dataesr/scientific-paragraphs-categorization huggingface dataset:
This makes Climate SPHINX the first climate dataset that includes a large number of ensemble members with a stochastic parameterisation at different horizontal resolution; along with other high-resolution simulation campaigns such as UP-SCALE  (Mizielinski et al. , 2014)  or ATHENA  (Kinter III et al. , 2013) , this demonstrates the ability of the climate community to exploit the more recent HPC machines. Details on the tuning procedure (aimed at providing a correct radiation budget in the standard configuration T255) have been presented. Moreover, a comprehensive description of the methodology adopted for the creation of the presentday and future scenario SST and SIC (starting from the HadISST 2. 1. 1 dataset) has been described. A novel method aimed at estimating SST, where SICs have disappeared in future climate simulations, has been introduced.
hpc. cineca. it/thredds/ sphinx. html), where it is possible to browse and directly download Climate SPHINX data. Details on the data accessibility and on the Climate SPHINX project itself are available on the website of the project (http://www. to. isac. cnr. it/sphinx/). The set-up of this infrastructure for data sharing has been possible thanks to DATA SPHINX, an EUDAT data pilot project, which will allow long-term storage and sharing among a wide scientific user community of high-resolution climate model output data. DATA SPHINX aims to build a repository serving the climate change impact modelling community, providing selected variables at high temporal and spatial resolution, with a focus on climate extremes and the hydrological cycle in areas with complex orography. Competing interests.
The authors declare that they have no conflict of interest. Seventh Framework Research Programme. The authors also acknowledge support by the PRIMAVERA project, funded by the European Commission under grant agreement no. 641727 of the Horizon 2020 Research Programme. Jost von Hardenberg acknowledges support from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 641816 (CRESCENDO) and thanks ECMWF for providing computing time in the framework of the special project SPITVONH. Edited by: W. Hazeleger Reviewed by: two anonymous referees.
Acknowledgements. We acknowledge  PRACE  for awarding us access to resource SuperMUC based in Germany at the  Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ)  and to the  Marconi HPC machine at CINECA, Italy. We thank the EUDAT (European Data Infrastructure) project for awarding us a data pilot project and CINECA for providing resources and technical assistance for the storage and distribution of the post-processed model output files. We thank  Peter Bechtold  and  ECMWF  for useful suggestions on the QBO tuning. Paolo Davini  acknowledges the funding from the  European Union 's  Horizon 2020 research and innovation programme COGNAC  under the  European Union Marie Skłodowska-Curie  grant agreement no. 654942. Jost von Hardenberg  and  Paolo Davini  acknowledge support by the  Project of Interest NextDATA  ( MIUR PNR 2011-2013 ).
This was the time when his research work came to be recognised worldwide. He had collected data on over 2000 different counting systems from around Papua New Guinea, a task of huge importance for the region and for the world of mathematics education in general  (Lean, 1994). It was not just a matter of documenting the counting systems, Lean was concerned to note the many ways in which counting was embedded in the local cultures and languages. Far from being a simplified abstract system, counting was revealed by Lean as one of the key mathematical foundational constructs of the societies in Papua New Guinea, and we can claim, everywhere. This ethnographic database still exists thanks to the very difficult task undertaken by Kay Owens in Dubbo, Australia  (Bishop, 1999; Owens, 2001). Allied to the work on counting there was a huge interest in other anthropological data, and later its methods.
To provide evidence on design principles, the same research approach needs to be studied across a range of topics, designers, and teachers-as well as students. That has been the strategic design of the sequence of diagnostic teaching studies; the principles have proved robust. Bowland Maths (2006-10) is a happier story. The goal was to develop 4-lesson units ("case studies") for 14-year old students that showed the power of mathematics through real (or fantasy) world situations. We developed two of these. How risky is life? confronts students with the mismatch between popular ideas of various dangers and the data. In Reducing road accidents the students explore a graphical database of detailed accident report data (Fig. 5 ) in order to advise a town council on what safety measures to take.
Making Sense of Mathematics Achievement in East Asia: Does Culture Really Matter? Frederick K. S. Leung Abstract East Asian students have persistently performed well in recent international comparative studies of mathematics achievement, and I have been offering explanations from the perspective of the influence of the Confucian Heritage Culture (CHC), which is shared by these high performing East Asian countries. In this paper, two challenges to this cultural explanation will be dealt with: whether these East Asian countries really form a group and whether there is a more direct way to study the influence of culture on mathematics achievement. Three studies on secondary analyses of the TIMSS and PISA datasets are presented to support the assertion that the East Asian countries do form a cultural cluster, and preliminary results of a study that looks into the influence of the English and Chinese languages on students' assessment in mathematics are reported.
The data were entered into a spreadsheet in which we captured both demographic information and a range of factors relating to projects, teachers, researchers, forms of collaboration, impact on students, nature of learning, and so on. Overall we had 8500 cells of data. For more detail see  (Robutti et al. , 2016). Analysis of the spreadsheet data led to identification of fundamental themes that could frame the topic of collaboration: • Theme 1-Different contexts and features of mathematics teachers working in collaboration [Addressing RQs 1 and 2] • Theme 2-Theories and methodologies framing the studies [Addressing  RQ 3]  • Theme 3-Outcomes of collaborations [Addressing  RQs 1, 2, 3, 4]. The comparison and contrast between the different sources led to the identification of the following dimensions and related sub-dimensions connected to the themes.
His presentations also raised questions about whose responsibility it is to develop some aspects seen as central to conceptualizations of mathematical literacy (e. g. , interpretation, critical reflection) that seem to fall outside some conceptualizations of mathematics. In Session 2, Klieme presented a plenary input drawing from his experience within the PISA Mathematical Literacy international assessment analysis team. He focused particularly on the somewhat under-emphasised aspect of comparisons of learning environments across participating nations. In his presentation he shared both the ways in which learning environments were conceptualized within the PISA data gathering process (based on 'Opportunity to Learn', 'Teaching Practices' and 'Teaching Quality'), and cross-cultural comparisons that were possible to draw from their dataset.
cambridge. org/dictionary/english/basic) aspect of what it might mean to focus on mathematical literacy in ways that are linked to, but not subsumed by, mathematics. Our sense, as co-chairs of TSG23 is that this direction, with the strands related to task design and learning environments noted above, indicates some promise for debates and further development in the coming years. As a professional community we should furnish mathematical literacy with a more distinct identity and a gaze that contains some aspects that are independent of mathematics, while retaining some overlaps, in order to better serve learners as well as the needs of society at large. Topic Study Group No. 24: History of the Teaching and Learning of Mathematics Fulvia Furinghetti, Alexander Karp, Henrike Allmendinger, Johan Prytz and Harm Jan Smid The Topic Study Group on the history of the teaching and learning of mathematics was formed in 2004 at ICME-10.
Discussions and Reflections The various presentations recognized how members of distinct cultural groups develop unique techniques, methods, and explanations that allow them alternative understandings, comprehensions, new actions, and a transformation of societal norms. Such historical research field has been a foundational area in ethnomathematics and continues to build a database of examples of mathematical thinking in distinct cultural groups. It is evident from the discussion from TSG35 that the theoretical basis of an ethnomathematics program offers a valid alternative to traditional studies of history, philosophy, cognition, and pedagogical aspects of mathematics. Therefore, there is a growing sensitivity to the understanding and comprehension of mathematical ideas, procedures, and practices developed by the members of distinct cultural groups.
This is due primarily to the expansion of studies related to culture, history, anthropology, linguistics and ethnomathematics. Because ethnomathematics offers a broader view of mathematics, including its ideas, notions, procedures, processes, methods, and practices rooted in distinct and diverse cultural environments, this aspect leads to increased evidence of cognitive processes, learning capabilities, and attitudes that influence the learning processes occurring in classrooms. In addition to reflecting on social and political dimensions of ethnomathematics, another important aspect of this program is the possibility for the development of innovative approaches for a dynamic and glocalized society as outlined by D'Ambrosio. The results of our discussions within the TSG35 show that it is important to understand the diverse sociocultural representations and concepts of ethno developed from distinct ideas, procedures, practices, and dimensions of space and time through the relationships between members of cultural groups.
The IMU provides the platform so that platform members can install their own image objects in a database and offer these images to other members for their use. " (Quoted from IMU website) 3. The content of the ICMI archive. Existing as a subset of the IMU Archive, the ICMI Archive comprises in particular five boxes of paper documents (mainly letters) collected during the last half of the 20th century. A large portion of these goes back to the early days of the Archive. Fewer documents have been filed since the early 1990s, when email was becoming the main channel of communication. The archived documents are mostly in English, with a few in French, German, or Italian. Of notable interest are, for instance, documents related to moments of turbulence in the life of the ICMI. The Archive also contains books produced in the context of ICMI activities (ICME proceedings, ICMI Study volumes, etc.
In the first part of the workshop, the structure of IEA data was introduced and then the access paths to data sources, technical documentation, analysis guides, and software tools were shown to participants. Sources and contacts available for support when working with IEA data were mentioned as well. In the second part, the possible uses of data for researchers who focus on the didactics of mathematics were discussed. The methodological challenges and limitations of ILSA data were explained along with solutions about how to address them for data analysis and interpretation, for example, by disclosing appropriate statistical analysis methods. Participants had the chance to study questionnaire and achievement test materials and to develop ideas for suitable research questions that can be answered through the use of TIMSS and TEDS-M databases.
Acknowledgements I would like to thank  Lena Pankow  for her strong and continuing support not only during the congress ICME-13, but in the work for this volume as well. Acknowledgements The author is grateful to and acknowledges her colleagues who have helped her learn across time and have influenced her thinking about the ideas in this paper:  Hyman Bass ,  Esther Enright ,  Susanna Farmer ,  Rebecca Gadd ,  Nicole Garcia ,  Imani Goffney ,  Lauren Hickman ,  Mark Hoover ,  Lindsey Mann ,  Blake Noel ,  Sabrina Salazar ,  Meghan Shaughnessy ,  Rachel Snider ,  Charles Wilkes , and  Amber Willis. The work discussed in this paper has been supported for more than 20 years by grants from the  National Science Foundation  to the author, and her colleagues,  Hyman Bass ,  Heather Hill ,  Mark Hoover , and  Laurie Sleep.
Acknowledgment The authors' work has received funding from the  European Research Council  under the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 )/ERC grant agreement no. 247029 , the  DFG Research Center Matheon , and the the  DFG Collaborative Research Center   TRR 109 "Discretization in Geometry and Dynamics". " What Is Mathematics?" And Why We Should Ask , Where One Should … Acknowledgements This work is based on the research supported by the  South African Chairs Initiative of the Department of Science and Technology , and  National Research Foundation of South Africa  (grant no. 71218 ). Any opinion, finding and conclusion or recommendation expressed in this material is that of the authors and the  National Research Foundation  does not accept any liability in this regard.
Acknowledgements Much of what I have written here tacitly recognizes the support coming from my wife and partner  Jennifer. Her  own study into language education, and especially the international language Esperanto, relates strongly to what I have written here. I also wish to recognize the work of my colleague and friend  Phil Clarkson  with whom I have shared many creative debates both on and off the golf course! Acknowledgements The contribution of all the authors and participants in TSG 39 are deeply acknowledged. Open Access Except where otherwise noted, this chapter is licensed under a Creative Commons Attribution 4. 0 International License. To view a copy of this license, visit http://creativecommons. org/licenses/by/4. 0/. The discussion has helped authors of articles for a special issue to appear in Mathematics Education Research Journal (e.
MobRISK modeling approach MobRISK is a model for assessing and simulating road users' exposure to road flooding due to extreme flash flood events by combining travel-activity simulations following an activity-based approach with hydrometeorological data. The MobRISK architecture includes (i) the simulated environmental changes considered for the study such as road flooding, (ii) an activity-based mobility model reproducing population travel-activity behaviors and (iii) a decision-making model predicting individual responses to weather disruptions. A discrete event simulator (DES) runs the main temporal loop of the simulations. In addition, the user input data is stored in a spatial relational database management system (Fig. 1 ).
Over the last years, several activity-based models have been developed: TRANSIMS  (Smith et al. , 1995) , ALBA-TROSS  (Arentze and Timmermans, 2000) , CEMDAP  (Bhat et al. , 2004) , MATSim  (Balmer et al. , 2006)  and ADAPTS  (Auld and Mohammadian, 2009). Although the mentioned models follow the same activity-based paradigm and provide useful frameworks for modeling individual motilities, they have some differences regarding the activity scheduling approach used, the decision-making process integration and the required input data structure. These differences depend essentially on research purposes and data availability. Whereas the mentioned models are essentially applied for transport forecasting and urban planning, the main objective of MobRISK is to assess population mobility exposure to road flooding, which requires the combination of the travelactivity simulation with hydrometeorological data and road flooding impact data.
Census data and travel-activity survey data are needed in order to assign daily activity programs to the population. Then, by locating the different activity areas, the population mobility is generated when individuals attempt to implement their activity programs. Finally, individual exposure over the flash flood event is defined by the prob-Figure  1. The MobRISK model architecture includes (i) the simulated environmental changes considered for the study such as road flooding, (ii) an activity-based mobility model reproducing population travel-activity behaviors and (iii) a decision-making model predicting individual responses to weather disruptions. A discrete event simulator (DES) runs the main temporal loop of the simulations. In addition, the user input data is stored in a spatial relational database management system.
Data and methods The MobRISK microsimulator was developed to measure the exposure of inhabitants and people working in the Gard administrative area, a region of southern France that has a long flash flood history. This region is characterized by a typical Mediterranean climate with heavy rainfall events during the autumn season  (Delrieu et al. , 2005; Gaume et al. , 2009). In fact, since 1225, the Gard region suffered 506 floods. A total of 66 % of the 353 municipalities experienced at least 10 referenced flood events and some of them were affected more than hundreds of times (CG30, 2016). Between 1316 and 1999,  Antoine et al. (2001)  recorded 27 fatal flood episodes and 277 deaths in Gard. Since 1999, five fatal events added about 30 casualties to the toll. In 2015, nearly 65 % of the businesses and 35 % of the population of the Gard area were located in a flood-prone zone.
3. 2;  Debionne et al. , 2016). Even though the points exposed to flooding may be of three distinct types: river crossings, low accumulation points and river-adjacent points. Low-accumulation points and river-bordering points are much more difficult to identify as they are mostly due to very local settings that are not detectable on the digital terrain model  (Versini et al. , 2010). Therefore, those two types were not considered in Versini's work or in the study presented in this paper. This section provides an overview of the required input data used in the MobRISK model. MobRISK makes maximum use of existing national databases, both geographical and social. SpatiaLiTE, the spatial extension of SQLiTe (a free relational database management system contained in the C programming library), is used extensively for input database building and preprocessing.
Population data Sociodemographic description of the population is based on census data provided by Insee in 2010 (French National Institute of Statistics and Economic Studies). We use especially the INDCVI dataset, which contains the description of sociodemographic characteristics of the individuals, their household composition and household geographical location at the municipality resolution. In addition, we combine MOBPRO (professional mobility) and MOBSCO (student mobility) datasets issued from the Insee complementary exploration of census data. In addition to individual sociodemographics and household characteristics, these datasets describe individuals' commuting patterns. They include information about the municipalities of residence, work and school activities, traveled distances and usual commuting modes of professionals in five categories: (1) no transport; (2) on foot; (3) two-wheel vehicle; (4) car, truck and van; and (5) public transport.
These data are stored into "individ-ual" and "household" tables and every individual is assigned to one household. The description of individual activity schedules is based on travel-activity data, provided by the French National Transport and Travel Survey (ENTD) carried out by Insee from 2007 to 2008. In this survey, the responders were asked to indicate their sociodemographic characteristics (age, gender, professional status, etc. ), their household composition and their mobility description during 1 weekday and 1 weekend. They were instructed to mention the different trips they made during the days of the survey, transport modes, trips' purposes, and time of departure and arrival. Based on these data, the individuals' schedules were retrieved, representing a sequence of activities mentioned by responders as trip purposes.
Ten main activities are proposed in the survey: home, school, working, shopping, medical appointment, administrative procedure, visiting, accompanying persons, leisure and holiday activities. The main objective of using the ENTD data is to assign daily schedules to the individuals described by the census data based on the effects of sociodemographic variables on schedules dissimilarities. The dissimilarities between sched-ules and pairs of sequences are measured by counting the number and type of operations needed to transform one sequence into the other (to match them). The operations considered are insertions, deletions or substitutions of activities. Figure  3  illustrates the matching of a pair of sequences in two different ways regarding the type of operation: (i) using only substitutions by replacing the different elements of one sequence with those in the second one and (ii) using a combination of insertion and deletion operations.
Then, every individual in the study area is connected to an average week schedule and an average weekend schedule based on these attribution rules. The proposed framework is implemented into a free package in R software called TraMineR  (Gabadinho et al. , 2011). Sequence discrepancy analysis methods have been especially used for exploring individual life trajectories  (Studer et al. , 2010; Widmer and Ritscard, 2009). Recent applications of sequence analysis methods on activity schedules and diary data have revealed the advantages of these approaches for capturing the complex structures of activity patterns and providing more accurate schedule classifications  (Lesnard and Kan, 2011; Kim, 2014). 1 A child node is a node directly connected to another node when moving away from the root node of the tree.
Geographical data The next step in preprocessing the data for activity-based mobility modeling consists of locating the different areas where individuals might conduct their activities. Concerning housing activities, census data provide the municipality of residency of every household. In order to have a more precise spatial resolution, we use the RFL data (Revenus Fiscaux localisés -household localized taxes) published by Insee in 2010. The RFL data concerns the number of households and individuals living in them and their sociodemographic description provided at 200 m × 200 m resolution for the entire French territory. Then, each household is located in the grid with respect to household densities by pixel. Concerning work and school activities, MOBPRO and MOBSCO datasets provide the municipalities' codes of both work and school places for workers and students.
In order to enhance the spatial resolution, work and school places are assumed to be mostly located close to municipalities' administrative centers. Therefore, we assign a road node inside a buffer with a radius of 200 m around administrative centers of work and school municipalities to every worker and student. Finally, since we do not have reliable data for the locations of other activities (shopping, leisure, visiting, etc. ), we randomly assign a road node inside a buffer of 500 m around the administrative center of every individual's municipality of residency. The road network sensitivity to flooding is based on the connection of three datasets providing the description of the road and river networks and a list of the road sections susceptible to flooding called road cuts. Road network data is provided in the BD CARTO ® database by the IGN (French National Mapping Agency), describing the road segments that compose the entire French road network by specifying their characteristics (regional, principal or local roads) and their locations in 2010.