dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the individual's brain is mapped to the FreeSurfer average brain (see subsequent section). This procedure requires up to 24 hours on a conventional workstation. Manual intervention is rarely necessary. The exceptions are patients with gross brain abnormalities such as tumors and lobectomies for whom the automatic segmentation may fail around the abnormal regions, and the medial wall of the anterior medial temporal lobe, which tends to be underestimated. Once FreeSurfer has completed processing the MRI, a postimplant computerized tomography (CT) or MRI scan is rigidly aligned to the preimplant MRI via an affine transform with six degrees of freedom (Figure  1: B ). iELVis provides Bash scripts for performing this coregistration using the flirt tool  (Jenkinson & Smith, 2001; Jenkinson, Bannister, Brady, & Smith, 2002)  from the Oxford Centre for Functional MRI of the Brain Software Library (FSL: www.
Depending on the number of electrodes, a user can typically tag all electrodes in 30 to 60 minutes. Next, electrode coordinates are saved to a BioImage Suite mgrid file which can be imported into MATLAB and visualized over the pial surface using iELVis functions (Figure  1 : E). Other neuroimaging interfaces (e. g. , FreeSurfer's tkmedit) could be used in lieu of BioImage Suite for identifying electrode coordinates if users record electrode locations in a text file compatible with iELVis conventions. Finally, the subdural electrodes are projected out to the leptomeningeal (i. e. , the smoothed pial) surface to correct for brain shift (Figure  1 : F). Brain shift is caused by factors such as loss of cerebrospinal fluid, swelling, and the displacement of the brain by electrodes  (Hastreiter et al.
DISCUSSION Despite the increasing importance and popularity of iEEG and iEBS research, public software tools for dealing with the unique technical challenges of this work are limited. With the iELVis toolbox, we have addressed the need for an open source, well-documented software package that solves many common iEEG/iEBS technical challenges. iELVis provides a family of MATLAB functions for identifying the anatomical locations of electrodes with mm-scale resolution, for surface-based mapping of electrode locations to an average brain for group analyses, for mapping electrode locations to five anatomical atlases, and for simultaneously, interactively visualizing electrode and neuroimaging data. The toolbox works on all major operating systems, is well-documented, includes welldeveloped tutorial and documentation materials, and is easy to integrate with other popular neuroscience freeware packages such as EEGLAB  (Delorme & Makeig, 2004; Delorme et al.
g. ,  Hermes, Miller, Noordmans, Vansteensel, & Ramsey, 2010 ) might be more effective in cases of significant failure. Finally, an inconvenience of iELVis is its current dependence on multiple non-MATLAB based third-party software packages. This complicates installation and maintenance of iELVis. However, in our experience these dependencies require minimal additional effort, especially since many laboratories already use FreeSurfer and FSL for neuroimaging analysis, and the core functionalities of these decade-old packages are quite stable. In the future, we plan to develop iELVis further to address some of these limitations and to add new functionality. For example, we aim to add tools to quantify the degree of confidence an electrode can be assigned to a particular anatomical region in order to identify electrodes whose anatomical locus is in doubt  (Mercier et al.
, 2016). Moreover, we hope to make it possible to spatially smooth electrode data over the cortical surface or to localize the neural generators of iEEG data in order to improve group analyses. Finally, we hope to add compatibility with additional brain atlases such as probabilistic maps of visual areas (L. Wang, Mruczek, Arcaro, & Kastner, 2015b)  and individual-data derived resting-state fMRI network parcellations  (Fox et al. , 2016; D. Wang et al. , 2015a) , and to remove some dependencies on third party software. We also welcome contributions from other iEEG/iEBS researchers and have established a users group and contribution guidelines to facilitate this (http://episurg. pbworks. com).
ACKNOWLEDGMENTS: We thank the patients for consenting to provide the data that made this toolbox possible. Moreover, we thank Miklos Argyelan, Kathrin Müsch, Taufik Valiante, and Jaime Gomez for helping to debug and to develop this software. This work was supported by the Swiss National Science Foundation (grants PBGEP3_139829 and P300P3_148388 to PM), the Page and Otto Marx Jr. Foundation (to ADM), and by the Natural Sciences and Engineering Research Council of Canada (RGPIN-2014-04465 to CJH).
(2012) neuroimaging and method for brain intracranial shift correction electrode data • Visualizes • Postimplant CT to subdural preimplant/postim electrodes overlaid plant MRI on the brain's coregistration • Correction for surface brain shift • Maps electrode locations to a cortical atlas eConnectom http://econnectome. umn. edu/ MATLAB None • Visualizes e (Windows, subdural electrode OS X, Linux) univariate and bivariate data overlaid on the brain's surface FieldTrip http://www. fieldtriptoolbox. org/tutorial/human_e MATLAB • Relies on • Postimplant cog (Windows, FreeSurfer CT/MRI to OS X, Linux) for T1 MRI- preimplant MRI derived coregistration cortical • GUI for manual surface localization of reconstructi electrodes in on intraoperative • Relies on photographs or SPM for postimplant pre-and neuroimaging post- implant neuroimagi ng coregistrati on iELVis https://github.
of OSCAR v2. 2, a simple Earth system model. The general philosophy of development is first explained, followed by a complete description of the model's drivers and various modules. All components of the Earth system necessary to simulate future climate change are represented in the model: the oceanic and terrestrial carbon cycles -including a bookkeeping module to endogenously estimate land-use change emissions -so as to simulate the change in atmospheric carbon dioxide; the tropospheric chemistry and the natural wetlands, to simulate that of methane; the stratospheric chemistry, for nitrous oxide; 37 halogenated compounds; changing tropospheric and stratospheric ozone; the direct and indirect effects of aerosols; changes in surface albedo caused by black carbon deposition on snow and land-cover change; and the global and regional response of climate -in terms of temperature and precipitation -to all these climate forcers.
OSCAR does not endogenously simulate intra-or inter-annual variability. Consequently, although the time step of its inputs and outputs is 1 year, the main purpose of the model is to simulate trends in the Earth system change, and not year-to-year variations. OSCAR is also a parametric model in which a relatively large number of parameters are almost all calibrated on complex models. We call this approach meta-modeling: each module of OSCAR is designed to emulate the behavior of other more specialized models (e. g. , global climate models, dynamical vegetation models, or chemistry-transport models). For most modules, we have access to several sets of parameters (one per complex model used to calibrate) and, rather than taking the average or arbitrarily choosing one, we adopt a probabilistic approach in which a given simulation with OSCAR is repeated many times with different sets of parameters picked at random.
The historical emissions of fossil-based CO 2 can be based on the CDIAC dataset  (Boden et al. , 2013)  or on EDGAR v4. 2 (EC-JRC/PBL, 2011). Those of CH 4 can be based on EDGAR, ACCMIP  (Lamarque et al. , 2010 ), or EPA (2012). Those of N 2 O can be based on EDGAR or EPA. Those of all hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF 6 ) and nitrogen trifluoride (NF 3 ) are based on EDGAR, while those of ozone-depleting substances (ODSs) are taken from  Meinshausen et al. (2011). The emissions of NO x , CO, VOCs, SO 2 and NH 3 can be based on EDGAR or ACCMIP -this being set independently for each species. Those of OC and BC are based on ACCMIP. Note that the emissions from biomass burning of natural vegetation are removed from these datasets, since those emissions are endogenous to the OSCAR model (see  Sect.
2. 4. 1) , but all other sectors provided by the inventories are included, notably (but not only) agricultural waste burning, shipping, and aviation. Similarly to what is done by  Li et al. (2016, Supplement Sect. B. 1) , the time series of anthropogenic emissions are constructed as follows. First, we choose one of the datasets above as the reference dataset whose absolute values are taken as they are over its period of definition. Second, if needed, we extend the reference dataset beyond its period of definition by following the relative year-to-year variation of other datasets. The extension forward is needed in two cases: with the EDGAR dataset as reference, in which case the extension covers 2008-2010 and is made with the EDGAR-FT v4. 2 dataset (EC-JRC/PBL, 2013) for greenhouse gases and the EDGAR-HTAP v2 dataset (EC-JRC/PBL, 2014) for other species, as well as with the ACCMIP dataset, in which case the extension covers 2000-2010 and is made with EDGAR first and then EDGAR-FT or EDGAR-HTAP.
The extension backward is also needed in almost all cases. It is done with CDIAC for fossil-fuel CO 2 based on EDGAR, with  EDGAR-HYDE v1. 4 (van Aardenne et al. , 2001)  for N 2 O based on EDGAR or EPA and with ACCMIP for any other species based on EDGAR or EPA. For N 2 O, however, given that the EDGAR-HYDE dataset has global values that differ significantly from more recent estimates, the dataset is rescaled to the global estimates by  Davidson (2009)  before being used for extension. For non-CO 2 species, the obtained time series are then linearly extrapolated from 1850, or 1860 in the case of N 2 O: to be zero in 1500 for biogenic emissions, and in 1750 for fossil-related emissions. The year 1500 is taken to be consistent with the dataset we use for landuse change (see Sect. 2. 2. 2). For the HFCs and PFCs whose emissions are not zero in 1970 (first year of the EDGAR inventory) the backward extrapolation is slightly different.
Land-use and land-cover change As OSCAR embeds a book-keeping module to endogenously calculate CO 2 emissions from land-use change, it needs specific types of drivers related to land-use and land-cover change (LULCC). These are threefold. The first driver is for land-cover change itself (δA b 1 →b 2 , an area per unit time): it is the human-induced transitions from one biome b 1 to another b 2. The second driver is for wood harvest (δH b , a mass of carbon per unit time): it is the extraction of woody biomass from a given biome b but without changing the land cover, and it can be seen as a coarse forestry driver. The third driver is for shifting cultivation (δS b 1 ↔b 2 , an area per unit time): it is the transition from one natural biome b 1 to another anthropogenic one b 2 which occurs simultaneously with the reciprocal transition.
The latter driver is therefore a triangular matrix on the (b 1 , b 2 ) axes, and it is typical of (but not exclusive to) the agricultural practice happening in the tropics and known as "slash-and-burn". In this version of OSCAR, only one LULCC dataset is available: the LUH v1. 1 dataset  (Hurtt et al. , 2011)  updated for the last TRENDY exercise  (Sitch et al. , 2015). Given that this dataset provides information only for an aggregated "natural vegetation" biome, whereas OSCAR considers different natural biomes, we need to combine the dataset with the natural vegetation maps used for the terrestrial carbon cycle in Sect. 2. 3. 2, so as to disaggregate further the natural vegetation provided by the LUH project. Other than that, the dataset is used as is over the 1750-2010 period; it is shown in Fig.
As was done previously, we alter the parameters obtained for croplands: we assume there is no wildfire at all within that biome. The CMIP5 fits are shown in Figs. S19 to S22 for fire intensity. Given how experimental it is to include fire processes in a model as simple as OSCAR, we also keep an option to turn off the preindustrial wildfire flux and/or its transient response. Regarding the TRENDY and CMIP5 data processing, it has to be noted that none of the models provide biomespecific outputs. So we choose to deduce biome-specific data by weighting the biome-aggregated outputs of a model by its biome area fraction map, taken to the power 3. This approach is used to give more importance -in a given region -to the grid cells in which biomes are purer, without taking the risk of having too few of those grid cells if we were to set a threshold of biome area fraction instead.
(38) The α bb parameters come from the GFED v3. 1 database (van der  Werf et al. , 2010). The biomass burning emissions of all species are averaged over the whole available timeperiod, and to each vegetation type or sector of GFED is associated a biome of OSCAR: "def" and "for" are forests, "woo" is shrublands, "sav" is grasslands, and "agr" is croplands; "pea", i. e. , peatlands, are left alone. As in Sect. 2. 3. 2, pastures are assumed to be 60 % grasslands and 40 % bare soil. The parameters are then obtained by simply taking the ratio of the emissions of a given species over those of CO 2.
, 2014)  or ESA-CCI (2015) -and by the radiation climatology used for ϕ rsds , in a similar fashion to  He et al. (2014). This approach ensures that the yearly-averaged albedo accounts for the local seasonality, and especially that of snow cover. Also, regarding the deduction of biome-specific albedos, three more assumptions are made: we apply the same weighting method of the land-cover fraction as in Sect. 2. 3. 2; we remove the grid cells that see less than 1 % of their area changing over the historical period and the RCPs according to our LULCC dataset  (Hurtt et al. , 2011) ; and pastures are assumed to be made at 60 % of grasslands and 40 % of bare soils.
Experimental setup We make two series of historical simulations, with the goal of evaluating the performance of each module of OSCAR v2. 2 separately and of the fully coupled model itself. The simulations are realized within a probabilistic framework: a set a drivers and parameters is drawn randomly, with equiprobability, from the pool of potential driving datasets and parameterizations that is summarized in Table  3. With the given drivers and parameters, two simulations are made: one in which the atmospheric concentrations of well-mixed greenhouse gases, the total and per component radiative forcings, and the various climate variables are prescribed to the model; and another in which nothing more than the drivers is prescribed. The first simulation is called "offline", and the second "online".
The offline simulation has the interest of uncoupling the different modules of OSCAR, thus separating them from each other and allowing an easier diagnosis of any potential issue or bias in each module. The online simulation is meant to diagnose the behavior of OSCAR when it is used as a proper Earth system model, i. e. , when it is driven only by the anthropogenic perturbations of the system. The Monte Carlo ensemble size is 10 000 simulations which are drawn from a pool of more than 10 44 potential combinations of parameters (see Table  3 ). As described in Sect. 2. 3. 2, the disaggregation of the terrestrial biosphere follows the nine regions of  Houghton and Hackler (2001)  and six biomes. The time step of solving is one-sixth of a year. For the atmospheric concentrations of well-mixed greenhouse gases, the forcing data used for the offline simulation are from the IPCC  (2013, Tables AII.
Finally, for the climate data used to force the offline simulation, we use the Had-CRUT4 data for global surface temperature  (Morice et al. , 2012) , the HadISST1 for sea surface temperature  (Rayner et al. , 2003) , and the CRU TS3. 23 dataset for local temperature and precipitation  (Harris et al. , 2014). For these three datasets, we assume the preindustrial equilibrium is their respective average over the 1901-1930 period. Note also that, because the climate data are based on observation, the offline simulation will show natural variability, albeit not as a feature of OSCAR but as one of the driving data.
(2011)  to translate biomes into plant functional types (see Sect. 2. 3. 2). Using this table indeed gives a more important fraction of land covered by bare soil than is the case in most of the TRENDY models, explaining the bimodal distribution of the offline land sink. As for the online simulation, the reduced land sink is also a consequence of the warmer tropical climate simulated by OSCAR than the one prescribed with the CRU dataset in the offline simulation (see below). The inter-annual variability of the land sink simulated by OSCAR in the offline case, which is a result of the variability in the input climate data, does not match that from Le  Quéré et al. (2015) , but we do not expect our crude and aggregated approach to model the terrestrial biosphere's response to climate to be able to reproduce this variability, especially as some factors such as volcanoes do not directly influence the terrestrial carbon cycle of our model while they seem to do in reality (e.
Halogenated compounds (Fig. 8) While other species are shown in Fig. S50 , here we show only the first compound of each group of halogenated compounds (i. e. , HFC-23 for HFCs, CF 4 for PFCs, and CFC-11 for ODSs) to illustrate two points. First, OSCAR is able to reproduce relatively well the past evolution of the atmospheric concentration of these compounds, although not with very good performance in all cases. Second, the fact that we only have one set of preindustrial lifetimes and one dataset of anthropogenic emissions hampers our ability to produce a proper distribution of results with OSCAR. Hence, if any of the data is wrong, the simulation with OSCAR will also be wrong with respect to those species. Alternative parameters and/or input data should be used in future versions of the model, or -more importantly -in any future study that would focus on those compounds.
The contrast can be explained by how we process the LULCC dataset that is used as input of this module: we use the TRENDY models, or land-cover climatologies, to know the proportion of each natural biome within a given grid cell of the dataset. Therefore, the huge difference in preindustrial land covers among the TRENDY models or the climatologies leads to this large spread; and the climatologies in which there seem to be too much bare soil (see Sect. 3. 2. 1) lead to the very negative values of this RF. We also note that the offline and online simulations of this RF from land-cover change are strictly equal because the module is driven only by LULCC drivers, and it is therefore not coupled to any other module. All in all, the total RF simulated by OSCAR -which is the sum of the above four RFs and the three drivers prescribed directly as radiative forcing -has a median value in the year.
Reference 2 is NOAA/NCDC  (Smith et al. , 2008)  for global surface temperature, ERSST4  (Huang et al. , 2015)  for sea surface temperature, and NOAA/NODC  (Levitus et al. , 2012)  for ocean heat content. Reference 3 is  (Hansen et al. , 2010)  for global surface temperature. global one, which gives temperature changes consistent with the CRU dataset  (Harris et al. , 2014)  in most regions, with the notable exception of tropical regions. This suggests regional processes should be accounted for, especially as some anthropogenic activities, such as emission of short-lived species and land-use change, can have important regional impacts. This is discussed in the conclusion. Although we cannot compare our global yearly precipitation with a long enough time series of observation, we can note that OSCAR simulates a wide range of precipitation changes, with a non-negligible difference between the offline and online configurations.
Conclusions In this paper, we have provided a complete description of the compact Earth system model OSCAR v2. 2, and we have presented the model's results in the case of an historical simulation. Overall, despite some caveats discussed in the previous section, we conclude that the model performance is good, especially given its level of complexity. OSCAR manages to satisfactorily reproduce most of the past changes in the global Earth system, with an even better performance over the recent period for which better driving data are available. However, we note that a good performance of a simple model over the historical period does not warrant a good performance in any other simulation. In the case of OSCAR, since its parameters are generally calibrated on simulations that go relatively far from the historical conditions (e.
Acknowledgement. First of all, we thank all the modeling team that produced the data we processed to build OSCAR. We also thank those who shared data or info from their model or study:  V. K. Arora ,  P. Cadule ,  A. R. Douglass ,  T. Fu ,  W. Fu ,  C. D. Jones ,  J. -F. Lamarque ,  Y. Lee ,  S. Levis ,  K. Lindsay ,  U. Lohmann ,  J. R. Melton ,  V. Naik ,  J. Pongratz ,  J. T. Randerson ,  B. Ringeval ,  L. Rotstayn ,  X. Shi ,  D. T. Shindell ,  S. Sitch ,  B. D. Stocker ,  J. Tjiputra ,  N. Viovy ,  S. Watanabe , and  H. Yu. Development of versions 2. 0 and 2. 1 was part of the  ACACCYA  project funded by the  GIS Climat-Environnement-Société. Development of version 2. 2 was supported by the  European Research Council Synergy  project  IMBALANCE-P  (grant  ERC-2013-SyG-610028 ). Thomas Gasser also acknowledges support from the  Research Council of Norway  with a visiting researcher grant (# 249972 ) during the writing of this paper.
Download and processing of CMIP5 data was made on the IPSL Prodiguer-Ciclad facility which is supported by  CNRS ,  UPMC , and Labex L-IPSL, and funded by the  ANR  (grant # ANR-10-LABX-0018 ) and the  European FP7   IS-ENES2  project (grant # 312979 ). Download of ACCMIP and CCMVal2 data was made on the  BADC  facility which is part of the  NERC-NCAS. Edited by: G. A. Folberth Reviewed by: two anonymous referees.
2 2. 11. 2 α P G ; β P G Global precipitation response. 25 2. 11. 3 ω PL Pattern scaling of the precipitation response. 2 2. 11. 3 π ohc Fraction of extra energy used to heat the ocean. 1 2. 11. 4 Total available parameterizations -excluding driving datasets 10 41 -including driving datasets 10 44 O 3 s N 2 O a First mention of the parameter in this section. bThepreindustriallandcover(A 0 ) is determined by these two options. cThewetlandpartition coefficients (πwet) are determined by these two options. dThe sensitivity of stratospheric ozone to nitrous oxide (χ.
The exposure of the Earth's surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR).