IT-Standards, both for the API and the data formats, to prevent future "vendor-lock in". Public data is already available in Vienna. The collected data is aggregated per building and collected daily to respect the inhabitants' privacy. Individual data collection has a 30-minute time-step. Aggregated energy consumption values are used.
Data Flow Data management and smart services projects are divided into three main categories comprising infrastructure, platforms, and applications as presented in Figure  4. Although data seems fluid and invisible, it is a constant challenge to organize data due to the dependence on various technical infrastructures and legal arrangements. The data type obtained raises privacy issues as not all data can be collected, especially individualized data. There are also technical, economic, and energy limitations that restrict data flow to the platform. In Lyon, the data platform is accessible to all internet users through a web portal. The main portal functionality is the search engine for finding data sets using keywords. The Grand Lyon data platform collects, edits and publishes more than 800 sets of data, mainly from the databases of the Metropolis' services in various fields: traffic infrastructure, waste management, geographical information, finance, education, and environmental data.
The guidelines for new buildings specify selected energy data sharing with the Grand Lyon data platform. Contractors install smart meters meeting the data-collection specifications. Smart meters and sensors connected to the city platform are integrated as a requirement for ST eco-refurbished buildings. The owners of the existing buildings in Lyon La Confluence must install smart meters as a part of their program of construction works. Since the solutions monitored within ST are implemented for, and in cooperation with the LHCs, there is an agreement that all collected data are shared with the data management platforms of each city. In Vienna, the demonstrated building solutions focus on social housing that is mainly in municipality ownership. The Munich DGK was implemented according to the protocol, which can be described as a process and workflow for cities handling Smart City-related data.
e. , data platforms and/or infrastructure applications, will be implemented in Munich. The overall functionality of the SDP has successfully shown following an end-to-end data exchange scenario within a proof-of-concept demonstrator using the Smart Home Sensors and including all main system elements: raw data input, data correlation, APIdefinition, and smart data output. The urban data platforms in Lyon and Vienna are operational. In Munich, a digital twin and urban data platform are in the planning stage with a planned launch in 2021, as a follow-up project. The SDP will be an operational data platform based upon the experiences from ST. The project findings and the Data Gatekeeper Projects also help to develop and implement future services for the Munich Digital Twin. Monitoring is underway in Vienna.
Technologies In Lyon, the data platform is composed of three layers: back, middle, and front office. At the back-office level, the Géosource software collects and manages internal data sources, while cataloguing metadata. The feature manipulation engine extraction transfer loading (FME ETL) tool integrates and organizes external data sets, generates a copy of the data catalogue, and authenticates the API in the middle office. In the front office, data is replicated and catalogued. The API is authenticated; and data is published through the authenticated APIs, MapServer, and SOS formats. The middle and front-office are hosted outside the municipal IT infrastructure by an external internet service operator, ensuring ready availability and security. Furthermore, Grand Lyon developed the CMS web application to provide new energy data-based services on the smart data platform.