dataesr/scientific-paragraphs-categorization huggingface dataset:
1 Go to DCSO validation directory [7] in GitHub; 2 On a different browser window or tab, go to the RDFShape validator service [8] ; [7] The DCSO Validation directory, in Github. https://github. com/RDA-DMP-Common/ RDA-DMP-Common-Standard/tree/master/ontologies/validation. Accessed 09 Mar 2022. [8] The RDFShape validator. https://rdfshape. weso. es/shExValidate. Accessed 09 Mar 2022. 3 On Github, copy the content of a valid DMP example from the file validexample d cso. 3. 0. 2. ttl; 4 Paste that valid example on the first text area of the RDFShape validation service, (e. g. , RDF input); 5 On GitHub, copy the content of the DCSO ShEx schema from the file dcsodmp. 3. 0. 2. shex; 6 Paste that schema on the second text area of the RDFShape validation service, (e. g. , ShEx); 7 Define what entity should be validated by adding the text ex : dmp 1 @ < dmp > to the third text area in the validator, (e.
Use Case: Data Stewardship Wizard Data Stewardship Wizard [12] or DSW, is a tool used for data management planning widely used in the European life-sciences Infrastructure for biological Information (ELIXIR) and beyond  [23]. Its versatility has been proven in several customisations such as VODAN-in-a-Box solution as a data-entry tool for case report forms  [24]  or as a FIP Wizard to allow efficient capturing of FAIR Implementation Profiles  [25]. The core idea of DSW is not to copy the structure of any funders DMP template and ask the same (open text) questions. Instead, DSW has a concept of knowledge models, extensible templates for smart questionnaires, where guidance is done in [11] The Transformation between DMP Common Standard serialisations application, in GitHub. https://github. com/fekaputra/dcso-json.
Then, if it is not a valid URI or the entity does not have an ID defined by DCSO, it still needs a URI. It is synthesised using related URI and question/reply UUID, e. g. , the URI of the DMP, the URI of the questionnaire and the UUID of dataset reply item. It is also important to point out the integrations in DSW. A user can, for example, select a funder using integration with CrossRef [14] , similarly a license from Wikidata [15] or affiliation from the Research Organization Registry (ROR) [16]. The URI of entry is then saved as a part of the reply and here used in the template for corresponding RDF triple following the linked data principles. Finally, Turtle is not the only RDF export format that DSW supports for maDMPs and DCSO. Using rdflib [17] for automatic transformations allows for export in [13] The Jinja project website.
https://jinja. palletsprojects. com. Accessed 09 Mar 2022. [14] The CrossRef website. https://www. crossref. org. Accessed 09 Mar 2022. [15] The Wikidata website. https://www. wikidata. org. Accessed 09 Mar 2022. [16] The Research Organization Registry Community website. https://ror. org. Accessed 09 Mar 2022. [17] The rdflib specification. https://rdflib. readthedocs. io. Accessed 09 Mar 2022. different formats such as RDF/XML, TRiG, N3, or JSON-LD. The export template is developed as open-source [18] , and anyone can easily contribute or use it. This approach allows both simple versioning of the template (e. g. , when a new version of DCSO is released) and adopting various future extensions to DCSO in independent branches.
Discussion The semantic representation of DMPs will facilitate exchange of information between the different services involved in data management as it becomes independent of their technical implementation. The use of standards to represent and annotate the DMP content makes it FAIR. In this ontology, classes are linked to existing and widely-used ontologies such as DCAT and Friend of a Friend (FOAF). Therefore, DMP content can be easily linked to other graphs such as PID graphs or Research object graphs. DCSO extension already provides a set of controlled vocabularies for languages, countries and currencies to annotate DMP content. This semantic representation of maDMP allows custom extensions of the format by adding properties to the classes or deriving subclasses from the maDMP classes.
Conclusions This paper report on DCSO, the creation process and path towards adoption by the DCS working group as a serialisation of the DCS application profile, as well as its adoption by the Data Stewardship Wizard (DSW), one of most the popular DMP creation tools. DCSO is a semantic representation of the DCS application profile, which cover all terms from the DCS application profile whilst also reusing terms from established domain ontologies. Furthermore, DCSO is equipped with a validation layer, which consist of a set of ShEx constraints that allows DMPs represented through DCSO to be validated against the DCS application profile. The team in charge of the development and maintenance of DCSO strives to follow the best practices in ontology engineering, whilst continuously trying to update and upgrade DCSO and its components.
Currently the team is focusing on preparing a formal proposal for the adoption of DCSO by the DCS working group as an official serialisation of the DCS application profile. Additionally it's also tackling or planning to tackle the following issues: (1) continued pursue of the integration of terms from other established ontologies into DCSO (e. g. the Data Integration for Grant Ontology (DINGO) [19] ), thus enriching the DCS application profile and potentially includes controlled vocabularies associated to all of the relevant concepts; (2) Performing semantic validation of DMP documents represented using DCSO would be an ambitious but useful feature, in particular for any funding agency stakeholders. Services aiming to perform the task through the usage of DCSO are currently being considered for the creation of proof of concept tools.
FiguresFigure1The class structure of DCSO. Figure1aThe class structure of the DCSO Core. Figure1bThe class structure of the DCSX. Figure 2 Diagram showing the shapes describing a DMP according to DCSO together with additional elements describing the associated project, cost, contributors and contacts. Validation related to Datasets is not included in this diagram. Figure 3 Excerpt of a ShEx schema validating a DMP. Figure 4 Example of a validation using RDFShape validation service.
Funding This work was supported by the  Austrian Research Promotion Agency FFG  under grant  877389  (OBARIS). Development and operation of DSW is supported from the  ELIXIR CZ research infrastructure project (Ministry of Education, Youth and Sports  grant No. LM2018131 ). SBA Research SBA-K1 is a  COMET Centre within the framework of COMET -Competence Centers for Excellent Technologies Programme  and funded by  BMK ,  BMDW , and the federal state of Vienna; COMET is managed by  FFG. The researchers from INESC-ID were supported by national funds through  Fundação para a Ciência e a Tecnologia (FCT)  with reference  UIDB/50021/2020 , by project  DRE ,  Imprensa Nacional -Casa da Moeda S. A. (INCM) , and Portugal 2020. Finally, the authors would also like to thank the  RDA  for establishing the community that made this collaboration possible.
Figures Figure 1 1 FiguresFigure1The class structure of DCSO. Figure1aThe class structure of the DCSO Core. Figure1bThe class structure of the DCSX. Figure 2 Diagram showing the shapes describing a DMP according to DCSO together with additional elements describing the associated project, cost, contributors and contacts. Validation related to Datasets is not included in this diagram. Figure 3 Excerpt of a ShEx schema validating a DMP. Figure 4 Example of a validation using RDFShape validation service.
The Data The empirical strategy relies on a comparison between the UK and other European coun- Given that not all ETS participants are power installations 16 , one crucial step is to identify power installations in the EUTL data. The main activity of each installation is publicly available, but there is no specific activity category for power installations. I rely on data provided by the UK-based think-tank Ember (formerly Sandbag) and a one-off file with more precise activity codes circulated by the EU Commission to identify power installations. Appendix A. 3 describes the specific steps followed. I identify a total of 4,938 power plants, including 302 in total for the UK, with an average of 190 active power plants per year in the UK over the 2005-2012 period and 189 active power plants per year in the other twenty EU countries 17 , over the same period (See Appendix A.
4% in 2012); and standby generators, also representing a small share of emissions 18. I aggregate plant-level emissions at the country level, separately for power and non-power plants  19. I obtain emission data for a panel of 21 European countries for the 2005-2017 period. I add to this panel a set of annual country-level variables which are used in the descriptive analysis (see section A. 1) or/and in the empirical strategy: country population, installed capacity and power generation by source, electricity consumption, electricity imports and exports, coal and gas prices, availability of lignite resources (a particularly polluting type of coal only used domestically), average age of the coal-fired plants. Most of these data come from Eurostat. See appendix A. 5 for details on each variable's source.
3 and in appendix A. 2, I add one predictor measuring for each country the amount of emissions coming from installations that opted out from the LCP directive in 2004 and are expected to shut down in 2015. To build this variable, I first identify the name and location of plants opted-out from the LCP directive based on the LCP data available on the European Environmental Agency's website. I then manually identify these plants in the EUTL installation-level emission data 22 to know how much CO 2 these plants emit each year. For each country and each year, I calculate the sum of power sector emissions coming from LCP opt-out plants. I divide each sum by the country population to obtain a variable of per capita LCP opt-out emissions. The opt-out decision had to be made before the CPS was introduced.
Notes: For each period, the variable of per capita emissions corresponds to the sum of CO 2 e verified emissions from power installations subject to the EU ETS except those in the UK converted to biomass, divided by the average country population that year. The synthetic UK is made of five countries: Ireland (49. 2%), Slovakia (25. 6%), the Netherlands (13. 7%), Finland(5. 8%), Czech Republic (5. 7%). Drawing on the difference-in-difference methodology, I calculate for each component the to the pre-treatment period, while I use annual emission data and have to allocate all the 36 Two large coal-fired UK plants shut down in the period considered while they had neither opted-out from the LCP or IED directives: Rugeley power station closed in March 2012 and Longannet power station closed in March 2016.
Notes: Emission values were obtained by aggregating plant-level emission data for ETS participants identified as power generators at the country level. Per capita emissions were obtained by dividing total emissions by the annual country population. "Other countries" include twenty European countries: Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, the Netherlands, Poland, Portugal, Slovakia, Spain, Sweden. ). There is no obvious break in trend in 2013. This continuous decrease is consistent with the continuous improvement of energy efficiency in buildings and electric appliances in the UK since 2009  (Staffell, 2017). The lack of a visible link between the CPS and a change in demand can be linked to the finding by Chyong et al.
Compared to the planned delivery year of projects mentioned in the CfD auction results, the actual date where a CfD project becomes operational is often delayed by several months. Given the actual date where each project becomes "operational" according to BEIS's renewable energy planning database, only 1,035 MW of renewable capacity from CfD projects is operational by the end of 2017. This represents 2. 4% of the total renewable capacity installed in the UK in 2017 49. I combine data on the date where each project becomes operational with data on running hours for each project (coming from the CfD auction results) to calculate the electricity generation associated with this installed capacity over the 2013-2017 period. I estimate that at most 1,588 GWh of electricity may have been generated by CfD projects -assuming that the theoretical number of hours given in the CfD auction results is the true number of hours.
This represents just 0. 4% of the total electricity generated with renewable energy sources 50 and 0. 1% of total electricity generated in the UK over that period. Abrell et al. (2019)  report that the average capacity-weighted emission rate of UK coal-fired plants (taken before 2013) is 0. 89 ton of CO 2 per Megawatt-hour of electricity produced. Such an emission rate implies that the 1,588 GWh of electricity produced by CfD projects would have caused emissions of 1. 4 MtCO 2 e 51 over the 2013-2017 period if they had been generated by coal instead. This amount represents less than 1% of UK power sector emissions in 2013. Other support policies to renewable energy exist in the UK, but the bundle of feedin-tariffs, support to R&D for renewable energy, and regulatory instruments does not look fundamentally different from that implemented in other European countries, according to the IEA/IRENA Joint Policies and Measures Database listing the support policies implemented 48 available at: https://www.
gov. uk/government/publications/renewable-energy-planning-database-monthly-ext 49 The denominator includes waste and biofuels, since part of the CfD projects are for power generation from waste 50 The denominator includes waste and biofuels, since part of the CfD projects are for power generation from waste 51 1, 589. 10 3 × 0. 89 = 1, 414, 210 tCO 2 e in each country since the 1970s 52. All in all, the difference in emissions observed after 2013 in the UK does not seem driven by a renewable energy policy specific to the UK, apart from the support to biomass conversion described above. UK level: capacity market The capacity market introduced in 2013 may impact emissions in two ways: first, before the capacity payment starts, securing a capacity contract can incentivize investing in new capacity, as auction payments can be seen as a subsidy for new power generation.
Indeed, the capacity market was initially supposed to facilitate investments in new gas capacity  (Evans, 2015). Second, once the capacity payment starts, the payment can keep a plant being economically profitable even with at low generation levels. A. 4 Summary statistics for the country-level dataset     Abrell et al. Column (3) : emissions for the coal units over the 2009-2016 period were estimated by applying an average emission factor of 0. 9 tCO 2 /MWh, the average emission rate reported by Abrell et al for Drax plant. *Emissions for the coal units in 2017 are estimated to be the same as emissions for the entire plant as reported in column (1), since all biomass units are fully converted by then and emit zero CO 2. Column (4): emissions for the biomass units if they had not converted to biomass are estimated differently for the 2009-2012 and for the 2013-2017 period: for the 2009-2012 period, the units had not yet converted to biomass, so their emissions are simply the difference between EUTL emissions and emissions estimated for the coal units (column (1) -column (3)).
5%), Poland (20. 7%), Ireland (16. 1%), and Denmark (1. 7%). Specification with the average age of the coal-fired power plants: six countries: Slovakia (35. 2%), Czech Republic (31. 2%), Sweden (12%), Hungary (11. 2%), Spain (7. 5%) and Finland (3%). 2017 period. This aggregate variable does not allow to identify individual plants and isolate confounding factors like I do in the main analysis. But it would be reassuring to find an emission reduction close to the main result when I apply the synthetic control method to this longer dataset. I keep the same predictors as in the main specification. I run a permutation test similar to the one performed in section 4. 3. Figure A. 10 shows that the decrease in emissions seen in the UK is not found with the same magnitude in other European countries.
tries and requires assembling a dataset at the country level. I combine installation-level data on carbon emissions aggregated at the country-level with country-level data obtained from different sources. The installation-level emission data come from the European Union Transaction Log (hereafter EUTL), the official register of the EU ETS, managed by the EU Commission. The EUTL checks, records and authorises all transactions taking place between participants in the EU ETS. Every year since 2005, the start date of the EU ETS, participants have had to report their CO 2 e emissions and surrender enough emission allowances to cover their emissions. Reported emissions are verified by an accredited verifier.
This band corresponds to the average consumption of large gas-fired power plants as reported in the European Environmental Agency's Large combustion plant database. I fill the few data gaps by imputing values from the third largest consumption band, or, if it is also missing, from the IEA gas price data. One drawback of this data source is that the consumption band categories and the methodology changed in 2007, which makes it difficult to build a consistent series of coal/gas price ratio before 2007. For this reason, I average the coal-to-gas price ratio predictor over the 2007-2012 period only (rather than the 2005-2012 period) when I apply the synthetic control method. I convert the obtained coal and gas price series to the same unit and combine them to build coal-to-gas price ratio for all European countries over the 2007-2016 period.
Furthermore, this complementary approach enables a cross-check of the results, so that the most up-to-date and precise information sources are used for the global assessment. As the collected numbers are also provided to the IEA for the annual Global EV Outlook publication, a further check of the data was undertaken. The survey focuses on road vehicles; therefore, fuel cell material handling devices are not included in the numbers. Similarly, fuel cell trains were not included in the analysis. The numbers reflect those vehicles on the roads, namely registered vehicles. Vehicles that were previously utilized in demonstration projects and already taken out of operation are not included. Similarly, vehicles that were produced and not yet put on the road are also not counted. The vehicle numbers were grouped into five categories, which are listed in Table  1.
Fuel Cell Vehicles According to our assessment, the worldwide fleet of FCVs surpassed 50,000 units, with 51,437 being on the road as of the end of 2021. The numbers of vehicles for each country and vehicle category are provided by AFC TCP member states Austria  [26] [27] [28] , Denmark  [29] , Canada  [30] , China  [31] , France  [32] , Finland [33], Germany  [34] , Italy  [35, 36] , Japan  [37] , Spain  [38] [39] [40] , Sweden [41,42], U. S. [43] [44] [45] , and South Korea  [46]  in the first place, based on the survey input. Data from further European countries such as Belgium, Estonia, Iceland, Latvia, Czech Republic, Luxemburg, Poland, and Slovakia was provided by the Clean Hydrogen Partnership Programme Office based on data from their database  [47]. Furthermore, complementary European data was also taken from the European Alternative Fuels Observatory  [48] [49] [50] [51].
g. , fuel cell transit buses receive a grant of USD 400,000  [82]. In Connecticut, the residents can receive incentives of up to USD 9500 (standard rebate: USD 7500, Rebate + New incentive USD 2000) from the Connecticut Hydrogen and Electric Automobile Purchase Rebate (CHEAPR) for all three FCV types available  [83]. Similarly, the Massachusetts Offers Rebates for Electric Vehicles (MOR-EV) program provides a rebate of USD 2500 for the purchase or lease of FCVs  [83]. It should be noted that further incentives are available in other federal states, which can be reviewed in detail in the Alternative Fuels Data Center database of the U. S. Department of Energy  [84]. China has the third highest fuel cell vehicle population and the second highest number of HRSs in operation. As mentioned previously, its market is dominated by buses, mediumand heavy-duty trucks, unlike any other country.
The increase rate in 2020 was 38%, and thus comparable to the increase rate of passenger cars. In 2021, an improvement was observed with a 48% increase rate in one year. Energies 2022, 15, x FOR PEER REVIEW 21 of 37 first data from passenger cars are for 2017, whereas the data collected for all vehicles started in 2018. It should be noted that Hyundai started offering the world's first massproduced fuel cell vehicle, the ix35 Fuel Cell, for lease in U. S. in 2014 and the Toyota Mirai was launched at the end of 2014 in Japan  [134]. For passenger cars, after a 56% increase in the annual deployment rate from 2017 to 2018, the highest increase rate of 69% was recorded from 2018 to 2019. The increase rate slowed down to 37% in 2020, which can be explained by the global economic recession caused by the COVID-19 pandemic.
Data Availability Statement: Not applicable. Acknowledgments: Data collection by the Advanced Fuel Cells Technology Collaboration Programme (AFC TCP) Executive Committee Members. The authors are deeply grateful to all Executive Committee Members of the AFC TCP who delivered data for the annual data collection. Special thanks also to the Clean Hydrogen Partnership Programme Office for supplying the European data. The results from the data collection were originally provided to the Global EV Outlook 2022 publication of the International Energy Agency (IEA). The authors thank Elizabeth Connelly and Jacob Teter from the IEA Secretariat for the excellent collaboration and information exchange.
[140]r of FCVs at the end of 2021 from this work (left) vs. GDP and GDP per capita based on the OECD database (right)[140]. 25,000 300,000 Number of fuel cell vehicles 5,000 10,000 15,000 20,000 5000 50,000 100,000 150,000 200,000 250,000 GDP per capita / US dollars per capita GDP / Million USD 0 Brazil Slovakia Estonia Finland Czech Rep. Luxemburg Poland Costa Rica Total (FCV) Spain Iceland Austria India Sweden Italy GDP Belgium Denmark Australia Norway GDP per capita Switzerland Canada France U. K. Netherlands Germany Japan China U. S. Republic of Korea 0 Figure 16.
Acknowledgements The authors of this work would like to show their gratitude to the financial support from the  European Regional Development Fund  in the framework of the  Interreg Atlantic program  through the project  "HYLANTIC"-EAPA_204/2016 ". Besides, we thank  M. -L. Rouget  ( Pôle Spectrométrie Ocean, IUEM Brest ) and  Dr. M. Waeles  ( LEMAR, UBO Brest ) for their help in elemental ICP-MS analysis,  Dr. S. Rioual  ( Lab-STICC, UBO Brest ) for his help in XPS analysis, and  P. Elies  ( SC PIMM, UBO Brest ) for his help in SEM-EDX analysis. The authors thank for technical and human support provided by  SGIker of UPV/EHU  and European funding (ERDF and ESF). J o u r n a l P r e -p r o o f.
From their simulations, they showed that the natural circulation flow rate is influenced by solar radiation and the temperature inside the storage tank. A correlation between those two factors and the flow rates was established using Reynolds and modified Rayleigh numbers. Later,  Budihardjo and Morrison (2009)  implemented the correlation using the Trnsys software to simulate the performance of WIG-ETC over a year in Sidney  [7]. The performance of solar water heaters for domestic utilization has been examined. Chow et al. (2011)  assessed the energy outputs of WIG-ETC and HP-ETC in a subtropical climate  [8]. They found that the HP-ETC has a higher useful annual energy gain. Nevertheless, they obtained approximatively the same payback durations thanks to the lower cost of WIG-ETC. In order to ensure high water temperatures, collectors have to be hydraulically connected to design the solar collector field.