dataesr/scientific-paragraphs-categorization huggingface dataset:
The fractions of the various ecosystems are smoothed and seem to better represent the vegetation diversity, thanks to the use of higher resolution datasets for the PFTs mapping. These differences should significantly impact the DGVM simulations. Indeed, PFTs fractions are used to define the vegetation characteristics in terms of photosynthesis capacity, phenology, roughness, etc. All these properties are determinant for the calculation of the water and carbon fluxes, especially the evapotranspiration and the GPP fluxes. Consequently, such modifications should impact the biosphere-atmosphere exchanges and will be analyzed in further works. This study also showed the difficulties to link vegetation classes to a limited number of PFT, constrained by global modeling and time computing issues. The absence of a shrub PFT and the solution to distribute the shrubs classes among grasslands, bare soils and forests is not satisfactory.
developed the SYNMAP product dedicated to earth system modeling, based on the merging of GLC 2000 and MODIS 4. 0 products. The final map, which separates 48 classes, is available at 30 scale (∼ 1 km). Frey and Smith (2007) inter-compared AVHRR and MODIS products at 1 km scale on West Siberia and highlighted the weaknesses of global LC products in northern wetland environments. Urban et al. (2010), focused on pan-arctic land cover mapping, and combine GlobCover, SYNMAP, MODIS LC and VCF and additional regional products like fire products, to create a new harmonized map separating 4 classes: trees, shrubs, herbaceous and barren areas. Sulla-Meneshe et al. (2011) developed the Northern Eurasia Land Cover (NELC) database from supervised classification of MODIS data which allows to separate 15 land cover classes including land use (urban, agriculture), wetlands and tundra classes.
Meanwhile, Pflugmacher et al. (2011) cross-compared GLC 2000, GlobCover and MODIS products as well as Landsat-based reference maps on northern Eurasia. The map legends were converted to a common classification on the basis of Discussion Paper | Discussion Paper | Discussion Paper | dominant life form type (LFT). The results show regional disagreements among products and the difficulties to map shrubs and herbaceous vegetation types. More recently, Shepaschenko et al. (2011) produced a highly detailed land cover/land use dataset for Russia essentially based on GLC 2000 dataset at 1 km resolution combined with vegetation continuous fields (VCF) from MODIS, soil and vegetation databases and different inventories and statistics.
Discussion Paper | Discussion Paper | Discussion Paper | Permafrost Institute SB RAS (A. Fedorov), ESA and JRC to have made available their datasets for this work. Discussion Paper | Discussion Paper | Discussion Paper | Delbart, N. , Kergoat, L. , Le Toan, T. , L'Hermitte, J. , and Picard, G. : Determination of phenological dates in boreal regions using Normalised Difference Water Index, Remote Sens. Environ. , 97, 26-38, 2005. Di Gregorio, A. , Jansen, L. J. M. , and Resources, S. : Land Cover Classification System (LCCS): Classification Concepts and User Manual, 1998. Dufresne, J. -L. , Foujols, M. -A.
are closely tracked by tundra shrub willows, Glob. Change Biol. , 16, 1542-1554, 2010. Frey, K. E. and Smith, L. C. : How well do we know northern land cover? Comparison of four global vegetation and wetland products with a new ground-truth database for West Siberia, Global Biogeochem. Cy. , 21, GB1016, doi:10. 1029/2006GB002706, 2007. Friedl, M. , Sulla-Menashe, D. , Tan, B. , Schneider, A. , Ramankutty, N. , Sibley, A. , and Huang, X. : MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets, Remote Sens. Environ. , 114, 168-182, doi:10. 1016/j. rse. 2009. 08. 016, 2010. Friedl, M. A. , McIver, D. K. , Hodges, J. C. F. , Zhang, X. Y. , Muchoney, D. , Strahler, A. H. , Woodcock, C. E. , Gopal, S. , Schneider, A. , Cooper, A. , Baccini, A. , Gao, F.
Liu et al. [13]  determined the rate laws of toxic metal release during thermal treatment of model wastes. A first-order rate was determined for a mineral matrix, and a second-order rate was determined for a model waste, of composition similar to that of real waste. More recently, Asthana et al. [14]  investigated the behavior of three HM during the combustion of a waste bed traveling on an incinerator grate. They took experimental vaporization kinetics reported by Falcoz et al. [15] , and coupled these laws to a previously developed mathematical model of the combustion of a MSW bed. In this context, two objectives are set in this work. The first aim is to present and validate a local CFD model for predicting HM vaporization in front of experimental data published by Falcoz et al. [15]. The second one is to compare this CFD approach results against those reported previously by Mazza et al.
Numerical solution The governing equations presented in the previous section are solved in 2-D using the CFD software FLUENT 12. 1. 4, which uses the finite volume discretization method. Although it is a 2D simulation, the dimension in the Z-axis is set as the particle's length in order to account for its volume  [44]. The software solves numerically the momentum equation, the species mass transport equation, the local energy equation and the radiation heat transfer equation in the fluid as well as in the porous particle. An extensive set of UDFs subroutines was programmed and complemented to the code so as to represent residual carbon combustion and heavy metal vaporization. The "pressure-based method" was applied to solve the momentum equation due to the low gas velocity. Additionally, second order discretization scheme was used for all scalars in order to obtain more accurate results.
Results and discussion The Cd evolution during a typical MSW incineration process was calculated applying the proposed model, which uses commercial CFD code FLUENT 12. 1. 4 as the basic modeling platform to simulate hydrodynamics, momentum transport and homogenous combustion. In addition, all phenomena related to heterogeneous carbon combustion and heavy metal vaporization were simulated by self developed UDFs. The results of the CFD model are shown hereafter.
, 2007)  at 1 • resolution when forced by the 1960-1991 CRU (http://www. cru. uea. ac. uk/) climatology. The reader is referred to  Ringeval et al. (2012)  for more details. The coarse-resolution effect on the wetland extent simulation through the coupling between ORCHIDEE-WET and TOPMODEL is illustrated in Fig. A3. In particular, the loss of information in subgrid topographic index distribution due to the coarse resolution (Fig. A3d ) leads to substantial difference in the wetland extents when compared to the a posteriori regrid of the 1 • simulated wetland extent (Fig. A3b ). Nevertheless, the global wetland extent simulated at FAMOUS resolution is close to the value given by  Prigent et al. (2007)  dataset at the same resolution (Fig. A3c ). As in  Bohn et al. (2007) , the coupling with TOPMODEL has been extended to compute wetland extents with a negative water table depth value.
For the PI period, the vegetation map from the HYDE 3. 0 database  (Klein Goldewijk et al. , 2007)  is used. The prescribed LGM vegetation comes from  Woillez et al. (2011). Contrary to SDGVM, the LGM vegetation is static during the entire transient D-O simulations in ORCHIDEE-WET. The mean altitude of each grid cell is taken from the orography boundary condition used in FAMOUS and is derived from the ICE-5G data  (Peltier, 2004). The mean altitude is used to derive the surface atmospheric pressure. The altitude is considered constant during the D-O run. Concerning the subgrid topography necessary as input of ORCHIDEE-WET through its coupling with TOPMODEL, we use the current subgrid topography given by HYDRO1k (http://webgis. wr. usgs. gov/globalgis/ metadata qr/metadata/hydro1k. htm) for all the simulated time periods.
This inconsistency between the treatments of hydrology/carbon cycle could be resolved by introducing new wetland plant functional types, which would be restricted to fractional grid cells diagnosed as wetlands using TOPMODEL. Furthermore, we suggest replacing the Q 10 formulation by an Arrhenius-type equation where the effective activation energy for respiration varies inversely with temperature. In SDGVM, we suggest modifying the contribution of the wetland extent versus CH 4 flux densities under PI conditions (see Fig. 4 ) to more closely satisfy available observations. This could be done by scaling the CH 4 flux densities to measurement from sites, and the global wetland extent, e. g. against the value given by  Papa et al. (2010)  dataset. While the present study does not underline a large effect of the imbalance between the two emission components on the simulated change in emissions in comparison to ORCHIDEE, it may not be the case under other climates.
Acknowledgements. We would like to thank  David Beerling  for original development of the SDGVM-CH 4 model. We would like also to warmly thank  Marie-Noelle Woillez  for providing the LGM vegetation map for ORCHIDEE and for her help in the review process. We also thank  Angela V. Gallego-Sala  and  Pierre Friedlingstein  for informative and helpful discussions during the elaboration of this work. Finally, we would like to thank  Joe Melton  for his constructive comments on the manuscript. Edited by:  V. Rath The publication of this article is financed by  CNRS-INSU.
Validation We validated our approach upon the Delicious database that contains social networking, bookmarking, and tagging information. It provides information about the user's friend relationships and the tagging relation information <U, T, R>. The users U are described through their ID. The resources R are described through their ID, URL and title. The tags T are described through their ID and value. We have tested our approach on a set of 100 users. These users have different number of neighbours (varying from 1 to 20). The number of tags, documents and tagging relations is different for each user. This number may roughly vary from 10 to 500 for the tags, from 10 to 500 for the documents, and from 20 to 600 for the tagging relations. For the result of the top-k documents relevant to a query, we have chosen k=10000.
The value of k is chosen according to the largest possible value, as we wanted to test (in this first stage) with the maximum of results achievable (even those with lower scores). Also, the choice of the k value is proportional to the number of resources (69226 URLs) and tags (53388 tags) in the database. Let's take as an example the tag "math" assigned by a user to different resources. This tag has a higher score according to the resource's title "IXL Math", which contains math related thematic, then the resource title "Online Dice Roller", which does not contain any information related to the thematic. So, according to this example, the tag "math" is relevant to the resource "IXL Math". After detecting this relevant tag, we will validate this result by using the user's neighbours. The validation objective is to show if this relevant tag is accurate to the user or not.
Introduction This paper explores how new example of the wider citizen science arena 1 , may help social scientists deal with the challenges of exploiting the growing range and volume media data. We report on work in progress to establish and exploit the potential of crowdsourcing for large-scale social media data curation and analysis. Our aim in this research is to explore the benefits and limitations, and develop ways of maximising the former while minimising the latter. Specifically, our objective is to devise approaches to crowdsourcing in this context that are scalable but do not sacrifice the quality of contributions and investigate how these can be used to improve the performance of computationally-generated annotations. The rapid growth of the Web as a publishing tool, and the recent explosion of social media such as blogs (and micro-blogs such as Twitter) and social networking sites (such as Facebook) presents both an opportunity and a challenge to social researchers.
Data that can shed light habits, opinions and behaviour is available now on a scale never seen before, but this also means it is impossible to analyse using conventional methodologies and tools. We are building COSMOS 2  , a platform providing an integrated suite of tools for harvesting, archiving, analysing and visualising social media data streams for use by social researchers  (Burnap et al. , 2013; Edwards et al. , 2013) , with the capability to link with other kinds of data, e. g. from ONS via open APIs. A critical task in the COSMOS research workflow is annotation of incoming social media streams. We have developed a range of computational tools (language detection, gender assignment, location, sentiment, tension, topic discovery). However, despite the growing sophistication of computational tools for social media analysis, they are not sufficiently reliable to substitute for human expertise.
Hence, what is needed is a way to combine computational tools with human expertise in ways that make the best of their respective strengths  (Procter et al. , 2013a; 2013b). This human expertise is essential for benchmarking and improving the performance of computationally generated annotations and analyses, and curating datasets. If this is to be feasible, then human expertise needs to be readily available and in numbers sufficient to deal with the quantities of data. One way for providing this expertise is through volunteer efforts in the manner of crowdsourcing  (Doan et al. , 2011) , as is now widely exploited under the rubric of citizen science  3  and which projects such as Galaxy Zoo 4  have already demonstrated the potential for in the physical sciences. To test the feasibility of citizen social science for social media analytics we are building a webbased tool, which volunteers will be able to use to access social research collected by COSMOS and perform simple annotation tasks.
These volunteered annotations will then be used to check and improve the quality of the COSMOS computationally-generated annotations. Our approach is modelled on a crowdsourcing facility now being piloted by the BBC to put massive, searchable media archives online using a combination of algorithms and crowdsourcing  (Raimond and Lowis, 2012). BBC Research & Development has built a browsable and searchable online archive, which uses crowdsourcing to validate and improve the quality of computationallygenerated annotations. Registered users can listen to programmes in the archive, add new annotations and vote on the quality of existing annotations. We begin by outlining the ways in which we generate social media annotations computationally. We then outline the BBC pilot and how we plan to build on that to improve the quality of computergenerated annotations for social media dataset curation and analytics.
Gender, location and language To identify the gender of the tweeter, the name the user added to their profile is extracted from the tweet meta-data. The first name is mapped on to the 40k Namen database a database of over 44,000 names from 54 countries around the world with each name classified as male, female, or unisex  (Michael, 2007; Morgan et al. , 2013). One limitation to this approach is that there are clearly more than 44,000 names in use around the world, so crowdsourcing could assist in classifying previously unclassified names. provide information about where the user lives. There are granularity issues with this approach. Some users provide a locality down to area or street level. Some tweets contain GPS metadata. However, our investigations suggest that very few users enable GPS on their tweets (0.
The incremental algorithm is better suited for real-time clustering because it is faster and er of clusters to be specified. However, it is more sensitive to differences in datasets and requires more parameter tuning to obtain good results. In both, each tweet is represented as a list of word counts or Consequently, it is the number and quality of features that determines the quality of the resulting clusters. Clustering performance can be adjusted by tuning parameters for specific corpora and research questions, such as selecting appropriate features, including keyword inclusion and exclusion, top term exclusion, feature weighting and feature number specification. Crowdsourcing could be used to improve clustering performance by ranking cluster quality and harvesting candidate cluster labels.
BBC crowdsourcing pilot BBC Research & Development is running an experiment with s World Service radio archive to demonstrate a way to put massive media archives online using a combination of algorithms and crowdsourcing. 5 We think we can automatically generate metadata for the archive that is good enough to kick-start crowdsourced metadata improvement. The archive has around 50,000 digitised programmes from the World Service English-language radio service  (Raimond and Lewis, 2012)  from over 50 years. It has high-quality audio, but limited metadata. We bootstrap the online archive by generating metadata automatically. We run the audio through a speech-to-text process using CMU Sphinx with the HUB4 acoustic model. This generates quite noisy transcripts, which are not normally readable, but from which we can still extract topics.
For the extracted topics we use linked data entities from DBPedia 6  , so that everything in the system with a unique URI. Using this data, we built a browsable and searchable online archive 7  , which uses crowdsourcing to validate and improve the machine-generated annotations. Registered users can listen to programmes in the archive, add new topics and vote existing topics up or down. We identified a number of potential user groups, including BBC production staff, academic researchers and fans of radio, the World Service, particular programmes and topics. So far, it has been used mainly up by fan communities and some BBC staff. The number of registered users is fairly small (1300 by March 2013), but there has been a significant amount of activity. About half of the registered users are active (i.
Crowdsourcing for social media curation and analysis The BBC crowdsourcing pilot provides a useful template for citizen social science and for how crowdsourcing may be used to improve the quality of computationally-generated annotations. However, there are some important features of the latter that may dictate that we have to employ different solutions. This enables us to define a series of specific objectives for this project. First, given the potential size of social media datasets, to identify ways to select a representative sample for annotation by crowdsourced effort. This sample must be chosen so as to maximise the value of the crowdsourcing for improving the quality of computationally-generated annotations, while keeping the effort required within feasible bounds. Second, this raises the question of how to recruit crowdsourcing contributions to match the volume of data  (Willett et al.
These changes have changed the relationship between the tax agency and the citizens. The citizens get little practice in doing tax as a background for handling any issues that may arise. My initial research question was to design better support for various kinds of citizen autonomy in light of the automated tax services -so that citizens can practice and know their tax issues even when tax is completely automated. To get an understanding of how tax is difficult for the citizens I have colistened to the telephone calls to the tax agency call centre. Co-listening introduced me to a fascinating world of telephone calls, tax advisors, talking citizens, tax issues, tax rules and regulations, online services and large agency databases. I also studied the work practices of the advisors that answer the telephones from the citizens in order to understand how they helped the citizens and supported their autonomy in various ways.
Acknowledgments We would like to acknowledge all members of the  SmartAgriFood  project and in particular the end-user representatives that expressed and shared their thoughts about the developing concept. Peter Rittgen  received a Master of  Science in Computer Science and Computational Linguistics from University Koblenz-Landau, Germany , and a PhD in Economics and  Business Administration from Frankfurt University, Germany. He is currently Full Professor at the  School of Business and IT of the University of Borås ,  Sweden. He  has been doing research on business processes and information systems development since 1997, especially in the areas  Business and IT Co-design & Collaborative Modeling, Business Network Governance and Business Process Simulation & Improvement. Dr. Rittgen  is the Vice-Chair of the AIS Special Interest Group on Modeling and Simulation, SIGMAS (www.
We, therefore, assessed whether the Spitzer 70µm point might be contaminated by background sources, but deem this to be unlikely as the flux varies by less than 4% between apertures of different sizes, whilst the uncertainty on each data point is 5%. There is more reason to question the IRAS point, as there is a greater than 30% variation in flux between the IRAS Point Source Catalogue (IRAS-PSC) and IRAS Faint Source Catalogue (IRAS-FSC), whilst the quoted uncertainties are 10% in IRAS-PSC and 8% in IRAS-FSC. In general, the IRAS-FSC is more reliable, and this is the point that we use, however, it may be that the uncertainty on this point should be increased above that quoted. If this fit is correct and the dust indeed acts like a black body, a temperature of 60 ± 10K would correspond to a radius of 75 +35 -20 AU.
Indeed, one must account both for the design projects done collaboratively by the design team, and for the platform itself. Concepts related to the project include project phases, individual and collective actions, designed artefact, used resources, or team members' roles. Concepts related to the platform include all newly created objects, and all actions allowed by the system. A second objective of OntoTATIN-PIC is to provide software agents with the conceptual resources allowing them to build a model of the on-going projects, which increases their supporting capacities. Because the ontology spreads across a vast domain, we propose to build it using a multi-level and multi-component approach. Thus, OntoTATIN-PIC is structured inline with the well known DOLCE-CORE ontology in its more recent version  (Borgo and Masolo, 2009; Masolo, 2010) , adding modules to describe sub-domains like actions or documents.
To let OntoTATIN-PIC answer the requirements, two versions of it have been designed using the OntoSpec method  (Kassel, 2005) : (i) an informal specification using the formalism of the OntoSpec method, used as a semantic reference for the project team; and (ii) an implementation using the MOSS formalism  (MOSS, 2000) , used by the software agents of the platform. The purpose of this paper is to present and discuss the content of the ontology and its purposes. Section 2 summarizes the ontological framework; Section 3 describes some resources used for defining OntoTATIN-PIC, focusing on virtual objects appearing on the graphics surface and on the actions needed to manipulate them; Section 4 presents how the ontology is exploited; the conclusion presents ongoing work meant to complement the ontology and improve supporting features.