dataesr/scientific-paragraphs-categorization huggingface dataset:
Replicability The data and R codes used for the LC, LL, CoDA, CoDA-C and MEM models are publicly available at https://github. com/mpascariu/MortalityForecast. The DG model data and R codes are available in the MortalityGaps R package  (Pascariu 2018)  and at https://github. com/mpascariu/MortalityGaps.
If "LOW BAT" is indicated, the battery must be replaced before starting the stimulation. Display. Data are transferred between the microcontroller and the display by the microcontroller PORTD (8-bit). Three PORTC pins are used for the transfer control. The resistor R11 limits the current through the backlight display. Keyboard. Button-press grounds the bit 1, 2 or 3 of the PORTB, which is configured in pull up. The CX PUSH capacitors clean up switch bounces. Actions on the keyboard are detected by the NAND IC3C, which is connected to RB0. RB0 is configured as an external interrupt. Buzzer. The microcontroller pin RB5 drives the buzzer via the NPN transistor T1. The resistor R BUZZ limits the base current. Serial port. The communication between the stimulator and the computer is assumed by the Universal Synchronous/Asynchronous Receiver/Transmitter (USART) module of the microcontroller.
Programming of the microcontroller The stimulator firmware is written in μC with the MPLAB X IDE software and compiled using the MPLAB XC8 C Compiler (Microchip Technology, Arizona, USA). In this section, we quickly describe the structure and key elements of the firmware. For further details, the source code is available on www. github. com/Open-tES. Program structure. The program consists of the main source file and two libraries. One library is dedicated to controlling the display while the second is focused on diverse functions. The configuration of the microcontroller is described in the first part of the main file. In the second part, the variables used by the functions \main" and \interrupt" are declared. Then, the interrupt function and the main function with an infinity loop are written.
Characterization Response time and step response. The paradigm consists of a switching between two setpoint values of 0 and 250 μA. We proceeded to 100 consecutive measures of rising edges. We defined the response time as a period between the request sending (from computer to stimulator) and the beginning of the current rising (over 0. 1 μA). For the step response, we measured delays to obtain 99% and 99. 9% of the set-point. The current was measured with a sample rate of 1 KHz (data acquisition system: MP36, BIOPAC Systems Inc. , CA, USA). A computer program written in Matlab (MathWorks, USA) controlled the stimulator and synced the data acquisition. Accuracy and precision. Precision and accuracy were assessed with measures for 26 current values ranging from 0 to 250 μA in increments of 10 μA.
However, the main result concern the credibility of the simulation which may be observed with Figure or on the video of the simulation (at the following address: https://github. com/mathieuBourgais/ExempleThese). Agents show various behaviors in almost similar situations which is explained by the various personalities as in real life where two persons in the same situation will not obviously make the same decision based on their personalities. It is also possible to observe behavior patterns: some people are leaving early because they perceived the fire while others are still dancing at the beginning of the simulation; the first persons evacuating are joined by people between them and the exit thanks to emotional contagion; people still in the nightclub lately are trapped in smoke and have to follow the signs which, in this particular case, are pointing to the toilets, leading to the vast majority of death in the simulations; people getting out of smoke and perceiving the exit change their action and flee from the nightclub.
Observations Over Europe, we make use of high spatial-resolution gridded observational datasets that include the highest station density (Fig. S1  in the Supplement): SAFRAN-V2 (France;  Vidal et al. , 2010) , UKCPobs (British Isles;  Perry et al. , 2009) , ALPS-EURO4M (Alps;  Isotta et al. , 2014) , and CARPAT-CLIM (Carpathian region;  Szalai et al. , 2013). To cover the Iberian Peninsula, we combine Spain02 v2  (Herrera et al. , 2012) and PT02 v2 (Belo-Pereira et al. , 2011). For other regions, we considered E-OBS v17  (Cornes et al. , 2018). E-OBS uses the complete observational stations network for Scandinavia, the Netherlands, and Germany (Gerard van der Schrier, personal communication, September 2019). E-OBS is therefore expected to be of good quality over these regions. For the remaining regions, such as the Mediterranean region and eastern Europe, we also make use of E-OBS, although the quality is most likely lower there  (Prein and Gobiet, 2017).
Domains We divide the European domain into subregions according to the areas covered by the observational datasets (Fig. S1 ). Over the subregions covered by E-OBS, we consider the Table  3. Information about the observational datasets used in this study (refer to Fig. S1  for the coverage). The time period refers to that considered in this study, not the available period of each observational datasets. 1971-2005 1971-2005 1971-2005 1971-2003 1971-2005 1971-2005  PRUDENCE regions  (Christensen and Christensen, 2007). Observations SAFRAN UKCPobs CARPATCLIM SPAIN02 v2 + PT02 v2 ALPS-EURO4M E-OBS v17 Throughout the paper, we therefore use AL for the Alps, BI for the British Isles, FR for France, CA for the Carpathians, CE for central Europe, IP for the Iberian Peninsula, MD for the Mediterranean Basin, NEE for northeast Europe, and SC for Scandinavia.
These results are summarized in a pie plot (right-hand side of Fig. 1 ) for all seasons (December-January-February, DJF; March-April-May, MAM; June-July-August, JJA; September-October-November; SON). The intercomparison of the model ensembles is performed as follows. 1. All datasets are regridded on the common EUR-44 rotated-pole grid, using a first-order conservative remapping. Then the precipitation data are pooled from each region and season. This step is repeated for every model and observational dataset. 2. On the histograms, the ensemble median is shown for each bin (thick line in Fig. 1 ) along with the interquartile range (shaded colours in Fig. 1 ). For the observation, the median and interquartile range are based on inter-annual variability. 3. The significance of the difference between the two PRI-MAVERA and CORDEX ensembles is calculated using a Welch's t test (unequal variance t test) for each bin based on inter-member spread (the p value is plotted with grey crosses in Fig.
1 ). We apply a 10 % threshold to each bin to test whether the two ensembles are significantly different (p value < 0. 1). 4. We group the bins into three intensity precipitation intervals defined on the observational datasets (Fig. 1 ): low (bins accounting for the lowest 40 % of mean precipitation), moderate (bins accounting for the next 50 % of mean precipitation: 40 %-90 %), and high (bins accounting for highest 10 % of mean precipitation). For each interval, we evaluate the percentage of bins over which the ensembles differ. 5. If the ensembles differ by more than 70 % over that interval, the section of the pie corresponding to the season, region, and precipitation interval is coloured (Fig. 1 , right). 6. If the ensembles differ by more than 70 %, we determine which one is less significantly different from the observations using the same metric between the observational spread (based on inter-annual variability) and the ensemble spreads (based on inter-member spread).
Observational uncertainty Prein and Gobiet (2017) advised readers to consider as many observational datasets as possible for regional analyses. Most datasets, however, are available either at a much coarser horizontal grid spacing than 50 km and therefore cannot be used for evaluating the ensembles at such a resolution or they are not available at daily timescales. We have done a test using GPCP (Global Precipitation Climatology Project) v2 available daily at 1 • horizontal resolution. The distribution shows almost no intense precipitation over most regions and in most seasons (not shown). The observational datasets used in this study are available at fine horizontal grid spacings (5-20 km), and they contain a very dense station network, which minimizes the effect of precipitation undersampling  (Prein and Gobiet, 2017).
This occurs in all gridded precipitation datasets that are purely based on gauges and results in dampening extreme precipitation peaks by smoothing the heavy rainfall and redistributing it over larger regions  (Haylock et al. , 2008; Hofstra et al. , 2009; Prein and Gobiet, 2017). To represent the spread in observations, we use the inter-annual variability of the gridded observation datasets. However, the heaviest precipitation interval is likely underestimated in our analyses. To overcome this problem, we redid some of our analyses by assuming a mean estimate of the undercatch error of 20 % over all regions and seasons, a method similar to  Kotlarski et al. (2014)  and  Rajczak and Schär (2017). In such a method, all observations are scaled by a factor of 1. 2 over all grid points and over the entire time series, which gives a rough estimate of observational uncertainties.
There are some precipitation intervals, seasons, and regions for which the two ensembles significantly differ. A major difference between the two ensembles is found for heavy precipitation in most seasons and regions. PRIMAV-ERA has less heavy precipitation than EURO-CORDEX and tends to be closer to observations. However, gridded observational datasets likely suffer from an underestimation of heavy precipitation, in which case EURO-CORDEX is favourable (Fig. S7 ). European summer precipitation is mostly driven by local convective precipitation, which is not explicitly simulated in state-of-the-art EURO-CORDEX RCMs and GCMs. At such resolutions (at best 12 km horizontal grid spacing), convection is parameterized. In RCMs, such parameters are commonly set by expert tuning or objective calibration to simulate a mean climate as close as possible to observations over the region of interest in hindcast simulations (using reanalysis boundary forcings; e.
PRIMAVERA and EURO-CORDEX (EUR-11 or EUR-44) should therefore be considered equally credible, depending on the user's needs. For studies at the local scale or over complex orography (such as the Alps), a higher-resolution model dataset, such as EUR-11, gives more detailed spatial information (e. g. Kotlarski et al. , 2014; Prein et al. , 2015). We have only focused on present-day simulations. Assessing future climate projections between the two ensembles may be more difficult because the results would depend on other parameters independent of the models themselves, such as the lack of a common protocol (e. g greenhouse gases and aerosols forcings) between RCMs and GCMs. The impact of aerosol forcings on the climate projections, which differ in GCMs and RCMs, is currently being investigated  (Boé et al.
, 2020; Gutiérrez et al. , 2020). We have limited our study to Europe, which has the advantage of having a large RCM ensemble. We showed that GCMs have the potential of providing, at a global scale, regional climate information which is on a par with CORDEX datasets. Therefore, in the future, this work should be extended to other regions of the world, where CORDEX-22 and CORDEX-44 ensembles can be compared to HighResMIP GCMs. In this respect, CORDEX-22 simulations following the CORE protocol (https://www. cordex. org/experiment-guidelines/cordex-core, last access: M. -E. Demory et al. :  European daily precipitation in EURO-CORDEX RCMs and HighResMIP GCMs October 2020) will be especially valuable, since they provide a core set of comprehensive and homogeneous regional climate projections across many domains of the globe  (Gutowski et al.
g. CMIP, HighResMIP, EURO-CORDEX (through C3S-PRINCIPLES), CORDEX-CORE, CORDEX Flagship Pilot Simulations), the increasing resolution and complexity of both GCMs and RCMs will advance the need for enhanced coordination to produce results that can be fully compared, especially regarding climate change projections (e. g. Boé et al. , 2020). In particular, there is still very limited coordination of kilometre-resolution scenario simulations, and a joint and coordinated database (such as available for CMIP6 and CORDEX) is still missing. Convection-resolving simulations can now be run at a decadal scale, but they are still too expensive to provide multi-model ensembles of centennial climate change projections. End users therefore have to rely on a combination of sources (CMIP, conventional and kilometreresolution CORDEX) for adaptation purposes.
Similarly, our results regarding precipitation distribution show that highresolution GCM and CORDEX simulations could be combined in a joint archive, following the respective format and data standards, thereby making it more convenient for impact groups to use these simulations. Code availability. The code used for the analyses presented in this paper has been developed by  Berthou et al. (2019)  available under the terms of the Apache 2. 0 license from https://github. com/PRIMAVERA-H2020/PrecipDistribution (last access: October 2020, https://doi. org/10. 5281/zenodo. 3956780). This code uses the method that computes precipitation histograms of the contributions of specific intensity bins to the total precipitation based on the ASoP1 diagnostics developed by  Klingaman et al. (2017)  and available under the terms of the Apache 2.
0 license from https: //github. com/nick-klingaman/ASoP (last access: October 2020). Data availability. All PRIMAVERA and CORDEX model data used in this study can be obtained from any Earth System Grid Federation node. Details on how to access the data are available at: https://cordex. org/data-access/esgf/ (last access: October 2020) for CORDEX, and at https://www. primavera-h2020. eu/modelling/data-code/ (last access: July 2020) for PRIMAVERA. Note that the simulations were still being produced at the time of the analysis presented in this paper, so the ensembles presented in this article may not cover all the ensembles available in the ESGF archive. Details about PRIMAVERA data availability can be found in  Roberts et al. (2017) ,  Roberts (2018) ,  Scoccimarro et al. (2018 ), von Storch et al.
(2018) ,  EC-Earth Consortium (2018), and Voldoire (2019). PRIMAVERA Persistent Identifiers (PID) and CORDEX file tracking IDs are listed in the data folder of https://github. com/PRIMAVERA-H2020/PrecipDistribution (https://doi. org/10. 5281/zenodo. 3956780; Berthou et al. , 2019). Spain02 is available at http://www. meteo. unican. es/datasets/ spain02 (last access: September 2019,  Herrera et al. , 2012) ; PT02 is available at http://www. ipma. pt/pt/produtoseservicos/ index. jsp?page=dataset. pt02. xml (last access: September 2019,  Belo-Pereira et al. , 2011). EURO4M-APGD is available at https://www. meteoswiss. admin. ch/home/search. subpage. html/en/ data/products/2015/alpine-precipitation. html (last access: September 2019, https://doi. org/10. 18751/Climate/Griddata/APGD/1. 0; Isotta et al.
Jesús Fernández acknowledges support from the Spanish R&D Program through project INSIGNIA (CGL2016-79210-R), co-funded by the European Regional Development Fund (ERDF/FEDER). We acknowledge the E-OBS dataset from the EU-FP6 project UERRA (http://www. uerra. eu, last access: September 2019) and the data providers in the ECA&D project (https://www. ecad. eu, last access: September 2019). We acknowledge the CARPATCLIM Database © European Commission -JRC, 2013. The authors thank IPMA for the PT02 precipitation dataset, as well as AEMET and UC for the Spain02 dataset, available at http://www. meteo. unican. es/datasets/spain02 (last access: September 2019). The SAFRAN dataset was provided by METEO FRANCE. The European Climate Prediction system, which provided UKCPobs, is funded by the European Union's Horizon 2020 programme, grant agreement no.
776613. We thank the Federal Office of Meteorology and Climatology MeteoSwiss for providing the Alpine precipitation grid dataset (EURO4M-APGD) developed as part of the EU project EURO4M (http://www. euro4m. eu, last access: September 2019). The authors would like to thank Andreas F. Prein and an anonymous referee for their thorough review and constructive comments that contributed to the improvement of this paper. Financial support. This research has been supported by the EU Copernicus Climate Change Service (PRINCIPLES grant). Review statement. This paper was edited by Fabien Maussion and reviewed by Andreas F. Prein and one anonymous referee.
In the context of the ongoing rise in the number of functional trait databases, ecological rarity is an important divergent axis of diversity to consider in conservation while we need to evaluate more thoroughly the impact of ecologically rare species on ecosystem functioning and their contributions to humanity  12. Our results bring insights through profiling ecologically rare species, i. e. species that are both located at the margins or in holes of the global functional space and geographically restricted. The underlying rationale is that species bearing distinct combinations of traits compared to others and filling a large volume of the functional space should be prioritized in conservation strategies owing to their unique potential contribution to ecosystem functioning. We show that the coverage of protected areas for ecological rarity was low, while human pressure and climate change could limit persistence of populations of ecological rare mammals and birds calling for a shared and transboundary responsibility for the conservation of these species.
Methods All analyses were done using R 82 v. 3. 6. 0 (specific functions within specific package are indicated in italic). All relevant R code is available from the associated GitHub Repository (see section Data and Code availability). Distribution data. We used the IUCN range maps  79  for 4787 terrestrial mammal species and the BirdLife range maps  83  for 9993 bird species (breeding ranges only). We removed extinct (EX) and extinct in the wild (EW) species. Given that trait and/or phylogenetic information were not available for all species, we only included species for which we had phylogenetic, functional, and distribution information (9287 bird and 4654 mammal species). Ranges were converted to 50 × 50 km equal-area grid cells. This resolution appears as a reasonable resolution to estimate local distinctiveness of each species and thus discuss the role of global distinct species at small scale.
SES ¼ Observed À MeanðNullÞ sdðNullÞ Values above the null expectation indicate that the cell contains more ecologically rare species than expected given the number of species in the cell, and vice-versa. IUCN status. We used the taxize package v. 0. 7. 8  99  to retrieve up-to-date IUCN status for mammals and birds  79. For easier interpretation, we grouped species in three categories: Critically Endangered (CR), Endangered (EN), and Vulnerable (VU) species as "Threatened" (TH); Least Concern (LC) and Near Threatened (NT) species as "Least Concern" (LC); Data Deficient (DF) and Not Evaluated species as "Not Evaluated" (NE). We performed a multiple comparison Kruskal-Wallis rank-sum test to compare indices across IUCN categories. We used the kruskal() function of the agricolae package v. 1.
2-6  100. Exposure to human pressure. For each species, we assigned three indicators of exposure to human pressure. (a) Global human footprint, which measures the cumulative impact of direct human pressures (extent of built environments, crop and pasture lands, population density, night-time lights, railways, roads, and navigable water-ways) on nature between 1993 and 2009 101. (b) Human development Index (HDI), which captures elements of life expectancy, education, and wealth for the year 2017 102. (c) Human conflicts, which sums the years of conflicts in each country between 1946 and 2015  103. Each dataset was rescaled at 50 km × 50 km resolution and we computed the average value over each species' spatial distribution. Global biodiversity scenarios. We used a similar approach as Thuiller et al.
32  and assessed potential climate change impacts on mammal and bird species under climate change scenarios using an ensemble projection framework. More specifically, we related species distribution to four climate variables describing current climate (1979-2013) derived from the CHELSA dataset:  104  annual mean temperature, annual temperature range, annual sum of precipitation, and precipitation seasonality. Four SDM algorithms available in the biomod2 package  105  were used to estimate these species-climate relations: Generalized Linear Model, Generalized Additive Model, Boosting Regression Trees, and Random Forest. Models were calibrated using 80% of the initial data and evaluated against the remaining 20% of data using the True Skill Statistic (TSS  106  ). Data were randomly assigned to each sub-dataset and this step was repeated four times in order to perform a robust cross-validation.
We fitted a linear regression between these two values to define the target for each species (see the electronic supplementary material of Thuiller et al. 2015  11  ). This was carried out for the two groups separately since they harbor very different distributions of range sizes. Geographic range size data (in km 2 ) was estimated in the Behrmann projection using species' extent of occurrence polygons from the IUCN  79. The proportion of range currently covered by protected areas for each species was extracted from the World Database on Protected Areas (WDPA). This proportion was divided by the defined target to estimate species target achievement; i. e. , how far defined targets for each species is realized. Note that we restricted analyses to protected areas classified as strict protected areas, i.
Acknowledgements This research is a product of the  FREE group  funded by the  Centre for the Synthesis and Analysis of Biodiversity (CESAB) of the Foundation for Research on Biodiversity (FRB)  and EDF. WT received funding from the  ERA-Net BiodivERsA-Belmont Forum , with the national funder  Agence Nationale pour la Recherche  ( ANR-18-EBI4-0009 ), part of the  2018 Joint call BiodivERsA-Belmont Forum call (project "FutureWeb" "). CV was supported by the  European Research Council (ERC)   Starting Grant  Project 'ecophysiological and biophysical constraints on domestication in crop plants' (grant  ERC-StG-2014-639706-CONSTRAINTS ).
Data availability All data used in this paper are freely available and downloadable from the web. Species distribution maps were provided by the Mammal Red List Assessment (http://www. iucnredlist. org/). For birds, breeding range distribution maps were extracted from BirdLife (http://www. birdlife. org/). All climatic data are available on the CHELSA data portal (https://chelsa-climate. org/). IUCN status are available on the IUCN red list (https://www. iucnredlist. org/). Spatial polygons of protected areas were provided by the WDPA (https://www. protectedplanet. net/). Human development index was provided by UNDP, Human Development Indices and Indicators: 2018 Statistical Update. (2018). (http://hdr. undp. org/en/2018-update) and number of armed conflicts by Armed Conflict Dataset (2016).