dataesr/scientific-paragraphs-categorization huggingface dataset:
Dynamic response and temperature acclimation of respiration Due to the changing environmental conditions, photosynthesis and respiration acclimation and dynamic response of plants could affect the carbon exchange rates, but this mechanism is missing from many biogeochemical models  (Smith and Dukes, 2012). In order to implement acclimation and short-term temperature dependence of maintenance respiration in BBGCMuSo, the respiration module was modified in two steps. The maintenance respiration in Biome-BGC is based on a constant Q 10 factor. In our approach, first the constant Q 10 value was modified based on  Tjoelker et al. (2001)  who proposed an equation for the short-term temperature dependence of the Q 10 factor (dynamic response), and showed how this relationship could improve the accuracy of the modeled respiration.
Optimization of the HSGSI driving parameters might resolve this issue. Model evaluation results of the Bugac site using the original Biome-BGC and the BBGCMuSo. The statistical indicators show that with the exception of SWC, the simulation quality typically improved with BBGCMuSo. BBGCMuSosimulated LHF has higher bias (in absolute sense) than the original model, but the other error metrics perform better. It is notable that NSE became positive for GPP, TER, and LHF while it was negative for all three variables with the original Biome-BGC. The SWC estimation by the original model was closer to the observations than the result with BBGCMuSo. However, as the original Biome-BGC has a simple, one-layer bucket module for soil hydrology, SWC estimation by the original model and topsoil SWC simulation by Biome-BGCMuSo are not comparable.
Values of NSE are close to 1. 0 using BBGCMuSo, indicating a good match of the modeled values and the observed data. For the original model, NSE values are much smaller or negative. NSE is negative for SWC both for the original and the BBGCMuSo version in spite of the lower bias of the latter. Again, due to the very simplistic SWC module of the original model, its SWC results are not directly comparable with observations. The Supplement Sect. S3 presents additional simulation results for the Mead1 site, demonstrating the effect of new model developments like irrigation fertilization, and the genetically programmed leaf senescence.
The management modules built into BBGCMuSo cover, on a global basis, the majority of human interventions in managed ecosystems. As management practices require prescribed input data for BBGCMuSo, accuracy of the simulations will clearly depend on the quality of management description which calls for proper data to drive the model. Representation of acclimation of plant respiration and photosynthesis was shown to be one major uncertainty in global biogeochemical models  (Lombardozzi et al. , 2015). We introduced acclimation of autotrophic respiration in BBGCMuSo, which is a major step towards more realistic climate-change-related simulations. Representation of photosynthesis acclimation  (Medlyn et al. , 2002)  is needed in future modifications as another major development. Due the implementation of many novel model features, the model logic became more complex.
, 2012). To address this problem we created a computer-based open infrastructure based on the workflow concept that can help a wide array of users in the application of the new model. The infrastructure is similar to PEcAn  (Dietze et al. , 2014)  which is a collection of modules in a workflow that uses the Ecosystem Demography (ED v2. 2) model. We followed the concept of model-data fusion (MDF;  Williams et al. , 2009)  when developing the so-called "Biome-BGC Projects Database and Management System" (BBGCDB; http://ecos. okologia. mta. hu/bbgcdb/) and the BioVeL Portal (http://portal. biovel. eu) as a virtual research environment and collaborative tool. BBGCDB and the BioVeL portal help sen-sitivity analysis and parameter optimization of BBGCMuSo by a computer cluster-based Monte Carlo experiment and GLUE methodology  (Beven and Binley, 1992).
Further integration is planned using alternative Bayesian calibration algorithms  (Hartig et al. , 2012)  that will provide extended calibration options to the parameters of BBGCMuSo. We would like to support BBGCMuSo application for the wider scientific community as much as possible. For users of the original Biome-BGC, the question is of course the following: should I move to BBGCMuSo? Does it need a long learning process to move to the improved model? We would like to stress that users of the original Biome-BGC will have a smooth transition to BBGCMuSo, as we mainly preserved the structure of the input files. The majority of changes are related to the seven-layer soil module and to the implementation of management modules. The ecophysiological parameter file is similar to the original but it was extended considerably.
Code availability The source code, the Windows model executable, sample simulation input files, and documentation are available at the BBGCMuSo website (http://nimbus. elte. hu/bbgc). The source code is also available at GitHub (https://github. com/ bpbond/Biome-BGC/tree/Biome-BGCMuSo_v4. 0). The Supplement related to this article is available online at doi:10. 5194/gmd-9-4405-2016-supplement.
Localised, temporally resolved data -Cefas-SmartBuoy and MARNET Cefas operates a network of SmartBuoys to provide autonomous in situ measurements of physical, chemical and biological parameters  (Mills et al. , 2005). A SmartBuoy was located directly north of the Dogger Bank ("North Dogger") at 55 • 41 N, 2 • 16. 80 E (see Fig. 2 , region 2) between 24 February 2007 to 15 September 2008 in 85 m water depth  (Greenwood et al. , 2010). O 2 concentrations were continuously recorded with a frequency of 5 Hz at 31 and 85 m. These autonomous O 2 measurements were corrected for F. Große et al. : Looking beyond stratification drift using O 2 concentrations determined from discrete water samples to give an accuracy of 0. 5 %  (Greenwood et al. , 2010). For validation purposes the O 2 data derived from the sensor at 85 m depth were used.
North Dogger data are published and can be accessed according to  Greenwood et al. (2016). The BSH operates a continuous monitoring station at 54 • 10 N, 6 • 21 E (see Fig. 2 , region 1; hereafter referred to as station "Ems"). The O 2 saturation is measured hourly using opto-chemical sensors (optodes). Sensors are located in 6 and 30 m depth, respectively, and the bottom depth is 33 m. The applied sensors have a resolution of 0. 03 mg O 2 L -1 and an accuracy better than 0. 26 mg O 2 L -1. Before deploying the sensors a 0-100 % calibration is conducted, and they are re-calibrated after operation to quantify any drift. In addition, a regular on-site validation takes place using a calibrated fast optode (accuracy of ±2 %) or by applying the Winkler titration (accuracy better than ±1 %).
Spatially resolved "snapshot" data -the North Sea programme During the North Sea programme, carried out by the Royal Netherlands Institute for Sea Research (NIOZ) with support from the Dutch Science Foundation (NWO) and the European Union, the North Sea was sampled from 18 August to 13 September 2001, and from 17 August to 5 September 2005 and 2008. The North Sea was covered by an approximate 1 • × 1 • grid, sampling approximately 90 stations in each of the years  (Bozec et al. , 2005 (Bozec et al. , , 2006;; Salt et al. , 2013). During each cruise, a total of 750 water samples were collected for dissolved O 2. In 2001, the O 2 concentrations were determined by the Winkler titration using a potentiometric end-point determination with an accuracy of ±2 µmol O 2 kg -1 (less than ±0. 07 mg O 2 L -1 depending on T and S).
In 2005 and 2008, the O 2 concentrations were obtained applying the spectrophotometric Winkler approach with a precision of less than 0. 03 mg O 2 L -1. A detailed description of the measurement system used is given in  Reinthaler et al. (2006). The data for the years 2001 and 2005 have been published and can be accessed according to  Thomas et al. (2012)  and  Thomas and Borges (2012) , respectively. The data available were gridded to the model grid (Fig. 2 ). In the case of multiple measurements for the same model grid cell and date, the average of these measurements was used for validation. To compare our model results to these data, we calculated the averages and standard deviations of our simulation over the observation period of the corresponding year.
Acknowledgements. We would like to thank  Sonja van Leeuwen  from  Cefas  for providing updated data on freshwater and nutrient loads for the major rivers across Europe. We further thank  Jerzy Bartnicki  for providing atmospheric nitrogen deposition data from the European Monitoring and Evaluation Programme (EMEP). We thank  Dilek Topcu  and  Uwe Brockmann  from the  University of Hamburg  for providing the map of observed O 2 deficiency in the North Sea. Furthermore, we thank the editor  Veronique Garçon  and two anonymous referees for the valuable comments and constructive criticism, which helped to significantly improve the manuscript. The model simulation was conducted on Blizzard, the IBM Power6 mainframe at the  German Climate Computing Centre (DKRZ) in Hamburg. The North Sea sampling in 2001, 2005 and 2008 was supported by the  Dutch Science Foundation (NWO) ,  CARBOOCEAN  ( EU-FP6 ) and the  Royal Netherlands Institute for Sea Research (NIOZ).
Cefas SmartBuoy data were collected under the  UK Department for Environment, Food and Rural Affairs (Defra)  contract ME3205 ( Marine Ecosystems Connections: essential indicators of healthy, productive and biologically diverse seas). Markus Kreus  was partly financially supported by the  Cluster of Excellence "CliSAP"  ( EXC177 ),  University of Hamburg , funded by the  German Science Foundation (DFG). This study was supported by the  German Environmental Protection Agency (UBA) in Dessau , in the frame of the project " Implementation of Descriptor 5 Eutrophication to the MSFD ", SN:  3713225221. The publication costs were covered by  Thomas Ludwig  ( Scientific Computing, University of Hamburg ) and "CliSAP". Edited by:  V. Garçon.
Data availability The time series data from the Cefas station North Dogger can be accessed via the Cefas Data Hub (https://www. cefas. co. uk/cefas-data-hub/) according to  Greenwood et al. (2016). The time series data of MARNET station Ems can be retrieved from the Deutsches Ozeanographisches Datenzentrum (DOD Data Centre) via email query to dod@bsh. de. The spatially resolved data from the North Sea cruises in 2001 and 2005 have been released in the framework of the EU-FP6 project CARBOOCEAN. These data can be accessed via the CARBOOCEAN data portal (http://dataportal. carboocean. org/) according to Thomas et al. (2012)  and Thomas and Borges (2012), respectively. The data of the North Sea cruise 2008 (R/V Pelagia 64PE294; Zemmelink,  2008)  have not been published, yet, but can be requested via the CODIS data portal (http://www.
Automation is beginning to transform the way data are collected in almost all scientific disciplines. The combination of robotics and software now allows data to be collected consistently and reproducibly, eliminating human error and boredom. This approach has been applied to macromolecular crystallography at MASSIF-1, a fully automated beamline at the European Synchrotron Radiation Facility (ESRF). Considerable human effort is still dedicated to evaluating protein crystals in order to find the few crystals that diffract well or collecting hundreds of data sets to screen potential new drug candidates. The combination of ESRF-developed robotic sample handling and advanced software protocols now provides a new tool to structural biologists. Not only is the beamline used efficiently, running 24 h a day without getting tired, data collection is also performed consistently by an expert system, often better than with a human operator.
However, the advent of pixel array detectors (PADs) dramatically changed the use of synchrotron beamlines. [43]  In addition to the enormous benefits of zero readout noise, the continuous readout ability of PADs allows the collection of data sets in the order of minutes, effectively eliminating the need for pre-screening. While this changed the focus of the beamlines, it has enabled them to become much more effective instruments than originally proposed. The increased throughput enabled by PADs does not remove the need to test the diffraction quality of crystals and also allows the collection of hundreds of data sets in the allocated beamtime. The focus of the MASSIF beamlines was therefore shifted to cover full automation of both screening and data collection using the capabilities of PADs.
The impact of automation: a year in crystals After just over a year of operation the beamline has processed over 15,000 crystals. Of these, 9872 were handled in 2015. Here, we have used the 2015 samples as the basis for an analysis of the impact MASSIF-1 has had on how data are collected and their quality. This cohort comes from a wide variety of projects from across the European structural biology community (Figure  2 ) and represents a reasonable snapshot of crystallographic projects. MASSIF-1 is used primarily for two types of project: those involving a large amount of screening to find the one crystal out of many that diffracts sufficiently and projects where large numbers of repetitive data collections are required, such as fragment screening campaigns. The full automation of data collection not only frees researcher's' time but also allows an expert system to make crucial decisions in the process using a large amount of information not usually used by human operators.
The robot does not get tired The most obvious benefit of a completely autonomous system is that it can run day and night without getting tired. This is particularly useful at synchrotron sources where the facilities are scheduled for 24 h of continuous operation. As MASSIF-1 runs automatically, all beamtime can be used efficiently without the need for users or beamline scientists to work overnight. This is demonstrated by analysing the time that data collections are initiated on MASSIF-1 compared to the human-operated beamlines at the ESRF in 2015 (Figure  3 ). The most obvious trend on MASSIF-1 is that data collection rates remain steady regardless of time of day (Figure  3(a) ). Fewer data collections are launched after 21:00 as this is the time when 100 crystals are completed, a common number received at the beamline.
How is the beamline used? The nearly 10,000 crystals processed on MASSIF-1 in 2015 represent a wide variety of projects composed of 180 different proteins. An analysis of the fate of all the samples received in 2015 is informative (Figure  7(a) ). Almost all of the crystals received were examined; a small proportion could not be mounted. The crystals that were processed are equally split into three categories: a third contained empty loops or diffraction was absent; a third diffracted but were of poor quality and the final third yielded a processed data set (Figure  7 (a)). Only 23% of crystals received could be indexed successfully by EDNA, showing that the beamline is used heavily for screening as well as for established projects. The collection of a default data set of 180°where indexing fails is also shown to be a useful procedure: for 678 crystals that could not be indexed, the default data set collected could be processed.
The impact of MASSIF-1 on data collection at other beamlines The technology developed for MASSIF has been directly exported to all the ESRF MX beamlines and has been extended into a wide variety of complex experiments. It was clear during the development of the instrumentation that the beamline would be highly dependent on on-line data analysis. This was pioneered at the ESRF MX beamlines  [57]  with the development of software for automatic indexing and strategy calculation such as DNA, BEST and EDNA. [13, 14, 17, 58]  However, it became clear that for MASSIF to succeed, the analysis had to be taken a step further to allow the results of analysis to be used in further experiments involving instrument control. These experiments pose a challenge for both hardware (sample changers, goniometers) and software (data acquisition and data analysis).
A classic example of this need is crystal reorientation, where characterization images must be collected in order to determine the orientation of the crystal, the goniometer κ angle must be moved and then the crystal re-characterized to determine the data collection strategy. [59]  X-ray centring in particular clearly requires analysis coupled to instrument control. In order to automate such experiments a high-level software environment is required to coordinate data acquisition and data analysis. We selected a workflow manager that allowed the first steps to be taken in the coupling of on-line data analysis to instrument control. [60]  The workflow manager allows complex workflows to be designed as modules that are independent from beamline control software and hardware. Workflows are then started by the beamline control GUI MXCuBE2  [61]  that connects to a dedicated workflow server via a web-service call.
The workflow then requests operations, such as data collections and motor movements, by connecting back to MXCuBE2 through an XML-RPC server. As each workflow is a module, they can be easily combined into more complex workflows, as was the case for MASSIF-1, where multiple processes such as centring, characterization and data collection are combined. The modules can also be easily exported to the other beamlines. This is the case for X-ray centring,  [40]  which is the most popular workflow run by users present at the beamline (Figure  8 ). Workflows have been expanded to cover a wide range of experiments such as dehydration of protein crystals,  [62]  kappa goniometer reorientations,  [59]  serial crystallography methods,  [63]  automatic strategies to determine the radiation sensitivity of crystals  [64]  and even solution scattering experiments.
Conclusions The full automation of MX data collection is not just an exercise in efficiency but can also lead to better data by consistently selecting the best region or regions of crystals and using the information gathered to collect the best data possible from each sample. As the system is not subject to human weaknesses such as fatigue, emotional involvement with specific samples or the need to visualize samples, it is a powerful tool for the modern structural biologist. The scientist remains deeply involved through the ability to define specific experiment requirements via a database  [66]  and the availability of results in real time. Full automation also allows new data archiving policies to be implemented smoothly. The beamline collects all necessary metadata for complete data sets and this will allow us to automatically archive raw diffraction images for data sets.
Acknowledgements The authors would like to thank the support groups at the  ESRF  that provide outstanding services that allow complex beamlines to be constructed and maintained. Matias Guijarro ,  Pascal Theveneau  and the technicians of the Structural Biology Group were instrumental in the construction of the beamline and are thanked for their invaluable contributions. The  ESRF-EMBL Joint Structural Biology Group  and the  ESRF Automation Task Force (ATF)  supported the developments.
Coherence resilience assays In order to test our approach Polysaccaride Lyase (PL) families of the CAZy  [21]  database were used as a study case. The protein (module) families in this database are organized into 5 different classes: Glycoside Hydrolases (GH), GlycosylTransferases (GT), Carbohydrate Esterases (CE), Polysaccharide Lyases (PL) and Carbohydrate Binding-Modules (CBM). The CAZy database version (c7-2011) that we used in our analysis has about 138,000 distinct UniProt identifiers distributed through the families in these classes as shown in Table  2. The performed assessments were limited to using the UniProt identifiers in those families because of their direct mapping to GO term annotations. Thus, for the annotation mapping we have used the GOA  [22]  annotation files (version 2013-03).
Within the CAZy database the PL class is the one that is better characterized by the Glycobiology community, in part due to its more tractable dimensions, as can be seen in Table  3. For this reason we also have elected to perform our assays using this class of families. We have run the coherence resilience assays that we describe below only for families PL1 to P12, PL16, PL17 and PL22 because these are the only ones that met the minimal size requirement for our assaying. In order to perform our assays we subjected each of these families (sets) to a degeneration procedure as illustrated by Fig. 2  where x % proteins in an original protein set are replaced by random proteins. In our assay these protein replacements were obtained randomly from the remainder of the CAZy families. The degeneration procedure was applied in discrete levels of 10 % protein replacement (from 0 % up to 100 % protein replacement) to each of the sets.
The goal is to capture protein subsets within families (or other functionally related sets) that characterize that family (or set), which can subsequently be used for annotation extension for potentially sub-annotated proteins within the same family (or set). The proposed approach is modular and can be integrated with other annotation methodologies mostly as a pre-processing step. In the future, we will be implementing both mUI and mGIC (along with other) metrics into our web application GRYFUN. This will more easily capture the annotation functional cores in protein sets and pipeline them to a custom annotation extension module based on HMM profiles that we are currently developing.
Acknowledgements This work was supported by the  Portuguese Fundação para a Ciência e Tecnologia  through a  PhD research grant  [PhD grant ref. SFRH/BD/48035/ 2008  to H. P. B] and three research centers Strategic Projects funding [project  UID/MAT/00006/2013  (CEAUL) to L. S. &  UID/MULTI/04046/2013 (BioISI) to L. A. C. & UID/CEC/00408/2013  (LaSIGE) to H. P. B. and F. M. C. ] and by the  European Commission  [ BiobankCloud  project under the  Seventh Framework Programme  grant # 317871  to F. M. C. ]. Furthermore we like to thank  Faculdade de Ciências da Universidade de Lisboa  for the tuition support for H. P. B.
Discrete Element Method In this research, the open-source software Yade-DEM is used to generate different particle packings. Yade-DEM is based on the Discrete Element Method (DEM)  Šmilauer et al. (2015). DEM simulates the movement of individual particles inside a packing made of a large number of particles  (Cundall and Strack 1979). For each and every particle, a force balance is written, accounting for the effects of gravity, boundary conditions, and the contact mechanics between particles. Each particle has its own mechanical properties such as Young's modulus (E), Poisson ratio (ν), and friction angle (ϕ) as well as a radius (R) and density. Regarding the contact mechanics between two particles, consider a particle i in contact with a neighbouring particle j. Particles i and j are pushed towards each other due to their surrounding particles.
Acknowledgments The first author gratefully acknowledges financial support from the  Technology Foundation STW , the technological branch of the  Netherlands Organisation of Scientific Research, NWO , and the  Dutch ministry of Economic Affairs  under contract no. 12538 , entitled Interfacial effects in ionized media. The second and third authors would like to thank  European Research Council  for the support they have received under the  ERC Grant Agreement  no. 341225. The fourth author acknowledges the CRCT granted by  Grenoble INP  in 2012 which initiated this joint research. The authors are thankful for the valuable comments by three anonymous referees, which contributed to improving the manuscript significantly.