dataesr/scientific-paragraphs-categorization huggingface dataset:
Food Safety Knowledge Markup Language (FSK-ML) The terms, concepts and metadata schema presented are an important foundation for the creation of the first specification for the Food Safety Knowledge Markup Language (FSK-ML) (URL: https://foodrisklabs. bfr. bund. de/fsk-ml-food-safety-knowledge-markup-language/). The FSK-ML specification is a software developer guidance document that explicitly specifies the structure and content of the files (FSKX-file) that may be used to encode models in the domain of food safety risk assessment in the future. Similar markup languages are used in different scientific domains like the Systems Biology Markup Language (SBML) (URL: http://sbml. org/Main_Page). The development of FSK-ML is crucial to enable sharing of data, QMRA, empirical models and PM models between different software tools and model repositories.
Discussion The harmonized terms, concepts and metadata presented in this paper are an important first result of the RAKIP project, a collaboration between three European food safety risk assessment institutions (ANSES, BfR and DTU Food). They were extensively discussed by the RAKIP partners, with the objective to create the foundation for the FSK-ML format. They are a requirement to create new community resources that support knowledge integration and exchange within the PM and QMRA areas, which is the main objective of the project. Besides the creation of the FSK-ML language, RAKIP aims at the creation of a web portal (URL: https://foodrisklabs. bfr. bund. de/rakip-webportal/) that allows the microbiological food safety community to (i) contribute to the definition on harmonized terms and concepts (ii) share software libraries, converter tools and software-specific import and export functions promoting the adoption of FSK-ML by the risk assessment community; (iii) share FSK-ML compliant models and data in a model repository with search and filtering functionalities that also allows the download, execution and modification of models.
judge how reliable the model might be.  Data: Symbolic representation of observable properties of the world.  Data collection: The process of gathering data. Different types of data can be collected (microbial concentrations, daily consumption, etc. ). The collected data should be fit for purpose, representative and allow a meaningful analysis.  Data-driven knowledge: Knowledge derived from experimental/observational data.  Decision-making: The action or process of making decisions based on scientific facts or results. In the context of risk management, decision-making considers the risk assessment results and other factors relevant for the health protection of consumers and for the promotion of fair trade practices.  Dimension: Possible number of elements constituting a metadata concept.
A C C E P T E D M A N U S C R I P T 28 parameter models  (Rosso et al. , 1993) , the log-logistic, log-probit and Weibull(-Gamma) models as dose-response models (FAO/WHO, 2003), time-temperature models  (Laguerre et al. , 2014; Laguerre and Flick, 2010; Lecoq et al. , 2016)     FSK-ML (Food Safety Knowledge -Markup Language): FSK-ML is a specification for knowledge exchange format that can be used to encode models in the domain of food safety risk assessment.  FSKX-file: File format defined within the FSK-ML specifications supporting the exchange of risk assessment models.  Generic Metadata Schema: The complete set of metadata concepts generated as part of the RAKIP project that allows annotating with sufficient detail any type of model or data in the QMRA or PM modelling domain. A   Module: The module definition comes from the Modular Process Risk Model methodology developed by  Nauta (2001)  where the food production chain is divided into modules.
Funding This work was jointly supported by the  German Federal Institute for Risk Assessment (BfR) , the  National Food Institute  ( DTU Food ) from the  Technical University of Denmark (DTU)  and the  French Agency for Food, Environmental and Occupational Health & Safety (ANSES). This work has been supported in part by the  AGINFRA   PLUS  project that is funded by the  European Commission 's  Horizon 2020 research and innovation programme  under grant agreement No  731001 , the  EFSA-BfR Framework Partnership Agreement   GP/EFSA/AMU/2016/01  and by funds of the  Federal Ministry of Food and Agriculture (BMEL)  for the project " FoodAuthent" based on a decision of the Parliament of the Federal Republic of Germany  via the  Federal Office for Agriculture and Food (BLE)  under the innovation support programme.
Physical data Annual SSTs originated from the dataset ERSST_V3 (1850-2014). This dataset is derived from a reanalysis based on the most recently available International Comprehensive Ocean-Atmosphere Data Set (ICOADS). Improved statistical methods have been applied to produce a stable monthly reconstruction, on a 1˚x 1˚spatial grid, based on sparse data  [16]. Both mean annual SST and SST variability (i. e. the coefficient of variation of SSTs) were calculated on the basis of monthly SSTs for the period 1850-2014; a total of 165 years x 12 months = 1980 values were therefore used to assess mean annual SST and SST variability in each geographical cell. SST variability was subsequently scaled between 0 and 1 as follows: g Ã ¼ g À minðgÞ maxðgÞ À minðgÞ ð1Þ With g Ã the scaled variable and g SST variability.
Biological data The dataset of observed biodiversity was provided by Dr Derek Tittensor, Dalhousie University  [4]. The data were compiled from empirical sampling data (foraminifers and bony fish) or from expert-verified range maps (euphausiids, oceanic sharks and marine mammals) encompassing many decades of records. The data was originally gridded on a 880-km equal-area resolution grid  [4]. The taxonomic groups we chose had a species richness that reflected well its biodiversity (i. e. species richness greater than 50% of known species; see their Table  1 ): foraminifers (Chromalveolata Rhizaria, 39 species; 88% of known species), euphausiids (Crustacea Malacostraca, 86 species; 100% of known species), oceanic sharks (Chondrichthyes, 27 species; 100% of known species), tuna/billfish (Osteichthyes, 12 species; 63% of known species), cetaceans (oceanic marine mammals, 81 species; 96% of known species) and pinnipeds (sea/land marine mammals, 36 species; 100% of known species); this last taxonomic group shows a wellknown inversed latitudinal biodiversity pattern  [4] , which is explained when biodiversity is decomposed into groups with different levels of steno/eurythermy  [7].
e. weighted mean of the degree of thermophily for all pseudo-species) per geographic square. These two properties were calculated to better understand ecogeographic patterns in the degree of niche saturation of the six taxonomic groups. We represented the degree of eurythermy using 10 categories, each representing a decile; for example, the first and last categories corresponded to the first and last deciles. Differences in biodiversity between the Last Glacial Maximum/mid-Pliocene and today (variables 4 and 5 in Fig  1 ): data on quantitative changes in biodiversity expected between the Last Glacial Maximum (LGM, ~20,000 years ago) and today, and between the mid-Pliocene (mid-Piacenzian, 3. 264-3. 025 Ma) and today, originated from the model of Beaugrand and colleagues  [8]. The index of quantitative change was the sum of the differences between theoretical abundance (standardized between 0 and 1) at two time periods weighted by species richness common for the two time periods.
This index was calculated for both periods for pseudo-species having an abundance higher than 0. 1 (a threshold below which the species was considered to be absent) for both periods. For a given geographical cell, quantitative index F was calculated as follows: F ¼ 1 p X p i¼1 ja i À b i j ð3Þ With p species richness common to the two time periods, a i the abundance of species i at the second time period and b i , the abundance of species i at the first time period. In this model, the maximum abundance of all species reaches 1. Those gridded data were used as a proxy for environmental perturbations induced by long-term climate change. All biological variables assessed from METAL were calculated on a spatial grid of 2˚longitude x2˚latitude from 178˚W to 180˚E and from 88˚S to 88˚N. Step 2: Spatial interpolation of mean and variability in SSTs (variables 6 and 7 in Fig  1 ).
Those data were utilized to examine the relationships between niche saturation and a proxy for evolutionary rates. Step 4: Spatial interpolation of observed species richness (variables 9-14 in Fig  1 ). Observed species richness for foraminifers, euphausiids, tuna/billfish, oceanic shark, cetaceans and Pinnipeds were also spatially interpolated on a grid of 2˚longitude x2˚latitude from 178˚W to 180˚E and from 88˚S to 88˚N using the inverse squared distance method  [26]. Step 5: Correlation between expected and observed large-scale biodiversity patterns. Linear correlations between expected and observed biodiversity patterns were calculated for each taxonomic group and between expected biodiversity and both mean and variability in SSTs (Fig  1 ). To account for spatial autocorrelation in the geographical pattern of species richness (two dimensions), the degrees of freedom were recalculated to indicate the minimum number of samples (n Ã ) needed to maintain a significant relationship at p = 0.
The authors thank the  "Centre National de la Recherche Scientifique " for funding partially this study. This work is also a contribution to the CPER research project  CLIMIBIO , the regional programme  INDICOP (Pas-de-Calais ) and the  French National ANR-FEM TROPHIK programme. The authors also thank the  French Ministère de l'Enseignement Superieur et de la Recherche , the  Hauts-de-France Region  and the  European Funds for Regional Economical Development  for their financial support to this project.
g. clonogenic cell assays, clinical trials, computational radiobiology, the majority of these high Z NP radiosensitisation studies for given cell line/tissue type are typically limited to two measurements for each tested photon source: a control and a single doped concentration (C M ). To enable the applica- tion of these datasets to a clinically relevant scenario, i. e. varied high Z NP concentrations (C) in different tissue regions, interpolated values for α A and β A can be determined using the interpolation framework of  Brown and Currell (2017), i. e. ,  where �α = α B (C M ) -α A and �β = β B (C M ) -β A. Substitution of Eq. 6 into Eq. 5 gives the final form of the RBED framework:.
, 2016). Moreover, thanks to an additive constraint on the model structure, this may improve the model performance. For instance, radiocarbon is an important tool for studying the dynamics of soil organic matter  (Trumbore, 2000). Indeed, 14 C data acquired from soil organic matter provide complementary information on the dynamics (temporal dimension) of soil organic matter. This tracer has the major advantage of being integrator of carbon dynamics on long timescales (a few decades to several centuries). It is therefore a very powerful tool to constrain conceptual schemes that may not be directly compared to variables measured in the field  (Elliott et al. , 1996). Different authors have already successfully implemented radiocarbon in soil models and were able to clearly show that the introduction of pools with turnover time of thousands of years were unnecessary to fit radiocarbon data  (Ahrens et al.
ORCHIDEE-SOM overview ORCHIDEE is the LSM of the IPSL Earth system model  (Krinner et al. , 2005). It is composed of three different modules. First, SECHIBA  (Ducoudré et al. , 1993; de Rosnay and Polcher, 1998) , the surface-vegetation-atmosphere transfer scheme, describes the soil water budget and energy and water exchanges. The time step of this module is 30 min. Second, the module of the vegetation dynamics has been taken from the dynamic global vegetation model LPJ  (Sitch et al. , 2003). The time step of this module is 1 year. Finally, the STOMATE (Saclay Toulouse Orsay Model for the Analysis of Terrestrial Ecosystems) module simulates vegetation phenology and carbon dynamics with a time step of 1 day. ORCHIDEE can be run coupled to a global circulation model where the boundary conditions of the model are provided by the atmospheric modules (temperature, precipitation, atmospheric CO 2 concentration, etc.
). In return, OR-CHIDEE provides the land surface carbon, energy and water fluxes. However, since our study focuses on changes in the land surface rather than on the interaction with climate, we ran ORCHIDEE in the offline configuration. In this case, atmospheric conditions such as temperature, humidity and wind are read from a meteorological dataset. The cli-mate data CRUNCEP used for our study (6-hourly climate data over several years) were obtained from the combination of two existing datasets: the Climate Research Unit (CRU;  Mitchell et al. , 2004)  and the National Centers for Environmental Prediction (NCEP;  Kalnay et al. , 1996). Our starting point is a ORCHIDEE-SOM version based on the SVN r3340  (Krinner et al. , 2005) , which is presented in detail in  Camino-Serrano et al. (2017).
All samples were crushed and air-dried. Once in the laboratory, they were homogenized, crushed, randomly subsampled and sieved at 200 µm. Then 14 C measurements were made us-ing the ECHoMICADAS following the recommendations of  Tisnérat-Laborde et al. (2015). For the four sites, the SOC (kg m -3 ), for each depth z, was calculated using carbon content and bulk density data using the following equation: SOC z = OCC z × BD z , (8) where OCC (wt/wt) is the carbon content and BD (kg m -3 ) is the bulk density.
Different model tests After the implementation of radiocarbon in the model, different tests were carried out (Table  2 ). Here we represent the outputs provided by three simulations: First, simulation using the initial version ORCHIDEE-SOM-14 C (labelled "Control" in figures and tables) in which no changes were made. The diffusion was kept constant throughout the profile (D = 1. 10 -4 m 2 yr -1 ) and the other parameters are those of the detailed version in  Camino-Serrano et al. (2018). Second, simulation using the initial version ORCHIDEE-SOM-14 C in which we modified some parameters following  He et al. (2016 ) (labelled "He et al. , 2016, parameterization"  in figures and tables). In brief, the authors used 14 C data from 157 globally distributed soil profiles sampled to 1 m depth to evaluate CMIP5 models.
Model simulations In order to reach a steady state of the soil module, we ran the model over 12 700 years (spin-up). The state at the last time step of this spin-up was used as the initial state for the simulations. For this, the CRUNCEP meteorological data for the period 1901-1910 were used. This has been applied for Misiones, Feucherolles and Mons. However, for Kissoko, a first spin-up similar to the other sites was carried out but a second one (over approximately 4200 years) was also done after the end of the first to take into account the change of the land cover from a tropical forest to a C 4 savanna at this site  (Schwartz et al. , 1992). The atmospheric CO 2 concentration has been set to 296 ppm (year 1901;  Keeling and Whorf, 2006)  for the spin-ups and the F 14 C has been set to one corresponding to pre-industrial values.
Therefore, the use of radiocarbon ( 14 C) tracers in global models emerges as a promising tool to constrain not only SOC turnover times in the long-term  (He et al. , 2016)  but also internal SOC processes and fluxes that have no direct comparison with field measurements. Nevertheless, the model evaluation performed here on only four sites should be considered as proof of concept and more in-depth evaluations are needed, in particular using a large 14 C database available at a global scale  (Balesdent et al. , 2018; Mathieu et al. , 2015). Indeed, the F 14 C is largely controlled by pedo-climatic conditions such as clay content, climate and mineralogy  (Mathieu et al. , 2015)  and the range of situations we covered here is relatively limited. 4 Conclusions ORCHIDEE-SOM-14 C, is one of the first land surface models (LSMs) that incorporates the 14 C dynamics into the soil  (Koven et al.
Acknowledgements. This study, part of  Marwa Tifafi 's PhD and financed by the  University of Versailles Saint Quentin , is within the scope of the  ANR-14-CE01-0004   DeDyCAS  project. Marta Camino-Serrano  acknowledges funding from the  European Research Council Synergy  grant  ERC-2013-SyG-610028  IMBALANCE-P. Part of the data were acquired in the frame of the  AGRIPED  project ( ANR 2010 BLAN 605 ). We thank  Matthew McGrath  for his valuable comments on the manuscript. Edited by: Carlos Sierra Reviewed by: three anonymous referees.
Code availability. The version of the code is freely available here: https://doi. org/10. 14768/20181114001. 1  (Tifafi et al. , 2018). Author contributions. MT, CH and BG designed the study. MT and MCS developed the model. HM, LM and SB provided soil samples and data on the Misiones site; SC provided data on the Feucherolles and Mons sites. HM, LM, SB and SC provided pedological information on sites. MT and CH measured 14 C on the Argentinian soil samples. MT planned and carried out the simulations.
Motivation: More than 20 years ago, our laboratory published an original statistical test (referred to as the Audic-Claverie (AC) test in the literature) to identify differentially expressed genes from the pairwise comparison of counts of cognate RNA-seq reads (then called "expressed sequence tags") determined in different conditions. Despite its antiquity and the publications of more sophisticated software packages, this original article continued to gather more than 200 citations per year, indicating the persistent usefulness of the simple AC test for the community. This prompted us to propose a fully revamped version of the AC test with a user interface adapted to the diverse and much larger datasets produced by contemporary omics techniques. Results: We implemented ACDtool as an interactive, freely accessible web service proposing 3 types of analyses: 1) the pairwise comparison of individual counts, 2) pairwise comparisons of arbitrary large lists of counts, 3) the all-at-once pairwise comparisons of multiple datasets.
The continuous usage (hence usefulness) of this statistical test now commonly referred to as the "Audic-Claverie test" (e. g. Bortoluzzi et al. , 2005; Metta et al. , 2006; Kim et al. , 2008; Tino, 2009; Wong et al. , 2013)  prompted us to revisit its mathematical formulation and adapt its numerical calculation to the much larger datasets and count values generated today. We then implemented this modernized R-library-based version of the test as an interactive web-service targeted to biologist end users, allowing three types of analyzes: 1) the basic pairwise comparison of individual counts, 2) the pairwise comparisons of arbitrary large lists of counts, 3) the all-at-once pairwise comparisons of multiple datasets. ACDtool is proposed as an easy-to-use generic tool capable of processing the immense variety of modern omics techniques (RNA-seq, metagenomics, barcoding, proteomics, population genetics, etc) generating very large (albeit often very sparse) count datasets.
Introducing a distance In addition to the calculation of a p-value from a pair of counts associated to the same event (organism, object, label, etc), the ACDtool web service now implements the possibility of comparing whole datasets at once. For this, we introduced a Shannon-inspired measure of distance involving the logarithm of the p-values as computed above, normalized by the relative contribution of each event to the total count. Each event contributes the distance: 𝑑 = - (𝑥+𝑦) ( 1+  2 ) ((, )) (5) to the global distance D, computed for all K events as:.
More recently developed codes calculate the CSR effect in dispersive regions directly from the Liénard-Wiechert potentials, albeit with approximations to improve the computational time. A new module of the General Particle Tracer (GPT) code was developed for simulating the effects of CSR, and benchmarked against other codes. We experimentally demonstrate departure from the commonly used 1D CSR theory for more extreme bunch length compression scenarios at the FERMI FEL facility. Better agreement is found between experimental data and the codes which account for the transverse extent of the bunch, particularly in more extreme compression scenarios.
The primary aim of this study is to determine if, during strong bunch compression, or for bunches with a large transverse-to-longitudinal aspect ratio, the limits of the 1D approximation could be found. This is achieved through comparing analytic results with simulation codes that incorporate the transverse bunch distribution, and with experimental data. The projected emittance of the electron beam was measured in parameter scans at the exit of the first bunch length compressor of the FERMI FEL  [8, 9]. This benchmarking study of CSR is accompanied by new insights on the CSR transient field at the edges of dipole magnets, which suggest novel compressor designs for the minimization of this instability. A new CSR feature of the General Particle Tracer (GPT)  [10]  tracking code was developed specifically for this study.
Numerical Validation In order to validate the analytical results of Sec. 2. 2 and 2. 3, we have numerically calculated the electromagnetic field distribution in an electron bunch in both the entrance and exit transient regimes using the GPT code  [10]. GPT is a particle tracking code that integrates the equations of motion of a large number of charged particles in the presence of electromagnetic fields. The code has the option to include the computation of the retarded Liénard-Wiechert fields of the tracked particles. Because this involves the storage of the trajectory of the particles and solution of retardation conditions, calculation of Liénard-Wiechert fields is computationally expensive. To reduce the computational cost, the GPT code does not evaluate the field of each tracked particle, but instead represents the particle bunch by a number of bunch slices (see Fig.
Simulation Setup Simulations of the FERMI injector (up to the exit of the first linac, see Fig. 10 ) have been produced using GPT. In order to accurately match the simulation to experimental conditions, the measured transverse and longitudinal profiles of the photoinjector laser were used as input parameters to the simulation (shown in Figs. 11  and 12 ), along with geometric wakefields from the injector linac. Full 3D space-charge effects were also included. The injector linac phase was optimised for minimal energy spread -as is done in the routine procedure of linac tuning -and good agreement was found between the simulated and experimentally measured bunch properties at the exit of the injector. From this injector simulation, the bunch was then tracked using the ELEGANT code  [17]  up to the entrance of BC1, including the effects of linac wakefields, the laser heater, which is a tool aimed to suppress the so-called microbunching instability that otherwise develops as the bunch propagates through the accelerator  [18, 19] , coherent synchrotron radiation and space charge models.