dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion Rankcluster is the first R package dedicated to ranking data, allowing modeling and cluster analysis for multivariate partial ranking data. Available on the CRAN website, this package is simple of use with its main function, rankclust(), having only one mandatory argument, the ranking dataset. By default a single homogeneous multivariate ISR model is fitted to the data, and mentioning the number of desired clusters leads to perform a cluster analysis, with selection of the number of clusters if several numbers are given. The analysis of two real datasets presented in this paper allows to illustrate the possibilities of the package, and also constitutes a user guide for the practitioners.
We use first field-based observations of precipitation and river isotopic chemistry from a three-year study (2009e2011) in rainforest and nearby savannah in central Guyana at the northern rim of the Amazon rainforest to establish the quality of modelled or remotely-sensed datasets. Our 3 years of data capture a reduced rainfall regime in 2009 and an extended wet season in 2010, in contrast to the widely documented Amazonian floods in 2009 and droughts in 2010. Comparisons of observed precipitation with satellite derived TRMM and ECMWF ERA-Interim reanalysis precipitation show that both of these data sets capture the general pattern of seasonality, but substantially underestimate rainfall amounts in the primary wet season (by up to 50% and 72% respectively). The TRMM dataset is generally better at characterising the main dry season from September to December but the ERA-Interim model can overestimate precipitation in the dry season by up to 175%.
Our new data on isotopic chemistry of river waters show that d 2 H/d 18 O values in this region are broadly consistent with interpolated global datasets of modelled precipitation isotopic signatures. The dominance of isotopically lighter water derived from the rains of the ITCZ during the wet season provides evidence of the close coupling of water chemistry of headwater rivers on the northern rim of Amazonia to the positioning of the ITCZ over the region. Our results highlight the challenge in understanding and representing local scale hydrological and biogeochemical characteristics using regional scale model data. We argue that combining point and local scale field data with larger scale model data is necessary to progress towards a comprehensive understanding of climateehydrology interactions in Amazonia.
This lack of knowledge is mainly because of the uneven coverage of field study sites, which do not provide an adequate representation of larger areas  (Batistella et al. , 2009). Logistical and other constraints in remote tropical rainforests commonly result in localized, widely scattered datasets, requiring interpolation or upscaling to understand regional patterns, often based on few ground-based datasets. In comparison with the central Amazon rainforest, the climate and hydrology of the northern Amazonia rainforests of the Guiana Shield region (incorporating Guyana, Suriname, and French Guiana, collectively known as 'the Guianas', with parts of southern Venezuela and northern Brazil) are even less well known. This region however, has one of the lowest deforestation rates, and incorporates approximately 15% of the forest in South America  (Hammond, 2005).
As deforestation trends continue elsewhere, the Guiana Shield will increasingly represent a greater share of the remaining intact forest cover  (Hammond, 2005) , and may provide a crucial link in maintaining moisture transfer from the Caribbean into central Amazonia  (Marengo, 2009). There is also uncertainty in the regional climate understanding, reflected by lack of consensus in Global Climate Model future scenarios for the equatorial South American region, and in the Guiana Shield region in particular (e. g. McSweeney et al. , 2010) , emphasising the need for more smallscale studies that link local processes with larger climate dynamics. Observation based interpolated climate datasets of the region do not allow an accurate representation of the climate of this area, because they incorporate few, if any records from Guyana (e.
g. NOAA Climate Prediction Centre unified gauge-based analysis of global daily precipitation datasets for 1970e2005 (http://www. cpc. ncep. noaa. gov/products/precip/realtime/GIS/SA/SA-precip. shtml, see also  Chen et al. (2008) ). Other datasets cover only the Amazon or Brazil (e. g. Shi et al. , 2000; Silva et al. , 2007) , thereby missing the Guiana Shield area altogether. Global datasets (e. g. CMAP,  Xie and Arkin, 1997; GPCP, Adler et al. , 2003)  combine similar sets of limited observations with satellite or model outputs only at a coarse grid-scale (2. 5 Â 2. 5 latitudeelongitude grids). A recent study of the precipitation and temperature regimes of the Guianas  (Bovolo et al. , 2012)  has shown that reanalysis datasets (specifically ECMWF ERA-40, with a 1. 125 grid) provide a reasonably good representation of monthly, annual and averaged seasonal temperature across the region when compared with observations.
However, the reanalysis data is limited in representing precipitation amounts  (Bovolo et al. , 2012). The newer ECMWF ERA-Interim dataset offers a better representation of the water cycle due to revisions in the humidity analysis and bias correction for radiance data  (Dee et al. , 2011)  at an increased 0. 75 resolution. In a comparison of descriptions of the hydro-climatology of the Amazon basin between ERA-Interim and ERA-40 data and local observations,  Betts et al. (2009)  found that ERA-Interim remedied substantial drawbacks in the ERA-40 precipitation climatology of the Amazon basin but still suffered from a general dry bias, specifically in the amplitude of the seasonal precipitation cycle, in comparison with local observations. A better spatial resolution is achieved by TRMM (Tropical Rainfall Measuring Mission) satellite observations (grid scale of 0.
, 2009; Vuille et al. , 2003). Stable water isotopes of inland waters have also been used successfully to determine water sources, assess the contribution of overland-and base-flows to stream flow, and establish potential rates of evaporation  (Martinelli et al. , 1996; Saylor et al. , 2009; Speed et al. , 2011; Strauch et al. , 2006; Wassenaar et al. , 2011). However, there are few observational data available for this region in the International Atomic Energy Agency-Global Network of Isotopes in Precipitation or Rivers (IAEA-GNIP/GNIR) databases, and most of the available records are very short  (Vuille et al. , 2003). To compensate for the lack of field-based data in the tropics, various interpolation models have been developed and used to identify monsoonal, altitudinal and seasonal dynamics  (Bowen and Revenaugh, 2003; Gonfiantini et al.
Modelled precipitation Reanalysis datasets offer physically coherent and realistic atmospheric parameters representing available historical observations at a global scale. The ECMWF ERA-Interim reanalysis is derived from modified operational weather forecast and analysis models, run in a retrospective manner  (Dee et al. , 2011). The precipitation outputs come as a forecast product from a 12-h analysis cycle, where available observations are combined with prior information from the forecast model to estimate the evolving state of the global atmosphere and at the earth's surface. Although not directly observed, precipitation is constrained by the temperature and humidity information derived from the assimilated observations used to initialise the forecast. ERA-Interim daily precipitation time series data were downloaded from the ECMWF data server for the period 1st Jan 1979e31st Dec 2011 (http://data-portal.
2 ). System for Data Analysis, Visualization, and Electronic Retrieval (WISER) at http://www-naweb. iaea. org/napc/ih/IHS_resources_ isohis. html. This dataset has been used to produce mean monthly d 2 H and d 18 O of precipitation maps for the globe  (Bowen et al. , 2005). Mean monthly d 2 H and d 18 O precipitation maps were downloaded from http://wateriso. eas. purdue. edu/waterisotopes for the grid cell that covers the Burro Burro catchment.
Modelled precipitation Tropical rainforests experience high amounts of convective rainstorms which can lead to high spatial variability. Therefore comparisons of point rain-gauge observations with modelled grid cells, which are representative of an averaged area, are not likely to give good results unless the rain gauge location is representative of conditions over a wider area. To assess the spatial representation of the new AWS dataset, a comparison was made against a tippingbucket raingauge at a similar elevation, located within the forest approximately 9 km away. The datasets show that although precipitation between gauges vary spatially on a daily timescale, rainfall over a monthly timescale is similar at both locations (R 2 ¼ 0. 88 based on a 17 month time-series between 2010 and 2012).
To the north of the raingauge, there are some small mountainous areas that suggest the ERA-Interim grid cell has a higher average elevation than at the raingauge location. It would therefore be expected that the ERA-Interim dataset may slightly overestimate rainfall in this grid-cell. Changing of the prevailing winds from the north-east, east or south-east, depending on the location of the ITCZ  (Bovolo et al. , 2012) , may also cause a rain shadow of the grid-cell but these local effects should be captured by the raingauge. The TRMM and ERA-Interim data capture a similar precipitation pattern to the observed 2009e2011 data sets with increased precipitation from May to August associated with the primary wet season. However, both models significantly underestimate the precipitation recorded at the forest and savannah sites, especially in the primary wet season; and the ERA-Interim overestimates rainfall slightly in the secondary wet season (Fig.
3 ). In the primary wet season of the 2009 'dry' year, TRMM underestimates observed rainfall by 5e25% for the rainforest and 8e48% in the savannah. In the 2010 and 2011 'wet' years this discrepancy is more pronounced, with rainfall underestimated by 7e43%, and 5e50% in the rainforest and savannah, respectively. ERA-Interim fares worse, underestimating rainfall in the primary wet season by 22e72%, 33e55% and 8e59% in 2009, 2010 and 2011, respectively. Both datasets also overestimate precipitation in the dry season from September to December but the ERA-Interim dataset is most pronounced overestimating observed precipitation by 6e175%. In total, over the 3 years, ERA-Interim rainfall underestimates rainfall by 53% in the forest and 58% in the savannah, whilst TRMM underestimates rainfall by 43% in the forest and 56% in the savannah.
River geochemistry Water isotope analyses were conducted on 40 river water samples (Table  2 ). The d 2 H and d 18 O values are plotted in Fig. 4a  with the Global Meteoric Water Line (GMWL), which represents the global average relationship between d 2 H and d 18 O in terrestrial waters across a latitudinal gradient (GMWL,  Craig, 1961; Rozanski et al. , 1993). Rainwater that has a deuterium excess can be indicative of either, effects of 'rainout' from orographic and convective rainfall, the 'amount effect' from increased rainfall or distance from the coast  (Dansgaard, 1964). The overall ranges of the d 2 H and d 18 O values are À47 to À5. 9& and À7. 6 to À2. 1&, respectively. Generally, the dataset plots along or near the GMWL with a correlation coefficient of 0. 99 and a slope of 7. 33 (see Fig.
Discussion The point-scale seasonal pattern of rainfall observed in the forest is broadly consistent with results from grid-scale TRMM and reanalysis data for both ERA-Interim (this study) and ERA-40  (Bovolo et al. , 2012). However, the actual amounts of precipitation are greatly underestimated in the peak wet season (both datasets) and overestimated in the dry season (ERA-Interim). ERA-Interim uses temperature, humidity and radiance data to forecast precipitation, which is therefore only indirectly constrained by observations  (Dee and Uppala, 2009; Dee et al. , 2011; Simmons et al. , 2010). In the Amazonia and Guiana region, approximations used in the model's representation of moist processes are not well constrained because of relatively few observations. Consequently, this causes difficulty in modelling convection at coarse spatial scales  (Kendon et al.
Previous studies  (de Angelis et al. , 2004; Franchito et al. , 2009)  focussing specifically on the "precipitation radar" component of TRMM found that the satellite data product captures important characteristics of rainfall over the Amazon, but rainfall regimes in less humid climates were less well represented. For combined (multi-sensor) rainfall products in an earlier version (v5) of TRMM,  Adler et al. (2000)  found a dry bias compared to the Global Precipitation Climatology Project (GPCP) dataset. The TRMM family of spatial precipitation data products was designed primarily to study the convective precipitation dominant in the tropics and sub-tropics. The incorporation of imagery from geostationary satellites (e. g. GOES) to estimate rates of precipitation from observations of cloud top temperatures is particularly suited to convective precipitation rather than orographic and (large-scale) stratiform precipitation.
Residence times of water within the Burro Burro catchment The peak wet and dry conditions of the region from 2010 to 2011 are captured in the d 2 H and d 18 O records of river water (Fig. 4 ) with the most distinct differences between the extreme dry and wet seasons of 2010. The riverine signatures show a notable seasonal isotopic separation that is broadly consistent with the rainfall pattern modelled for the Iwokrama region using the interpolated GNIP datasets by  Bowen et al. (2005)  (Fig. 6 ). TRMM images confirm that the spatial positioning of the ITCZ was likely to be responsible for the isotopically lighter water received by river catchments during the peak wet season (Fig. 6 ). Although the river d 2 H and d 18 O datasets are much more variable, this is probably a result of localised effects such as evaporation and a mixing of surficial runoff and baseflow waters.
Given the observed seasonal pattern in riverine d 2 H and d 18 O signatures, it suggests that the 'amount effect'  (Dansgaard, 1964) , which brings isotopically lighter water associated with tropical rains of the ITCZ, is the controlling factor in determining wet season isotopic signatures of river water in the region. This is consistent with other studies from Amazonia  (Martinelli et al. , 1996; Matsui et al. , 1983; Rozanski and Araguás-Araguás, 1995; Salati et al. , 1979). A notable exception exists for the uppermost section of the Burro Burro River during the wet season of 2010 (close to the savannahrainforest transition) that exhibits a similar isotopic signature to the dry season (upper right hollow square in Fig. 4a ). This suggests that the rivers draining the upper catchment of the Burro Burro River were not influenced by the ITCZ rains from the north at this time, which is difficult to discern from TRMM datasets alone.
, 2012). However, monthly precipitation totals are not well characterized by either of these models, with the TRMM and ERA-Interim models underestimating total monthly rainfall by up to 50% and 72% respectively during the wet seasons. The TRMM dataset is generally better at characterising the main dry season from September to December but the ERA-Interim model can overestimate precipitation during the dry season by up to 175%. Although local effects, including topographical variability at the sub-grid scale and the random nature of localised convective storms, may account for some discrepancies, there appear to be systematic biases in the modelled data. The first new records of river discharge and isotopic geochemistry within headwater catchments in the forest reflect the rainfall patterns of the area, with the d 2 H/d 18 O isotopic values of the river being broadly consistent with the GNIP rainfall interpolation model  (Bowen et al.
, 2005). The position of the Inter-Tropical Convergence Zone (ITCZ) is shown to influence the isotopic composition of river water, with isotopically lighter water derived from the tropical rainfall of the ITCZ dominating during the wet season. This dataset from central Guyana provides new field hydroclimate observations in a data-scarce region which may become increasingly important as one of the remaining extensive areas of intact tropical forest of Amazonia, and which may also play a critical regional role in transferring moisture from the Caribbean to the interior of South America. Our comparison of field data against global models of rainfall and geochemistry highlight the existing uncertainties of modelled data that should be taken into account if used for the interpretation of hydrological cycling within large river systems, such as Amazonia.
Acknowledgements This research was supported by  Inter-American Development Bank (IADB)  Grant  ATN/MC-11548-GY , project  GY-T1069-Measurement of Climate Change Impacts and Eco-System Services at Iwokrama. RP acknowledges support from  NERC  grant  NE/ H525254/1 , TW acknowledges support from the  Royal Society Wolfson Research Merit Award , CIB also acknowledges support from the  IADB. We thank the whole  Iwokrama  staff and support team, specifically  Angela FranklineLewis  ( Monitoring Manger ), and all the rangers, especially  Floria Francis ,  Glenny King ,  Russian Dorrick  for field support, and  Ken Rodney , forestry manager. We also thank  Robert Posey  and  Jurian Hoogewerff  for help with stable hydrogen and oxygen isotope measurements.
g. , capacity to learn, robustness to noise or changing rules) be assessed by rigorous eval-uation procedures, as it is defined for example in the domain of machine learning. In this case also an easy access must be proposed both to the inner circuitry of the models and to the specification of the external world. With in mind this double goal of offering convenient tools to both scientific communities, we report in this paper the specifications that we have elaborated and present the corresponding software platform that we call VirtualEnaction.
Symbolic vision is also available, as a list of blocks visible in its visual field, which access to the block characteristics. An adaptation of the minecraft open game software yields the proper answer to this wish-list and the so called virtualenaction is an open-source freelicense implementation of these specifications. Each user buys a end-user low-cost mojang license (< 20$) for minecraft, while virtualenaction is free of use under a CeCILL-C license. Fully-documented scripts facilitate the installation of the software bundle under Linux OS. The bot is implemented in either C/C++, or possibly in Python (via an existing swig wrapper) or other computer languages. It uses a simple API, as described in Fig. 2. Furthermore, in order to both observe in slow-down real-time the bot behav- ior and interact with the digital experiment, a graphic user interface is available as described in Fig.
NEUROSCIENCE APPLICATION Let us now discuss how this setup constitutes a step towards integrative neuroscience digital experimentation. First of all, let us compare this project with complementary connected projects. The AnimatLab is a software platform allowing to simulate embodiment (bio-mechanical simulation of a body) allowing investigate the relation between brain and body  (Cofer et al. , 2010). Furthermore, it proposes a neural network architecture for the implementation of cognitive function. On the contrary, the present framework has a rather limited description of the embodiment, but a much larger set of possible interactions with the environment. A step further, not only artificial based neural network models are usable in VirtualEnaction, whereas the interface with any existing neuroscience simulation tool (e.
g. , python based neural simulators, see  (Brette et al. , 2007; Davison et al. , 2008) ) is straightforward. This feature is essential, since we must simulate the system at different modeling scale, as developed now. The Morse is a generic simulator for academic robotics, with realistic 3D simulation of small to large environments, allowing complete integration with any simulation tools. It outperforms concurrent systems like Webot. The interest of Vir-tualEnaction with respect to Morse is twofold: Since we target integrative cognitive tasks of survival which is exactly what happens with the Minecraft environment, using this specialized product is far simpler and somehow more demonstrative. In term of per-formances, as being less sophisticated (using a simplified 3D rendering, while Morse has all 3D capabilities) and being agnostic in terms of programming languages (i.
e. , allowing fast C/C++ implementation of user modules, whereas Morse is limited to Python scripts) the VirtualEnaction platform is a priori expected to be more efficient in terms of CPU usage. However, with a larger humanpower all what has been developed within VirtualEnaction could have been developed in Morse. The main application regarding neuroscience is to test cognitive computational models in realistic conditions. Very simply, a behavioral experiment is performed on an animal model or on human. Usually with a training phase, the measurement phase and the data analysis. In order to formalize the obtained result a computational model is proposed that explain the data, and may also have prediction regarding other falsifiable future experiments. The present software and methodological tool allow us to propose to enhance this very general paradigm in the following directions: • Test the model prediction for several others experimental conditions or model parameter ranges : The idea is to reproduce the experimental setup in this virtual environment (e.
B COMPUTER IMPLEMENTATION This experimental virtualenaction setup is fully documented both at the user (Denoyelle and  Pouget, 2014)  and developer  (Pouget and Denoyelle, 2014)  levels. The user view of the computer implementation is oversimple as illustrated in Fig. 4 , though the underlying computer development is not that trivial as sketched out in Fig. 5. It runs a bukkit server in the 1. 4. 5 version and is designed as a set of plugin for this server, with minimal changes in the original code. This design choice was optimal in terms of middle-ware development effort and fi-nal code stability. At the server level, two plugins have been designed. The VirtualEnaction plugin allows us to replace usual player mouse and keyboard interactions with the game-play by a programmable API. The Characteristics plugin allows us to implement blocks and entities additional characteristics such as temperature, hardness or smell.
MATERIALS AND METHODS Participants Twelve healthy right-handed women [mean age AE SD: 24. 14 AE 3. 06 years; range: 21. 41-34. 66 years] participated in the study. Participants were further screened for the absence of eating disorders using the Bulimic Inventory Test, Edinburgh (BITE)  (Henderson and Freeman, 1987). The mean BITE score was 4. 00 AE 3. 22 (range: 0-9). The subjects tested here were part of a wider study including female patients with eating disorders. The study was conducted in accordance with the Declaration of Helsinki. See Supplementary Data for additional details.