dataesr/scientific-paragraphs-categorization huggingface dataset:
Other, more user-specific scores could be used, such as the potential economic value  (Richardson, 2000). Various ensemble post-processing techniques have been proposed to improve probabilistic forecasts using historical data: dressing  (Bröcker and Smith, 2008) , Bayesian model averaging  (Raftery et al. , 2005; Berrocal et al. , 2007) , ensemble model output statistics (EMOS,  Gneiting et al. , 2005) , random forest quantiles  (Taillardat et al. , 2016) , among others. Simon (2018)  presented a statistical thunderstorm forecasting technique based on a generalized additive model (GAM) algorithm. These techniques tend to require large homogeneous learning datasets, which may be difficult to obtain for rare events. In most weather centres, model upgrades occur frequently (at least annually), in which case the learning datasets have to be updated using potentially expensive reforecast techniques  (Hamill et al.
Verification method Probability scores rely on the comparison of forecasts and observations of a binary variable, the thunderstorm occurrence. Thunderstorm observations are defined using lightning and radar data as explained in section 2. 1. Thunderstorm forecasts are defined as the probability p that a scalar thunderstorm activity diagnostic, x, exceeds a predefined threshold u: at observation point j, the forecast probability is denoted p(j) = P (x(j) > u). Classically, in ensemble prediction, the forecast PDF P is defined by counting the number of members i that exceed the threshold at this point: p(j) = card{x i (j) > u} i (1) This is equivalent to defining the PDF as a sum of Dirac distributions located at the n ensemble member values (assumed equally likely): P = 1/n i δ[x i (j)] (2) so that p = u -∞ P (x)dx (3) In section 3 we will use a more general definition of P , but the forecast probability will remain a function of the thunderstorm member fields predicted by the ensemble members x i (j).
The optimization technique uses latin hypercube sampling and a surrogate model algorithm, with a diagnostic of tolerance intervals around the estimated optimum parameter values. Output probabilities can be calibrated using an a posteriori rescaling, although  more elaborate calibrations could be used. The post-processing technique has been tested on thunderstorm forecasts. Only thunderstorm occurrence is predicted, not its intensity. The corresponding pseudoobservations have been generated from lightning and radar measurements. Three multiensemble systems, called 'blends', have been post-processed during 92 days of summer 2018, over mainland France. The dataset includes a wide variety of forecast ranges (from 9 to 93 hours) and model resolutions (from 1. 3 to 25km horizontal mesh), using four operational ensembles: a poor man's ensemble of lagged deterministic forecasts, a high-resolution limited area ensemble (AromeEPS), and two global ensembles (PEARP and IFSens).
The optimization of the post-processing parameters, as well as the calibration, appear to have enough statistical robustness for real-time operational applications. The robustness comes at the expense of neglecting variations between models, ensemble systems, and forecast times. Our diagnostics have shown that these variations may have significant implications. The optimized, blended superensembles have reasonable thunderstorm forecasting abilities, although they could probably be improved by including more tuning parameters to better account for the neglected parameter variability. These modifications to the post-processing system would require more computational resources and larger training datasets. We recommend to further improve the proposed algorithm by making the (u, w, d) parameters dependent on the model type used (e.
In production settings where reforecasts are not available, one could adjust the size of the learning dataset so that it always contains enough observed and forecast thunderstorm events to achieve statistical robustness. In some parts of the globe, the availability of ground-based lightning and radar data may be problematic, in which case thunderstorm products derived from satellite observations should be useful alternatives. We have shown that the statistical post-processing has a large impact on the performance of the post-processed multiensembles. Probabilistic forecasts based on direct ensemble output (without any post-processing) usually benefit from higher model resolution and larger ensemble size, but as we have seen, this is not always true after post-processing: in some conditions, a poor man's ensemble or a low-resolution global model can outperform more expensive NWP systems.
The probabilistic forecast quality and the benefit of multiensembles also depends on the user cost-loss ratio -that is, on the relative cost that is attached to false alarms and to missed events. In a nutshell, multiensembles do not necessarily beat single-ensemble systems in all respects, but with a suitable post-processing they can be an attractive way of combining output from multiple systems, for specific user needs, and on a broad range of forecast horizons. It would be useful to extend the approach used here to forecast violent thunderstorms. This would require a complexification of the observation definition (taking into account important meteorological variables such as gusts, hail and rain accumulation) and of the model diagnostics used. The rarity, and often low predictability, of these events will require larger training datasets, possibly including nowcasting products in the blending.
Remote sensing data The PROFOUND DB includes remote sensing information at different spatial scales and temporal frequencies, specific for each product. We   Center (ORNL DAAC) (https://daac. ornl. gov/MODIS/, last access: 5 June 2020). The ORNL DAAC MODIS subsetting web service is implemented to allow users access to massive amounts of remote sensing data  (Santhana-Vannan et al. , 2011). In addition, a second set of vegetation indexes was calculated from the reflectance values. A summary of this information is shown in Table  6. The full list of variables and how they were aggregated is provided in Table  S10. The main difference among the forest sites is the data quality, which is highly dependent on the presence of clouds. When possible, low-quality observations have been substituted by interpolated values, otherwise the cell was left blank.
In any case the alteration of the original data was minimal. It is also important to note that the size of the pixel is large compared to the plot size of the forest stands, which means the pixel data also contain other vegetation types than the ones present at the sites. Three general types of data are included: (1) geophysical variables as measured from the MODIS sensor, i. e. reflectance and temperature; (2) spectral indexes derived directly from reflectance values at different wavelengths; and (3) vegetation properties (i. e. FPAR, LAI, GPP and net photosynthesis) as estimated from physical variables through a range of models. Although the MODIS sensor acquires daily information, the PROFOUND DB includes only composite data; that is, for each pixel the best value during a period of time (8 or 16 d) is selected as being representative of that specific period.
Gross primary productivity and net photosynthesis estimations are based on the light use ef-Table  6. Summary of the remote sensing data included in the PROFOUND DB. VIS, NIR and SWIR are the visible, near-infrared and shortwave infrared regions of the electromagnetic spectrum. NDVI: normalized difference vegetation index; EVI: enhanced vegetation index; FPAR: fraction of photosynthetically absorbed radiation; LAI: leaf area index; GPP: gross primary productivity; NDWI: normalized difference water index; AR: angle at red; ANIR: angle at NIR; AS1: angle at shortwave infrared 1; AS2: angle at shortwave infrared 2; SANI: shortwave angle slope index; SASI: shortwave angle slope index. ficiency (LUE) concept  (Monteith, 1972)  using satellitederived FPAR (from MOD15) and independent estimates of PAR, besides other types of ancillary data.
The PROFOUND R package (ProfoundData) The ProfoundData R package provides functions to access the PROFOUND DB (Figs. S2  and S3 ). The ProfoundData package plus a detailed vignette explaining the functionalities are available on CRAN (https://CRAN. R-project. org/ package=ProfoundData, last access: 5 June 2020). The Pro-foundData package serves as an interface for users that want to access the PROFOUND DB as a relational database via the R statistical software  (R Core Team, 2016). The following main functions are included to achieve this goal: https://doi. org/10. 5194/essd-12-1295-2020 Earth Syst. Sci. Data, 12, 1295-1320, 2020 -"getData" to download data (data can be downloaded for one forest site and one underlying dataset at a time); -"browseData" to check the available forest sites, datasets, variables for a dataset, datasets for a forest site and the database version, metadata, data policy and original data source; -"plotData" to quickly inspect any variable of the datasets visually; -"summarizeData" to summarize data from the database; -"queryDB" to pass self-defined queries; -"writeSim2netCDF" to write netCDF files and can be used to convert data (and other files such as model simulation output) into netCDF files.
This article present observational and experimental data describing a range of biotic and abiotic parameters that can be related to ecosystem services under contrasted types of crop management: conventional, conservation and organic agricultures. Ninety fields, either cultivated with winter wheat or fava bean, located in Southwestern France, near Toulouse, were monitored for two growing seasons  (2014) (2015) (2016). The dataset encompass data about crop pests (aphids, grain borer, bean beetles, slugs), crop pest natural enemies (hoverflies, parasitoids, predators), soil sensitivity to erosion, crop productivity, pathogenic fungal infection and root colonization by mycorrhiza. This article present detailed protocols applied for each measurement and data collected to describe the context of each field: soil structure, landscape and crop management indicators.
Value of the data This dataset provides an unprecedented broad range of simultaneous measurements of biophysical, ecological and agronomic parameters, for the study of agroecological performances of conservation agriculture in France, with comparison with organic and conventional production systems. It comes from a collaborative work with farmers allowing reliable in-field assessment rather than experimental plots. This dataset provides a priceless reference for future researcher in agronomy and ecology seeking reference for agroecological performances of early adopters of Conservation Agriculture in France. It also provides in-fields values that are still lacking to support studies on ecosystem services bundles in agroecosystems. This dataset can be reused for meta-analyses, either by focusing on one specific ecosystem service or agronomic performance or by appreciating bundles of them.
Data In this data article, we are sharing an extensive recollection of data resulting from two years monitoring of winter wheat, fava bean and mix fava bean-wheat fields distributed among 52 farms. Data encompass several separate measurements presented in 16 separate excel sheets in the file Full_dataset. xlsx. In the sheet 'Cropping_System', a set of crop management indicators to describe each farm is presented, including tillage, use of chemical inputs, diversity of production and semi-natural habitats. The sheet 'Soil_Parameters' presents a soil analysis (texture, pH, organic matter) for the wheat field of each farm. A coarse description of the surrounding landscape, within 1. 5 km around the wheat field of each farm, can be found in the sheet ' Land-scape_1500 m' and the area of semi-natural habitats that can be found on the farm are listed in the sheet ' Semi_Natural_Habitat'.
Several measurements of pests and natural regulations were made: counts of grain aphids on wheat ears, are in the sheet ' Aphids' , and counts of pupae of one of their potential predators, the long hoverfly, are in the sheet ' Hoverflies'; counts of wheat stubbles obstructed by grain borer, are in the sheet ' Grain_borer'; number of fava bean grains parasitized by bean beetles and assessment of bean beetle parasitism by Triaspis thoracicus are presented un the sheet 'Bean beetles'; Number and species of slugs counted in wheat fields at different seasons (spring and fall) are presented in the sheet ' Slugs' and number of ground predators (including spiders, carabids and rove beetles) counted simultaneously to the slug assessment are presented in the sheet ' Ground Predators'. Other agronomic performances are presented in this dataset, such as speed of water infiltration and soil surface aggregate stability as indicators of soil sensitivity to erosion, in the sheet ' Erodibility' , crops yields, respectively in the sheets ' Wheat_Yields' and ' Fava_bean_Yields' , and visual assessment of Leaf diseases, in sheets ' Wheat_Diseases' and ' FavaB_Diseases'.
The standardized scale used for leaf diseases assessment can be found as supplementary material of this article. Concentrations of several mycotoxins in wheat grains were analyzed and also presented in the sheet 'Wheat_Diseases'. Finally, descriptors of mycorrhiza colonization of roots are presented in the sheet ' Mycorrhiza'. The second file, Labels_descr_dataset. xlsx, gives a comprehensive list of sheet and column labels that appear in Full_dataset. xlsx with their description and unit, as well as indication of the fields the data came from (NTW: not treated wheat, OW: organic wheat, W: wheat, F: fava bean, WF: wheat + fava bean mix) and either data referred to the first (2014-2015) or the second (2015-2016) year of experiment (see experimental design below). On every sheet of the Full_dataset.
xlsx , a code to identify the farm ( 'Farm_code' ) is given on the first column along with date of sampling and identification of the field ( 'Field_code' : NTW, OW, W, F or WF) when relevant. Data were collected around Toulouse, Southwestern France, from September 2014 to July 2016. Farms were distributed along a 200 km line intersecting Toulouse, from 43 °N, 0 °E to 44 °N, 2 °E, crossing three French counties: Gers, Haute Garonne and Tarn. Our agreement with farmers did not allow us to publicly identify their fields with geographic coordinates. However, county of each field can be found in the sheet Landscape_1500 m of the dataset and a map along with more details about location can be found in Chabert and Sarthou  [2].
In this dataset, raw values of actual volume infiltrated and corresponding time of infiltration are given for the first ( Vol_1, Time_1 ) and second pouring ( Vol_2, Time_2 ). Structural stability of soil aggregates was assessed based on a method for measuring water turbidity using a Secchi disk  [4]. A small version (45 mm diameter) of the standard Secchi disk was plunged into the metal cylinder immediately after the second pouring of water and the depth of clear water from the water surface was immediately measured. This method relies on the assumption that measured turbidity is attributable to the quantity of fine particles that are likely to be detached from the soil due to rain and runoff. In cases where water infiltration was too rapid to allow measurement, additional water was poured to completely fill the cylinder.
Crop yields Winter wheat and fava bean yields were estimated by collecting grains immediately before harvest, simultaneously with Long hoverfly pupae and Broad bean beetle parasitoid sampling in 2015. To allow expression of yields in quintals per hectare for both crops, fava bean plant density was estimated by counting plants in three random plots of 1 m 2 and 20 plants were randomly sampled for weighting grains. For wheat, sampling effort was not homogenous among all fields because of very scarce development of wheat in some fields (6 to 15 for the samplings for hoverflies were kept for yields). Yield values are expressed per hectare, taking into account the number of hoop area sampled, yet to keep track of the sampling effort the number of samplings is recorded in the dataset ( Sampling_nber ).
g. in BPD patients,  (a et al. , 2012) ). These miniblocks were counter-balanced and delivered in a pseudorandom order (with never more than 2 mini-blocks of the same type in a row). For the social condition, we used 40 individual faces (20 female) with neutral (for the cues), happy and angry expressions (for the feedbacks) from the Karolinska Directed Emotional Faces database  (Lundqvist et al. , 1998). To familiarize the participants with the task and to remove any effects of learning, a practice session was performed before the fMRI session, outside the scanner, using faces which were not included in the fMRI experiment. To ensure that all participants understood the task fully, we questioned them about the meaning of the feedback following each trial.
MRI data acquisition and analysis Functional images were acquired using a multiplexed EPI sequence  (Feinberg et al. , 2010)  with repetition time (TR)=650 ms, echo time (TE)=30 ms, flip angle=50°, 36 slices, 64 × 64 pixels, voxel size=3 × 3 × 3 mm. The multiband acceleration factor was 4, and parallel acquisition technique (PAT) was not used. Structural images were acquired with a T1 weighted 3D sequence (MPRAGE, TR/inversion time/TE=1900/900/2. 27 ms, flip angle=9°, PAT factor=2, matrix size=256 × 256 × 192, voxel size=1 × 1 × 1 mm). All fMRI data analyses, including image preprocessing and analyses, were performed using SPM8 software (http://www. fil. ion. ucl. ac. uk/ spm) implemented in Matlab R2012b. During preprocessing, the functional volumes were first realigned to the mean image, normalized using the EPI template provided with the SPM toolbox, and finally smoothed with an 8 mm 3 Gaussian kernel.
To account for any residual movement artefacts after realignment, we used the Artefact Detection Toolbox (ART; http://web. mit. edu/swg/software. htm; see Supplementary Material for thresholds employed). In addition to utilizing the ART toolbox, we also visualized a portion of the images from each participant at each analysis step in order to ensure the quality of the data. Statistical analyses were performed on a voxel-wise basis across the whole-brain. Using an event-related approach, individual events were convolved with a standard synthetic hemodynamic response function (HRF). Six individual regressors represented the main event types. For the cues: social and non-social cues; for the feedback: social win outcome, social lose outcome, non-social win outcome, non-social lose outcome. Events corresponding to feedback for erroneous responses (i.
Model 2 was conducted to test H2, by determining the differences between the groups when processing social feedback. The corresponding linear contrasts were entered into a separate 2 × 2 × 2 full factorial model with two withinsubject factors 'Social Condition' (social, non-social) and 'Reward Outcome' (win, lose), and one between-subjects factor 'Group' (HC, BPD). As we expected to find group differences in the frontal brain in Model 1 (i. e. anticipatory social cue processing) and the amygdala in Model 2 (i. e. socially evaluative feedback processing), we utilized a region-of-interest (ROI) analysis approach using the WFU PickAtlas toolbox for SPM  (Maldjian et al. , 2004; Maldjian et al. , 2003). Specifically, we created one mask of the frontal lobe (used to test H1 in Model 1) and a second mask of the bilateral amygdala (used to test H2 in Model 2), both of which were from the Talairach Daemon database.
Acknowledgments This work was supported by the  National Center of Competence in Research (NCCR) Affective Sciences  financed by the  Swiss National Science Foundation  (grant number:  51NF40-104897 ) and hosted by the  University of Geneva , and by the grant  SFETD-IUD 2012  from  Institut UPSA de la Douleur  (to EO). We would like to thank  Rosetta Nicastro  and  Paco Prada  for their role in recruiting the BPD patients as well as  Emilie Douine  for her help in assessing all participants.
The Lake Baikal region of southern Siberia has a rich mortuary record with good skeletal preservation that has provided the most comprehensive isotopic database for palaeodietary studies of north-temperate hunter-gatherers in the world. Building on previous work, this study contributes new δ 13 C, δ 15 N, and AMS radiocarbon dating results from the cemeteries of Verkholensk (n = 44) in the Upper Lena River micro-region and Ulan-Khada (n = 19) in the Little Sea micro-region. Our results reveal several previously unrecognized patterns. Early Bronze Age (EBA, 4600-3700 cal BP) individuals at Verkholensk exhibit lower δ 15 N values than in the Late Neolithic (LN, 5570-4600 cal BP), suggesting a shift to a more terrestrial diet in the later period. In addition, EBA individuals at Verkholensk differ in both δ 13 C and δ 15 N from those at the nearby site of Obkhoi, suggesting territorial divisions at a surprisingly small scale, though there is a diachronic component that also needs to be considered.
Introduction The Lake Baikal region of eastern Siberia has been well-studied archaeologically for over a century  (Okladnikov, 1950 (Okladnikov, , 1955 (Okladnikov, , 1959)). More recently, the Baikal Archaeology Project  (BAP, 1995 (BAP, -2011) )  and the Baikal-Hokkaido Archaeology Project  (BHAP, 2011 (BHAP, -2018) )  have contributed much new data focused on the excavation and analysis of human and faunal remains. The extensive comparative isotopic and radiocarbon datasets now available make it possible to obtain a finer resolution understanding of prehistoric hunter-gatherer lifestyles than is usually feasible. The current study builds on previous research by exploring the interactions between two micro-regions: Lake Baikal's Little Sea and the Upper Lena River (Figure  1 ). Specifically, this study sheds light on our understanding of intergroup dietary variation, practices of land tenure and sharing, and temporal shifts in diet and mobility.
Analysis of individuals from Ulan-Khada allows us to expand upon an extensive dataset for the Little Sea micro-region and to further assess previously identified dietary patterns, e. g. to test the validity of the Little Sea 'Game-Fish' and 'Game-Fish-Seal' diet groups  (Weber and Bettinger, 2010; Weber and Goriunova, 2013; Weber et al. , 2011) , as well as any age-or sexbased patterning within them. Yet, the analysis of individuals from Verkholensk is particularly significant because previous investigations in Cis-Baikal have focused less on groups living along the Upper Lena River. The inclusion of data from Verkholensk, the largest excavated cemetery there to date, begins to fill this gap, and allows testing of previously proposed hypotheses. Specifically, hunter-gatherer travel between the Upper Lena to the Little Sea has been suggested based on carbon, nitrogen, and strontium isotope results  (Weber and Goriunova, 2013).
, 2018). Comparisons with results from other laboratories as well as with environmental datasets are therefore problematic. Nevertheless, while attenuating the biogenic signal, the exchangeable oxygen is expected to be similarly affected as long as the samples were prepared at the same time in the same laboratory (i. e. , the 'principle of identical treatment'), and so can still be compared in relative terms with one another. We are mainly interested in: 1) determining whether or not the expected difference in δ 18 O can be identified between the two sites, indicating that at least part of the endogenous signal survives; and 2) to further investigate outliers identified through δ 13 C and δ 15 N analysis.