dataesr/scientific-paragraphs-categorization huggingface dataset:
The resulting binary maps were averaged to create a consensus map showing the proportion of models predicting any pixel as suitable  10. To visualise the variability of habitat suitability estimates according to the environmental datasets used to calibrate the models, we calculated the standard deviation for each pixel of the consensus maps (Supplementary Fig. S1 ). We calculated the proportion of area unanimously predicted as respectively suitable and non-suitable, and the proportion of area for which at least 95% of the models agreed upon (i. e. which value is lower than 0. 05 or higher than 0. 95) which will thereafter be referred to as "high agreement areas". All these results were computed considering the suitability of landmass (  1 ) worldwide (Supplementary Table  S2 ) and (  2 ) on a restricted focal area including most of Europe and the Mediterranean area, comprised between longitudes 20° W and 50° E and latitudes 27.
Potential distribution under future climate conditions. To assess future distributions, we relied on projections of the environmental variable values based on several GCMs developed by climate institutes  45. These numerical models can be run using different scenarios of greenhouse gases concentration changes called "Representative Concentration Pathways" (RCPs) and labelled after their radiative forcing value in 2100 (e. g. RCP2. 6 or RCP8. 5). To predict future distributions, we used future climate projections for 2050 (average for 2041-2060) and 2070 (average for 2061-2080). These estimates were obtained using 11 different GCMs obtained from the WorldClim database (version 1. 4) (Table  1 ), for 4 different RCPs: RCP2. 6, RCP4. 5, RCP6. 0 and RCP8. 5. We used the models selected in the second step (see "Results") to create a total of 12,320 models (combinations of 14 environmental datasets × 10 replicates × 4 RCP × 11 GCMs × 2 years).
The resulting maps (one by GCM) (Supplementary Fig. S5 ) finally displayed a negative value when the GCM considered predicted a lower suitability than the average of all GCMs and a positive value otherwise. We used the R software  46  and the following R packages to perform modelling and render the maps: dismo 47 , biomod2  48  , ENMeval  44  , cowplot  49  , ggplot2  50  , rnaturalearth  51  and raster  52.
Results SDM under current conditions. Out of the 126 environmental datasets tested in the first step of the analysis, none fulfilled the evaluation criteria for Xylosandrus crassiusculus, either due to lack of predictive power regarding European occurrences, low AUC (< 0. 8) or low TSS (< 0. 6) values. Consequently, we could not perform ecological niche modelling on this species, and the results presented hereafter only concern X. compactus. For this species, 14 datasets fulfilled the evaluation criteria of step 1 and were thus used in step 2. Supplementary Table  S4  shows a summary of the evaluation metrics for the models retained to perform the consensus map (Fig. 2 ). Under the current climate, 55% of the world surface was unanimously predicted as non-suitable, whereas 1. 13% was unanimously predicted as suitable.
Failing to include such peculiar populations might affect SDM outputs, although it is difficult to evaluate the magnitude of such effect. A last possible explanation for SDM failure could be related to variable selection, but this latter possibility appears very unlikely as we followed the same procedure as for X. compactus and created models with 126 variable datasets made of 11 environmental variables. Illustration of the variability between the GCMs used to create consensus maps using centered and standardised maps. These display a negative value when the GCM considered predicts a lower suitability than the average of all GCMs and a positive value otherwise. A and B correspond to two GCMs (MG and MI respectively) used to compute the RCP2. 6 maps for the year 2050, C and D represent two GCM (HD and MG respectively) used to compute the RCP8.
5 for the year 2070. The maps were generated using R 4. 0. 0 (https :// cran. r-proje ct. org/). Potential distribution of X. compactus under current climate. Correlative SDM uses relationships between environmental predictors and species' occurrences to predict its potential distribution. Therefore, unreliable information regarding X. compactus' distribution such as countries for which exact occurrence data were not mentioned, or localities where the species was detected but was not proved to have settled, had to be removed from datasets. Despite these precautions, our model successfully spotted suitable areas that were not initially included in the occurrence dataset (e. g. , in Central Africa and Oceania) for X. compactus. Similarly, the models predicted the Greek province of Peloponnese and the Balearic Island Mallorca, where it was reported as established after we stopped gathering occurrence data  30, 31  , which suggests that the models have good predictive power.
Acknowledgements We would like to thank  Giovanna Tropea ,  Jared Bernard ,  Andrew Johnson  and  You Li  for sharing their occurrence data with us. This work was supported by the  LIFE  project  SAMFIX (SAving Mediterranean Forests from Invasions of Xylosandrus beetles and associated pathogenic fungi ,  LIFE17 NAT/IT/000609 , https ://www. lifesamfix. eu/)which received funding from the  European Union 's  LIFE Nature and Biodiversity programme.
PsrPopPy2 In order to infer the underlying Galactic radio pulsar population in the presence of selection effects, we follow the example of  Pol et al. (2019)  and deploy the package PsrPopPy2. The package provides a wide range of tools for the simulation and modelling of the pulsar populations and their evolution as described in  Bates et al. (2014). The simulations are done within PsrPopPy2 creating a pulsar population with given pulsar parameters and then analysing their detection during one or many chosen model surveys that reflect the real-life observing campaigns that led to the observed population. In the "snapshot method", the aim is to determine  obs , the number of pulsars detected by a survey, depending on the total number of pulsars in the population  tot. The analysis is based on a Monte-Carlo-simulation: A number  tot of pulsars in the Galaxy is created, where each pulsar is given random parameters in luminosity and physical properties, drawn from a number of models distributions each (see  Bates et al.
2014). After that, the population is run through a simulated radio telescope survey. Based on the specific survey parameters encoded in the PsrPopPy2 framework, it is checked how many pulsars could have been detected on Earth, which is then used as follows. The goal is to obtain  of Eqn. 3 for each kind of pulsar. Populations with numbers of pulsars,  tot , varying between 10 and a few thousand are created and subjected to the simulated surveys, saving the number of observed pulsars,  obs. That yields a dataset, to which a Poisson curve (cf. Eq. 2) is fitted to obtain  for each  tot. With this information,  is calculated for each pulsar by a linear fit to all pairs of ( tot , ), following equation (3). The size of the expected galactic DNS (pulsar) population  real , i. e. the population size  tot , that yields one detection, is given by rearranging equation 3 and inserting  =  obs = 1.
2. Finally, in order to obtain a continuous intensity distribution as a function of  for our later modelling, we perform a spline interpolation. The lack of information about the main pulse's beam profile in the range of  MP ∈ [-5°, 5°] poses a major problem. We apply the LSQUnivariateSpline function from the Python package numpy. interpolate, because it returns a spline function with the option of setting explicit internal knots, so the edges can be shaped manually. The different shapes of the main pulse and the interpulse impose a slightly different interpolation process for both beams. Starting with the main pulse, the values for | MP | > 15°show short-term oscillations as discussed before (cf. Fig. 1 ). Hence, for this range we set grid points manually. It turns out that grid points at ±16°, ±17°, ±18°, ±19°and ±20°yield a stable spline that represents the shape of the profile.
In addition, the lack of data points for both main pulse and interpulse from 2009 to 2012 forces us to interpolate the resulting wide gap nearly linearly as first-order approximation. The real pulse shape may be different in this gap. As discussed previously, also the shape of the intensity distribution in the center of each pulse poses an uncertainty to our merger rate estimate for the population of PSR J1906+0746. A more accurate profile could lead to a larger illuminated fraction of the sky and thus to a small decrease of the DNS merger rate estimate. 1. 8 +7. 9 -1. 4.
12-7311-82, eBioscience), anti-TNF-α-PE-Cy7 (cat. 25-7321-82, eBioscience), anti-TNF-α-eFluor450 (cat. 48-7321-82, eBioscience FC was carried out with a Citoflex flow cytometer (Beckman Coulter) and data analyzed with Cytexpert software (Beckman Coulter). For the IFNγ ELIspot cells were plated at 4 × 10 5 and 2 × 10 5 cells/well in duplicate and spots were counted using an automated ELISPOT reader (Aelvis ELIspot reader, A. EL. VIS Gmbh, Germany).
Imaging and data analysis Particles assembled in the traps were imaged by bright field and fluorescence optical microscopy (Nikon Eclipse Ti). ImageJ  26  was used to merge the fluorescence images, and to overlay fluorescence and bright-field images by combining the red, green, and phase contrast channels. The yield of the assembly steps was quantified by particle localization and cluster analysis using a custom-made routine implemented in Matlab. Microscopy images of a target region of the template containing >50 000 traps were taken after each deposition step with fluorescence optical microscopy using either the red or the green channel to discern particles assembled during each step. Particles of different fluorescence were then located in the different images using Matlab code to calculate the position of centroids of bright spots to sub-pixel accuracy.
27, 28  The particle coordinates were then tagged and collated according to the fluorescence channel used to acquire the image. The Matlab routine rangesearch was used to construct the list of nearest neighbours of each particle based on a cut-off Euclidean distance of r ≤ 1. 4σ, where σ is the diameter of the particles. The list of nearest neighbours was then used to find and classify clusters of particles separated by distances less than r.
This technique achieves a yield up to 95% for each individual step, where the yield is defined by the percentage of the traps on the template where the desired number of particles is successfully deposited. For each experiment, the yield was quantified by imaging at 40× magnification, using both bright-field and fluorescence microscopy, and automatically counting particles with a custom-written Matlab software. The yields and the flexibility in the designs reported below are on par with what can be achieved by conventional sCAPA in an open chip. 14, 15  We started by creating particle dimers by running the microfluidic sCAPA process twice, sequentially. We first injected a colloidal suspension of green fluorescent polystyrene particles with diameter of 2 μm through the inlet into the central section of the channel, using a manually operated syringe, until the template was fully covered.
Acknowledgements The authors acknowledge support from an  ETH Research  Grant  ETH-15 17-1  (R. S. ), from an  ETH  Postdoctoral Fellow-ship  FEL-21 15-2  and  SNSF PRIMA  Grant  179834  (E. S. ), from a  Postdoctoral fellowships  programme " Beatriu de Pinós ", funded by the  Secretary of Universities and Research (Government of Catalonia)  and by the  Horizon 2020 programme of research and innovation  of the  European Union  under the  Marie Sklodwoska-Curie  grant agreement no. 801370  (Grant  2018 BP 00029 ) (M. A. F. R. ) and from a  Gordon and Betty Moore Foundation   Investigator Award on Aquatic Microbial Symbiosis  (grant  GBMF9197 ) (R. S. ). The authors thank  Dr. Heiko Wolf  at  IBM Research Zurich  for insightful discussions.
(2014). The APSIM software platform links modules of different crops (selected on a plug-in/plug-out basis, including crop mixtures) with common modules of soil water balance, surface organic matter, soil N and C balances (P optional), crop and soil management, and weather input and output/reporting. APSIM includes crop modules for many crops, including maize, sorghum, millet, wheat, peanut, and rice as simulated in this project. The crop modules (both C-3 and C-4) share a common template for crop development and biomass accumulation and partitioning, although APSIM-Maize (derived from CERES-Maize with modifications for tropical conditions  (Carberry et al. , 1989) ) and APSIM-Wheat have yet to be standardized (but conform to the template in terms of their growth and development processes).
Soil evaporation in APSIM uses the  Priestley and Taylor (1972)  approach to estimate potential atmospheric demand, adjusted for cover conditions of canopy and surface residues, and the  Ritchie (1985)  two-stage soil evaporation model to determine actual Es. Transpiration is based on the transpiration efficiency (TE) approach. The TE method computes daily transpiration based on daily dry matter gain (from RUE module) multiplied by TE. The TE is a species-dependent function of VPD and CO 2 that operates on daily VPD to estimate crop water demand. The capabilities of APSIM to simulate CO 2 effects on C-3 crop growth have been tested empirically with APSIM-Wheat and applied for all C-3 crops in APSIM as reported by Van Uytrecht and  Thorburn (2017). Effects of the increasing levels of CO 2 are captured by modifiers to RUE, TE, and a reduction in N stress on photosynthesis using look-up functions.
The same modifier coefficients and CO 2 effects as used for wheat are employed for all the APSIM C-3 crop modules in this project, except for APSIM-Maize and APSIM-Sorghum, for which CO 2 does not modify RUE. APSIM-ORYZA uses leaf-level photosynthesis, which is sensitive to CO 2 at the leaf level  (Bouman et al. , 2001; Bouman and van Laar, 2006).
Introduction to DSSAT crop models The DSSAT software Version 4. 5. 1. 023  (Hoogenboom et al. , 2015; www. dssat. net)  includes more than 40 crop models which share the same soil water balance, same soil N balance, and same soil C balance modules (in that respect, the module approach is very similar to APSIM). The CERES-Maize, CERES-Sorghum, CERES-Millet, CERES-Wheat, CERES-Rice, and CROPGRO-Peanut models were used in this project. The DSSAT models are described by  Jones et al. (2003)  and related papers. The CERES-style models use the RUE approach, based on the fraction of light intercepted, RUE, and modifiers of RUE (depending on temperature and CO 2 ; see  Boote et al. (2010)  for a description of the CO 2 modifier on RUE for CERES-style C-3 and C4 crops in DSSAT). The CROPGRO models in DSSAT use leaf-level photosynthesis (based on rubisco kinetics theory) scaled up to canopy assimilation  (Boote and Pickering, 1994; Pickering et al.
Experimental data for regions and calibration for distributions of yields within farm surveys The regional teams in West Africa, East Africa, South Africa, Southeast Africa, Pakistan, and South India obtained farm survey yield data for selected crops from households in their regions, and matched this with available farm management information, historical weather, soil information, and local cultivars (calibrated from experiments in their regions). Unfortunately, we were lacking knowledge of initial conditions for all survey yield fields including initial inorganic N and soil water status, and prior crop residue, all of which influence yield levels via N supply and water supply, especially for low-input farming systems. Furthermore, somewhat generic soils for the sites were used rather than actual observed soil characteristics.
Evaluation of Model Sensitivities to CO 2 , Temperature, Rainfall, and N Factors The teams selected representative farms from the "mid-range" within the distribution of farm yields on which to evaluate DSSAT and APSIM model simulations for response to CTWN. The sensitivity ranges for CTWN were 360, 450, 540, 630, and 720 ppm for CO 2 ; -2 • C, ambient, +2 • C, +4 • C, +6 • C, and +8 • C for air temperature; 25%, 50%, 75%, 100%, 125%, 150%,  175%, and 200% ambient for rainfall; and 0, 30, 60, 90, 120, 150, 180, and 210  kg N ha -1 of applied N, all done as single-factor responses (limits set following  Ruane et al. , 2014). Model simulations were conducted for 30-year historical records (historical weather if available or the AgMERRA climate forcing dataset;  Ruane et al. , 2015). Then, the means of the 30-year results were computed and reported in the graphs that show the responses to CTWN for APSIM and DSSAT.
Proceedings of Challenge Program for For the infertile sites in West Africa, East Africa, and Southeast Africa, simulated maizef yield was often somewhat reduced when rainfall was increased above ambient (100% case), which in the models is attributed to the leaching of mineralized N from the soil and loss of N for the maize crop uptake (see Fig. 8(a) , example for medium-potential zone in Kenya). This N-leaching effect, e. g. , a reduced yield at higher rainfall under no N fertilization, was repeated for millet in West Africa as well (Fig. 9(a) ). In the Republic of South Africa, where rainfall is lower but soils more fertile (and with higher N fertilization), the maize yield increases strongly with increased rainfall (see Fig. 8(b) ). We had expected to see differences between APSIM and DSSAT because of the differences in transpiration methodology (APSIM using the TE method, and DSSAT using the Priestley-Taylor method).
The original CERES-Maize prior to 2000 did have elevated temperature effects on RUE and RGFIL in the source code, but during the "modularization era" the coefficients were removed to become external "read-in" parameters, that were not correctly re-parameterized. At that time, there were few existing studies at elevated temperature on maize for  parameterizing these functions. Since then, experiments at elevated temperature have been conducted by  Rattalino-Edreira et al. (2011 ), Lizaso et al. (2017 , 2018) , and others. In addition, the two models have different soil organic carbon modules, with different assumptions about the pools of SOM available for N mineralization and different temperature parameterizations of that process. The temperature parameterization of soil organic C decomposition in APSIM is carried over from older versions of CERES-Maize that used DSSAT's Godwin-Papran function  (Godwin and Singh, 1998).
However, the DSSAT-CERES-Maize for all the DFID-funded simulations used the CENTURY soil C module that has a different temperature parameterization from APSIM and different also from the DSSAT's Godwin-Papran function. For additional information, see  Bassu et al. (2014)  for intercomparison of multiple maize models for sensitivity response of yield to temperature, CO 2 , and rainfall. In general, rising temperature (2 • C, 4 • C, 6 • C, or 8 • C above ambient in CTWN) reduced the yield for both maize models at most sites including South India (Fig. 11 ), consistent with a shorter crop life cycle, a shorter grain-filling duration, and a small reduction in RUE. In addition, there is a reduction in grain growth rate at high temperatures for both models, but the DSSAT-CERES-Maize model has a stronger reduction in grain growth rate (RGFIL in Table  1 ), thus causing the model to be more sensitive than APSIM-Maize to high temperature.
The RUE effect is minor in part because the mean daytime temperature is rarely above 33 • C, except at the high end of the temperature sensitivity response. There is one additional causal factor, which is that the two models have different temperature parameterizations for soil organic C mineralization. APSIM uses its own soil organic C mineralization equations, whereas DSSAT in these studies used the CENTURY organic C module. The two SOC modules have different temperature functions.
This chapter outlines the utility of data pipelines modeling in the context of a data driven project and enumerates metrics for evaluating the quality of the data modeling regarding the readability and the comprehensibility of the models. We start with explaining the challenges surrounding the DataBio project that led to the adoption of data pipelines modeling using the Enterprise Architecture language ArchiMate. Then we present the data modeling process with examples from DataBio pilot studies starting with modeling software components provided by project stakeholders and ending up with integration of components into data pipelines that achieve the data analytics lifecycle intended by the pilot study. We end the chapter with the evaluation of the quality of DataBio data pipelines models with metrics collected by a monitoring tool for ArchiMate models.
Introduction DataBio  [1]  aims to develop a platform that exploits the potential of big data technologies in the domains of agriculture, fishery and forestry. Given the complexity of the task, the project decided to adopt the "Enterprise Architecture" modelling language "ArchiMate 3. 0"  [2, 3]  as a common modelling framework for representing the requirements of the pilots and modelling the technical architecture of the components, thus facilitating communication and comprehension among partners. Most of the software components interact with data from different origins and with various formats such as satellite imagery, sensors data, geospatial data (see Chap. 4), etc. In each pilot, components are connected together through several interfaces to form a data pipeline, (see Chap. 1) in which each component has a specific function in the data value chain such as data collecting, data processing, data analytics and visualization.
Modelling Data Pipelines The Enterprise Architecture language ArchiMate provides several concepts for modelling the different layers of the enterprise: • The physical layer contains the devices and their connections, which are used in the deployment of the IT system. • The application layer contains the software services and the data flow. • The business layer contains business services, interfaces and actors. The modelling of software components enabled the DataBio partners working in the various pilots to easily understand the underlying functioning of each pilot. At first, partners were asked to provide models for the software components that they have provided. In a second time round, the partners were instructed to provide data pipelines diagrams that highlight the integration of the components in each pilot study.
Modelling Software Components The project developed a naming convention, where each software component has an identifier with the pattern "Cxx. yy" where "C" refers to the word "Component", "xx" represents the number of the partner that had provided the component and "yy" represents the component number of that partner. Datasets are correspondingly expressed with the template "Dxx. yy", For example, "C16. 01" denotes the first component from partner 16, which is VTT. An expanded notation is "C16. 01: OpenVA (VTT)", as the component is called OpenVA, which is a platform that consists of software modules that are used as building blocks of web based visualisation and analytics applications  [6]. Components are modelled with diagrams that follow a predefined template. These diagrams include deployment view, interfaces view and subordinates view.