dataesr/scientific-paragraphs-categorization huggingface dataset:
The benefits of this procedure can be summarized as follows: i) all the registered values are taken into account, and ii) a previous selection of the repeated values from a context is unnecessary because the system selects the best suited for each moment. Thus, all the laboratory results are respected. We have to be conscious that the available software (OxCal, Calpal, etc. ) calibrates the dates and draws the frequency graphs disregarding their origin, such that each chronologic value is related to a laboratory reference but not to its site of provenance. Consequently, for this software there is no difference if a hundred dates are obtained from only one site or from a hundred different sites; on the contrary, our procedure establishes a dialogue between the date and its archaeological context.
2. Databases are composed of very different archaeological contexts in terms of entity (i. e. major sites vs. temporary camp-sites), functionality (i. e. settlement vs. funerary), knowledge degree (i. e. whole site excavated vs. partial test-pit), and cultural representativeness. The only common nexus for all the considered sites is their temporal convergence. Likewise, every type of human settlement offers different conservation patterns that, logically, interfere in the database construction (for example, dating funerary contexts is usually easier than dating habitation levels).
3. Databases are not independent of several problems inherent to the archaeological discipline, such as taphonomy, post-depositional processes or history of the research. For these reasons, we believe that dating frequency curves never can be seen as a faithful reflect of the actual peopling. In this sense, in this paper we analyse SCDPD focusing on the nature of the known archaeological record -which in our territory is heavily conditioned by the geomorphic evolution typical of soft lithological environments-, especially in the lack of information from taphonomic processes and its influence in the interpretation of prehistoric dynamics.
Therefore, we are obliged to think about the methods and objectives of our discipline. We must be aware that our concern as prehistorians is the understanding of a very remote human ecumene and we must accept that this comprehension cannot be restricted to a mere quantification of ciphers (whether obtained from the tool assemblage or from radiochronological statistics). Our methods imply a continuous exchange of data between all the information sources. Radiochronology is only one of them (a very important one, indeed), but its capacities are very limited if we isolate the samples from their contexts and if our thought is limited to the dated archaeological record. The understanding of archaeological gaps and fills can be envisaged only by means of interdisciplinary research. The typology of the sites that make up the database, their geologic structures, and geomorphologic evolution since the Late Pleistocene and along the Holocene reinforces our arguments on the severe loss of archaeological information.
Acknowledgements Special thanks to  T. A. Surovell  ( University of Wyoming ) for his valuable advice concerning radiocarbon curves and to  A. I. Barbero  and  F. Vadillo  ( University of the Basque Country ) for their help with statistic issues. This work has partially benefitted from financial and technical support provided by the Projects CLIP ( HAR2014-59042-P ) and  DINAMO3  ( CGL2015-69160-R ), provided by the  Spanish Inter-Ministry Commission of Science and Technology (CICYT). Also by  H07-First Dwellers of the Ebro Valley PPVE and Quaternary Paleoevironments PALEOQ (Research Groups of the Government of Aragón and European Social Fund) , the  Research Group of Prehistory of the Government of the Basque Country  ( IT622-13 ) and Ibercaja- CAI Program Research Stays. This paper fits within the research scope of  IUCA (Environmental Sciences Institute of the University of Zaragoza ).
R. Domingo is a  Ramón y Cajal Research Fellow  (ref:  RyC2013-12613 ,  Spanish Ministry of Economy and Innovation). A. Soto was funded by a post-doctoral research fellowship of the  Government of the Basque Country. Authorship contributions: Archaeological data:  A. Alday ,  R. Domingo ,  L. Montes ,  A. Soto  and  P. Utrilla. Geological  and geomorphological data:  J. L. Peña-Monné ,  M. M. Sampietro-Vattuone  and  M. Sebastián. Paleoenvironmental data:  J. Aranbarri  and  P. González-Sampériz. Discussion and final remarks have been agreed by all authors.
Acknowledgements I would like to acknowledge my colleagues  Louis Piccon , architect and Director of the architecture department of the  National Institute of Applied Sciences of Strasbourg ,  Christelle Gress , civil engineer and chief of the specialization of architecture at the  INSA Strasbourg  and  Frank Guêné , architect and Pedagogic Responsible for the content of the AI1 educational cycle, for supporting and giving me the opportunity to integrate the aforementioned pedagogic approach to a concrete pedagogical situation. I am also grateful to  Anke Vrijs , colleague and talented artist, and  Jean-Jacques Virot , architect and art theory expert for their friendship and support. Furthermore, I would like to thank all the participants of the seminar for their participation and for giving me their written permission to diffuse their works.
Objectives and rationale 20 The first objective of ESM-SnowMIP is to assess the current state of the art of snow models on spatial scales ranging from the site scale to global scales. On the site scale, the availability of longer-term high-quality observations at a larger range of sites than in previous intercomparison exercises provides the opportunity for a more comprehensive assessment of the current modelling capacity in different climate settings (see the section on reference site simulations). Similarly, on the global scale, a wealth of new large-scale observational data sets based on advanced remote-sensing techniques allows for 25 more meaningful evaluations than has been possible in the past, as will be described in the relevant section below. In this respect, one particular motivation of ESM-SnowMIP is to profit from the multi-model CMIP6 setting, and, at the same time, make particular snow modelling and observational expertise available to climate modelling groups that in the past have not focused their attention on the representation of snow in their coupled models.
We start with simulations at the plot scale, part of which have already been carried out and are currently being analysed, and then describe global distributed simulations that will be carried out by a subset of the models participating in ESM-SnowMIP, namely global land surface models (LSM) that are also components of an ESM. Finally, we describe 30 planned ESM experiments. atmospheric models as in CMIP6), global products providing vegetation and soil characteristics for model parameters are often contradictory, and global observations of snow properties for evaluation of models (e. g. , for snow density and thermal conductivity) are limited. To gain more insight into the behaviour of models in coupled and uncoupled global snow simulations, ESM-SnowMIP includes experiments using high-quality driving and evaluation data from well-instrumented reference sites.
The challenges of maintaining unattended hydrometeorological measurements in cold and snowy environments and a requirement for multiple years of data limit the number of possible sites, but the range of sites and the numbers of years simulated in ESM-SnowMIP far exceed those in similar experiments for SnowMIP and PILPS2d. Only climate and Earth System Models will be able to perform the global coupled simulations required for CMIP6, but the 20 local uncoupled reference site simulations for ESM-SnowMIP can be performed by a wider range of models at much lower computational expense. Models that have already completed the first round of reference site simulations, listed in Table  3 , include land surface schemes (LSS) of CMIP6 models, sophisticated snow physics models, hydrological models and multiphysics ensemble models.
Downscaled large-scale forcing (LSF-downscaled-Site). Most of the mid-latitude ESM-SnowMIP reference sites were 30 established for snow research in mountainous regions and are at higher elevations than much of the surrounding terrain. Meteorological variables in large-scale forcing datasets, such as the GSWP3 forcing provided at 0. 5° spatial resolution for LS3MIP, would therefore be expected to be biased relative to in situ measurements at the sites even if they were perfect on the grid scale. Col de Porte, for example, is located at an elevation of 1325 m in the French Chartreuse Mountains but lies within a 0. 5° grid cell with an 870 m average elevation. Figure  8a  shows that an FSM simulation for winter 2009-2010 at Col de Porte with GSWP3 driving data gives almost no snow accumulation; this is because temperature is overestimated, total precipitation is underestimated and snowfall is severely underestimated.
Site and grid elevations for Sodankylä, in contrast, only differ by 40 m. The large-scale simulation shown in Figure  8b  is not so strongly influenced by driving data biases. Downscaling is commonly required when using regional climate predictions in hydrological impact studies. An ESM-5 SnowMIP experiment with bias-corrected large-scale driving data will be helpful in using reference site observations and simulations to evaluate the performance of models in large-scale simulations. Simply removing average biases in the GSWP3 driving data, with no attempt to adjust variations on interannual and shorter timescales, greatly improves the SWE simulation for Col de Porte, replacing a massive underestimate with a slight overestimate (Figure  8a ). For Sodankylä, removing the smaller long-term driving data biases has very little effect on the SWE simulation (Figure  8b ).
Large-scale observational data Observation-based estimates of SWE and SCF are required for evaluation of historical mean and model spread from ESM-, AMIP-, and LMIP-type simulations, as well as prescribed historical SWE and SCF as required for specific experiments. For this purpose, we have developed a blended data set of snow analyses from ERA-Interim/Land  (Balsamo et al. 2015) , MERRA  (Rienecker et al. 2011) , MERRA-2  (Reichle et al. 2017) , ERA-Crocus  (Brun et al. 2013) , ESA-GlobSnow  (Takala 20 et al. 2011) , ERA-Brown  (Brown et al. 2003 ) and GLDAS-2  (Rodell et al. 2004 ). These SWE data sets were assessed previously for temporal and spatial consistency (2015), who proposed a climatology derived from a combination of these five datasets. The rationale for using a blended suite of snow analyses is threefold.
Still, the illustrated spread is a useful proxy for observation-based uncertainty (which cannot be determined when a single product is applied for evaluation) and may be used to evaluate the corresponding level of agreement from LS3MIP and ESM-SnowMIP simulations over a similar historical period. A second reason to use a suite of analyses for MIP evaluation is that uncertainty in the fully characterized bias and error of individual data sets provides minimal reason to favor one analysis over another. Furthermore, it has been demonstrated that combinations of products have both lower bias and RMSE than individual products when evaluated over domains with in 5 situ data  (Schwaizer et al. 2016). For this reason, a blended combination of snow analyses will be used for time varying prescribed SWE simulations (see Section 3.
) will have to be adapted accordingly; again, given the diversity of snow modules, it is impossible to define here exactly how this needs to 5 be done in general. Note that these considerations also apply for the land-only simulations of LS3MIP in which soil wetness and SWE are to be prescribed  (van den Hurk et al. 2016). In cases of snow modules where an unequivocal relationship ties surface albedo to SWE, it might be sufficient to run only the albedo scheme with prescribed SWE as input. While a number of snow analyses are available to serve as prescribed SWE, we recommend the  Mudryk et al. (2015)  combined climatology (see section 0) for ESM-SnowMIP simulations. Because biases in individual products are 10 compensated through averaging, this dataset represents an improved reference for model evaluation compared to any single component dataset (Sospedra-Alfonso et al.
15 Tier 2: Fixed land cover (FLC-LSM). Previous studies show that inaccurate representation of vegetation distribution and parameters in LSMs may result in large biases in simulated surface albedo for snow-covered forests  (Essery 2013; Wang et al. 2016). Most current LSMs represent vegetation from a set of Plant Functional Types (PFTs), which are usually derived from global land cover datasets  (Bonan et al. 2002; Poulter et al. 2015). There are large differences among PFTs used in LSMs, which may result from the differences in the land cover datasets, the cross-walking tables used to map land cover 20 datasets into PFTs represented in LSMs, or uncertainties in dynamic PFT simulations  (Hartley et al. 2017; Poulter et al. 2011). In order to separate biases due to differences in vegetation distribution from those due to physical processes in LSMs, we propose an experiment in which models derive their PFTs from the same land cover dataset and using the same crosswalking table.
Several global land cover datasets are available with spatial resolutions ranging from 300m to 1km  (Bontemps et al. 2012). 25  The newly released European Space Agency (ESA) Climate Change Initiative (CCI) land cover datasets are developed specifically to address the needs of the climate modelling community (ESA 2017). The CCI maps include 22 level 1 classes and 15 level 2 sub-classes based on the United Nations Land Cover Classification System, which was identified as a suitable thematic legend and compatible with the PFT concept of most LSMs  (Bontemps et al. 2012). While most previous land cover datasets are for a single year, the CCI datasets are available from 1992-2015 at 300m resolution  (ESA 2017). The finer 30 spatial resolution of 300m (versus 1km) makes it inherently superior for land cover mapping in heterogeneous landscapes where different datasets tend to disagree  (Herold et al.
2008; Fritz et al. 2011). In addition, a cross-walking table to convert the categorical land cover classes to the fractional area of PFTs was provided with the CCI datasets  (Poulter et al. 2015). We thus suggest the use of the CCI 2000 as the common land cover dataset from which to derive PFTs. Different models usually have their own unique set of PFTs. For cases in which both the phenology type and the associated climate zone are considered, the Koeppen-Geiger climate classification can be used as in  Poulter et al. (2015). The simulation period of 1980-2014 matches the LS3MIP Land-Hist offline reference simulations. A comparison of the surface albedo with the reference simulation will isolate the impact of PFT distribution on surface albedo and associated feedbacks in snow-covered areas.
Timeline, data request and data availability The first set of reference site simulations (Ref-Site) have already been carried out and are currently being analysed. The 5 additional site simulations will be carried out in the near future. In 2018 and 2019, the global climate modelling community will be heavily involved in CMIP6. To decrease peak work load on the modelling groups while at the same time optimizing synergies with the CMIP6 activities and in particular with LS3MIP, it was decided to launch the ESM-SnowMIP simulations after the main CMIP6 activities. This has, however, the disadvantage that ESM-SnowMIP global simulation results will not be available for analysis in time for the 6th IPCC assessment report. The tentative schedule for the realization of the various 10 ESM-SnowMIP simulations is indicated in Table  1.
For these global land-only and coupled simulations, the request for output variables is identical to the LS3MIP data request 2 for the respective reference simulations indicated in Table  1 , and the model setup will be very similar, as described in the preceding sections of this paper. This further keeps the additional workload for the ESM-SnowMIP coupled simulations to a minimum. 15 The ESM-SnowMIP site simulation output is sufficiently small to be easily handled via a ftp server at one of the participating institutes (see the dedicated website at https://www. geos. ed. ac. uk/~ressery/ESM-SnowMIP. html). Gridded northern hemisphere SWE data are freely available from the National Snow and Ice Data Center (http://nsidc. org/data/NSIDC-0668). Large-scale meteorological forcing (GSWP3) for the distributed simulations, also used in LS3MIP, will be made available via the ESGF https://esgf-node.
Acknowledgments We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups participating in CMIP5 for producing and making available their model output. For CMIP the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth 25 System Science Portals. Analysis of ESM-SnowMIP reference site simulations is supported by  NERC  grant  NE/P011926/1  IGE and CNRM/CEN are part of  LabEX OSUG@2020  ( ANR10 LABX56 ). Simulations by the SWAP model were supported by the  Russian Science Foundation  (Grant  16-17-10039 , where  Ye. M. Gusev  is the recipient).
Temperatures are warming on a global scale, a phenomenon that likely will affect future crop productivity. Crop growth models are useful tools to predict the likely effects of these global changes on agricultural productivity and to develop strategies to maximize the benefits and minimize the detriments of such changes. However, few such models have been tested at the higher temperatures expected in the future. Therefore, a "Hot Serial Cereal" experiment was conducted on wheat (Triticum aestivum L. ), the world's foremost food and feed crop, in order to obtain a dataset appropriate for testing the high temperature performance of wheat growth models. The wheat (Cereal) was planted serially (Serial) about every six weeks for over two years at Maricopa, Arizona, USA, which experiences the whole range of temperatures at which plants grow on Earth.
In addition, on six planting dates infrared heaters in a T-FACE (temperature free-air controlled enhancement) system (Hot) were deployed over one-third of the plots to warm the wheat by additional target 1. 5°C during daytime and 3. 0°C at night. Achieved average degrees of warming were 1. 3 and 2. 7°C for day and night. Overall, a dataset covering 27 differently treated wheat crops with three replicates each was obtained covering an air temperature range from -2 to 42°C. Herein, the management, soils, weather, physiology, phenology, growth, yield, quality, and other data are presented.
1 OBJECTIVE: The primary objective was to obtain a dataset on the response of wheat to a wide range of temperatures. This was achieved by planting the wheat serially about every six weeks for over two years at Maricopa, Arizona, which experiences temperatures from below freezing to higher than most places on Earth where wheat is grown. In addition, on six of the planting dates, infrared heaters in a temperature free-air controlled enhancement (T-FACE) system were deployed so as to provide a treatment wherein only the crop temperature was varied with respect to reference and control treatments.
The canopy temperature data were processed by dataloggers, which provided 0-10 V control signals to dimmers, which in turn modulated the output of the heaters {[Model FTE-1000 (1000 W, 240 V, 245 mm long x 60 mm wide)] mounted in reflector housings, so as to maintain the canopy temperatures of the Heated plots at 1. 5°C warmer than those of the Reference plots during daytime and 3. 0°C warmer at night. Additional experimental details are presented in  Wall et al. (2011 Wall et al. ( , 2013)) ,  Ottman et al. (2012) ,  White et al. (2011 White et al. ( , 2012)) , and  Kimball et al. (2012 Kimball et al. ( , 2015 Kimball et al. ( , 2016)). Solar radiation, air temperature, and wind speed were measured on a weather mast in the experimental field most of the time starting with the fall, 2007 planting.
For times when field mast data were not available, we utilized data from the AZMET weather station (http://ag. arizona. edu/AZMET/) located about 1 km away. A bias between air temperatures measured at the field and by AZMET was detected when data from times when both were operating were plotted against each other. Consequently, AZMET data were adjusted to the field condition using a regression equation that had solar radiation and wind speed as covariates. To ensure that the dataset could be used to evaluate crop growth models, careful records were kept of agronomic operations, such as tillage; planting and emergence dates; amounts, dates, and types of fertilizer applications; and dates and amounts of irrigations. Dual meters were used for the irrigations that were applied via drip tubing.
PRIOR WHEAT GROWTH MODELLING ACTIVITIES: The HSC dataset has been utilized in some prior model evaluation and inter-comparison activities. The first was by  Grant et al. (2011)  who found good agreement of hourly canopy temperatures as well as with grain yields with the ecosys model. Kimball et al. (2015)  used the data to validate a model predicting the infrared heater requirements needed to conduct T-FACE experiments. However, the largest effort was done by the Wheat Team (http://www. agmip. org/wheat/) of the Agricultural Model Inter-comparison and Improvement Project (AGMIP,  Rosenzweig et al. , 2013) , who did a major inter-comparison study among 30 wheat models with the HSC data  (Asseng et al. , 2015) , as well as predicting likely impacts of global warming on global wheat productivity.
The Wheat Team also used the HSC data to improve the accuracy of their responses to high temperature. With 15 of such improved models,  Mariorano et al. (2016)  showed that both accuracy and precision were indeed improved when tested against several independent datasets from around the world. To evaluate whether simulation of canopy temperature improves the ability of simulate heat stress impacts, nine wheat models were tested against the HSC dataset by  Webber et al. (2017). The model output and the HSC data contributing to  Asseng et al. (2015)  and  Maiorano et al. (2017)  are presented in a companion paper  (Martre et al. , 2018).
Overview of the main HSC dataset files. of selected plots at six depths at 15min intervals from March 2007 through July 2008. Soil Moisture 2008Aug-2009May. ods Soil water content of selected plots at six depths at 15min intervals from August 2008 through May 2009. This work was supported by the U. S. Department of Agriculture, Agricultural Research Service and the University of Arizona, Tucson, Arizona. We acknowledge the helpful cooperation of Dr. Robert Roth and his staff at the University of Arizona, Maricopa Agricultural Center, Maricopa, Arizona, USA. Technical assistance by Charles Blackshear, Matthew Conley, Steven Farnsworth, Justin Laughridge, Laura Olivieri, and Zahra Troeh is appreciated. We also thank Dr. Larbi Rhazi (Institut Polytechnique LaSalle Beauvais, France) for his help with the analysis of protein and starch polymer size distributions, and David Alvarez and Annie Faye (UMR GDEC, INRA Clermont-Ferrand, France) for their technical help with other grain analyses.