dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Data resources and AP2/ERF superfamily transcription factor identification in sesame AP2/ERF genes and proteins sequences of Arabidopsis thaliana, Vitis vinifera, Solanum lycopersicum, Solanum tuberosum and Utricularia gibba were downloaded from the Plant Transcription Factor DataBase (http:// planttfdb. cbi. pku. edu. cn/) [41]. In addition, the sesame genome and proteome were downloaded from the Sinbase (http://ocri-genomics. org/Sinbase/) [60]. The phylogeny data of the six species were downloaded from NCBI Taxonomy common tree (http://www. ncbi. nlm. nih. gov/ Taxonomy/CommonTree/wwwcmt. cgi). The Hidden Markov Model (HMM) profile of the AP2/ERF domain (PF00847) was obtained from Pfam v28. 0 database (http://pfam. xfam. org/) [61]  and searched against the sesame proteome using Unipro UGENE  [62].
The amino acid sequences of the 15 motifs identified by MEME Suite were searched on Pfam database to find out the AP2/ERF motifs and their sequences logo were generated. Alignment, phylogenetic analysis and identification of microsatellite markers in sesame AP2/ERF genes A single alignment of sesame AP2/ERF domain sequences and a multiple alignment analysis of the amino acid sequences of the AP2/ERF genes in sesame, Arabidopsis, grape, tomato, potato and Utricularia gibba were conducted using the Clustal W program built in the MEGA 6. 0 software  [67]  with a gap open penalty of 10 and gap extension penalty of 0. 2. Alignments were displayed using BoxShade (http://www. ch. embnet. org/software/ BOX_form. html) (Additional file 8) and un-rooted Maximum-Likelihood (ML) trees were constructed in MEGA 6.
0 software with a 1000 bootstrap value. Combining the phylogenetic trees with the conserved domain analysis, the AP2/ERF genes in sesame were classified into several subfamilies and groups according to  [18]. Furthermore, the web based software Websat (http:// wsmartins. net/websat/) [68]  was used to identify simple sequence repeats (SSRs) in the predicted 132 AP2/ERF genes in sesame with the following parameters: two to six nucleotide motifs were considered, and the minimum repeat unit was defined as five reiterations for dinucleotides and four reiterations for other repeat units. Comparative mapping of orthologous AP2/ERF genes in sesame, Arabidopsis, tomato and grape The amino acid sequences of the predicted AP2/ERF proteins were BLASTp searched against protein sequences of Arabidopsis, tomato and grape in NCBI.
Tissue-specific expression profiling using RNA-seq and qRT-PCR analysis of AP2/ERF genes under drought stress To analyze the expression patterns of AP2/ERF genes in sesame, different transcriptome data from root, stem tip, and leaf previously obtained by our group were used. These data were downloaded from SesameFG (http:// www. ncgr. ac. cn/SesameFG). The analysis were performed using Cluster3. 0 (http://bonsai. hgc. jp/~mdehoon/software/ cluster/software. htm), and reads per kilobase per million mapped reads (RPKM) values for each gene in all the tissue samples were log10 transformed. Finally, a heat map was generated by Multi Experiment Viewer (MEV)  [71].
An open-source database containing metrology data for X-ray mirrors is presented. It makes available metrology data (mirror heights and slopes profiles) that can be used with simulation tools for calculating the effects of optical surface errors in the performances of an optical instrument, such as a synchrotron beamline. A typical case is the degradation of the intensity profile at the focal position in a beamline due to mirror surface errors. This database for metrology (DABAM) aims to provide to the users of simulation tools the data of real mirrors. The data included in the database are described in this paper, with details of how the mirror parameters are stored. An accompanying software is provided to allow simple access and processing of these data, calculate the most usual statistical parameters, and also include the option of creating input files for most used simulation codes.
Simulations require the availability of the measured data, prepared with suitable preprocessing (e. g. detrending the main profile) and reformatted for being read by the simulation tool. Here, we propose to share some of the data stored in our metrology laboratories and make them available to the beamline designer. The idea is to facilitate the task of finding good data for modeling mirror errors in a realistic way to make accurate predictions for the planned optics, and compare performances of configurations with different surface errors. Although the idea of sharing metrology data in a common database is not new and has been floating in the metrology community, it was never completely implemented because of lack of manpower and coordination requiring a common and parallel effort from many laboratories.
The DABAM database 2. 1. What is stored in the database: data and metadata In addition to the obvious necessity of storing the mirror profiles (heights or slopes) in the database, it is important to consider how the data, and which additional information, should be made available. The data needed for optics simulations is the mirror heights profile, usually with the main curvature removed (circular or elliptic profile) which permits to simulate and study separately the effect of surface errors and the focusing parameters (radius, ellipse semi-axes, etc. ). It is important to note whether the metrology was a full area measurement or a single line scan measurement. At this point all the profiles stored in DABAM are single line scans. The storage of full area measurements in the database is left for a future upgrade.
Therefore, the question for the proposed database is whether we include the raw data as measured (usually slopes) or the detrended profile provided by the laboratory that provides the profile data. The raw data permit an advanced user to make the same analysis on different profiles and in this way better compare the effects. The detrended profile simplifies the use for a non-expert, who is not concerned with the technicalities of the data preparation. We decided, therefore, to store both raw and detrended data when available. The next topic is to define which information should be available for every profile. This is the metadata. This should include a minimum set of values defining the important parameters in three families: the physical and manufacturing aspects of the mirror, the measurement and the data analysis, and results.
There are two main finishing approaches: full area polishing and small area polishing. This is basically the size of the polishing tool. Full area refers to the entire polished surface to always be in contact with a polishing tool. Small area refers to IBF, EEM, MRF and CNC localized polishing techniques. It is recommended to include the instrument used for the measurement (NOM, LTP, etc. ). It is also interesting to include results of the treatment or detrending applied to the original measured data, and statistics from them (e. g. SD for heights and slopes). It has been decided that the manufacturer of the mirror will not be mentioned in the database. The reasons for this are that we do not want to incite competition nor to bias decisions on the choice of a manufacturer based on data that by nature are incomplete.
Database format, content and access The database is formed by a collection of mirror profiles (heights or slopes) from different facilities. The volume of the data is small, starting with a few tens of profiles, and every profile usually has a few hundred points. Also, metadata is quite light, i. e. only a few values and keywords. This fact, and the underlying idea to concentrate on the data and not on the container, drove us to select the simplest possible solutions for file format. We store the data in a format that is (i) human readable (in ASCII) and (ii) close to the initial raw format or what is usually supplied and exchanged by users. The use of the original raw individual files is discarded because there are multiple formats and complicate the access. We decided to use an ASCII common format.
Each mirror measurement has a unique identification number, which is just the order in which it has been included in the database (e. g. dabam-010:dat). Files containing the mirror profiles have the :dat extension. A single file (database entry) can contain multiple columns which allows the raw data and the detrended data to be included. The two first columns will include the 'default information': the first column is always the spatial coordinate along the mirror, and the second is the main data, that can be either slope or profile, either raw or detrended. The additional information accompanying the data file is stored in an additional file with the same name and :txt extension. This file is also ASCII and contains a list of predefined keywords and their values (e. g. 00 YEAR FABRICATION 00 : 1995).
To avoid defining a new file format and to write new data parsers, we adopt a common solution for including these metadata in ASCII with the keyword-value structure: we use the json format (json:org). Following these considerations, our database is a collection of files dabam-n:dat dabam-n:txt, (n = 1,. , N), placed in a single place (local or remote directory) that can be accessed remotely or copied locally. Each mirror in the database is represented by two files, one containing the profile data (dabam-n:dat) and another containing metadata (dabam-n:txt), which may also include results of the analysis of the data (results of SD slopes, SD roughness, PSD, etc. ). Initially we only concentrate on the use of one-dimensional profiles, typically from LTP or NOM measurements. However, several profiles for the same mirror can be stored using multiple columns in the :dat file.
How to contribute to the database The number of entries in the database is limited (25 at the time of writing this paper) but it is our intention to continue uploading new mirror profiles to the database, and to invite other metrology facilities and mirror manufacturers to submit files. The simplest way to contribute is to send 'candidates' mirror profiles for the database to the database maintainer. They will take care of checking that the data and metadata are the correct format before uploading them to the server. To make the submissions easier, we have implemented a web page where a user can upload the data file and define the metadata keywords in a user-friendly form (see Appendix B).
DABAM in practice: extraction and processing of data from the database The database files described in the previous section contain all the data and information required for optical simulations. However, because the access and evaluation of the data is not straightforward, we provide a software tool that is able to access the data, perform usual operations (e. g. calculate SD values, plot profiles, etc. ) and store it locally in different formats ready for simulations (e. g. for SHADOW). Again, for the sake of simplicity, we implemented this tool using the Python language, which runs on almost every machine, and is in many cases preinstalled in the machine operating system (e. g. Linux and MacOS). This piece of software is contained in a single file dabam:py that can be dowloaded from the DABAM repositories and run by entering in a terminal window the command: python dabam:py n, with n the DABAM entry number.
These operations are exact in the absence of errors but have to be taken with care when using experimental profiles. The integration in the presence of random error leads to the accumulated error along the trace. Errors also affect differentiation. The particularities of the numerical methods used also make some differences in the integration and in the Fourier transform  (Yaroslavsky et al. , 2005). The mean values are " z z ¼ 1 N X i z i ; " z z 0 ¼ 1 N X i z 0 i ; ð3Þ and the standard deviations (slope error SD and roughness SD, respectively): Output of the command python dabam:py 10: h ¼ 1 N À 1 X i z i À " z z ð Þ 2 " # 1=2 ; s ¼ 1 N À 1 X i z 0 i À " z z 0 ð Þ 2 " # 1=2 : ð4Þ.
More generally, the slopes profile can be fitted to a polynomial of degree d (we use the numpy:polyfit routine). The fit is subtracted from the slopes. A linear detrending corresponds to a linear fit d = 1 of the slopes, equivalent to fit the heights profile to a best circle with radius R = 1/m, where m is the slope of the straight line. By default, for non-elliptical mirrors, a linear detrending of the slopes profile is systematically carried out to remove the best circle in mirrors with a circular heights profile (cylindrical, spherical or toroidal). In the case of plane mirrors, this linear detrending helps to remove a first order of a smooth approximation of other shape errors (e. g. the gravity sag). Fig. 2  shows the heights and slopes profiles for the plane mirrors in the database. Similarly, Fig.
5. As will be discussed later, all PSDs show a similar behaviour, approaching a straight line (in log-log plot) with negative slope. Calculated values of slope error s (in mrad) and height error h (in nm) for the different database data calculated from the points of the heights or slopes profiles, respectively. The first value in parentheses is calculated by integrating the PSD. The second value in parentheses, where available, is the value provided by the user that supplied the data. Scan length (L in mm) and mirror shape are also included. Detrended heights (a) and slopes (b) profiles of the elliptical mirrors listed in Table  1.
As an example, we have used the profile entry 12 and simulated a profile with the same parameters (N = 443, = 2. 8, s = 0. 3 mrad). The original and simulated profiles and their PSD are compared in Figs. 7  and 8 , respectively. From these figures, it can be appreciated that the experimental and simulated profiles have similar structure for large frequencies, but for small frequencies (figure or shape errors) are completely different, because of the random phases used. In fact, another realisation with different phases produces a profile with completely different shape errors. This illustrates the interest in having real profiles available, so justifying the DABAM database. Moreover, one can also simulate ab initio profiles with the Gaussian height distribution and Gaussian autocorrelation function  (Garcia & Stoll, 1984).
Using also the entry 12 in the DABAM data (c l = 44 mm, N = 443, s = 0. 3 mrad) a simulation is performed and shown in Figs. 7  and 8. It is evident that the shape and PSD of the simulated profile are very different from the experimental one, thus concluding that our profiles are not Gaussian correlated. In the ab initio simulation of profiles it is important that the sampling parameters are strongly correlated to the profile inputs (c l or ) and profile normalization (to s or h ). Therefore, if one wants to work using simulated profiles, it is interesting to work in tandem using the DABAM tool that permits different experimental profiles to be looked at and compared with the results of a simulation tool. Both tools are available in the ShadowOui package.
Hybrid (ray-tracing and physical-optics) calculations For many cases in new X-ray sources with particular regard to diffraction-limited storage rings and free-electron lasers, ray-tracing is insufficient for giving a good estimate of the distribution of the intensity at the focal position  (Pardini et al. , 2015) , since it does not include coherent effects due to the mirror aperture and the mirror imperfections. On the other hand, wave-optics simulation using single wavefront propagation (plane, spherical waves or single electron emission) is Image produced by circular mirrors set to focus with p = 30 m, q = 10 m, g = 3 mrad with slope errors from the database entries 3 ( s = 0. 17 mrad), usually not accurate because the synchrotron beam is not fully coherent and, moreover, is not diffraction-limited [the product of the source size s and source divergence s does not verify s s ' =ð4Þ, with the photon wavelength].
DABAM integration in simulation packages: ShadowOui The availability of DABAM and its Python binding makes it suitable for integration into simulation environments that can interact with Python. Oasys is a Python-based environment (Sanchez del Rio et al. , 2014) designed to integrate optics simulations into virtual experiments. ShadowOui is the Oasys user interface for the ray-tracing SHADOW, also including the hybrid approach used before. The DABAM database is fully integrated into ShadowOui and the user can automatically access and use the metrology files for simulations in an automatic and transparent way. Fig. 12  shows the Shadow-Oui widget dedicated to access DABAM and insert the slopes errors into the SHADOW optical elements. ShadowOui also includes a profile simulator that is able to create synthetic surfaces by using experimental or synthetic (fractal or Gaussian) profiles and then combine them into a two-dimensional surface by defining the geometry of a transversal profile.
Summary and conclusions A collaborative effort among different metrology laboratories of several institutions has permitted collection of a set of measured mirror profiles that are grouped and stored in a database. This information is open to the community to allow the role of mirror irregularities and errors in the performances of optical systems to be assessed, mostly in synchrotron beamlines. In addition to the experimental data, software is written to retrieve and process the mirror profiles in the database. Some statistical parameters are calculated on the detrended profiles. Moreover, using simple methods of propagation based on geometrical optics (ray-tracing) and physical optics, we can compute the effect of the profile errors in a simple focusing configutarion. Intensity profiles produced by circular mirrors set to focus with p = 30 m, q = 10 m, = 3 mrad with slope errors from the database entries 3 ( s = 0.
This is no longer true for entry 3, where both geometrical optics and physical optics contributions have comparable widths. The DABAM database is not centralized, and local copies or other servers can be deployed. In particular, the DABAM concept can also be used to store all data measured in a metrology laboratory but keeping the server and access private. The main use of the DABAM data is in combination with simulation tools, like ray tracing. The ShadowOui package incorporated in the hybrid model contains tools to access DABAM data and to simulate ab initio surfaces. The DABAM database can easily be integrated into other simulation tools.
Acknowledgements The work of DB is partially funded by the  Austrian  Grant No. 849109 ). That of FS is partially funded by the European Metrology Research Project  EMRP-JRP SIB58 Angles  within the  EURAMET program  of the  European Union. This research used resources of the  Advanced Photon Source , a  US Department of Energy (DOE) Office of Science User Facility  operated for the  DOE Office of Science by Argonne National Laboratory  under Contract No. DE-AC02-06CH11357. The work of PT was performed at  Brookhaven National Laboratory  with support from the  US Department of Energy  under Prime Contract No. DE-SC0012704. The Advanced Light Source is supported by.
Data assimilation methods provide a rigorous statistical framework for constraining parametric uncertainty in land surface models (LSMs), which in turn helps to improve their predictive capability and to identify areas in which the representation of physical processes is inadequate. The increase in the number of available datasets in recent years allows us to address different aspects of the model at a variety of spatial and temporal scales. However, combining data streams in a DA system is not a trivial task. In this study we highlight some of the challenges surrounding multiple data stream assimilation for the carbon cycle component of LSMs. We give particular consideration to the assumptions associated with the type of inversion algorithm that are typically used when optimising global LSMs -namely, Gaussian error distributions and linearity in the model dynamics.
, 2013) , and the spread between model projections is still very large  (Sitch et al. , 2015). Aside from model structural and forcing errors, one source of uncertainty is related to the parameter (i. e. fixed) values of a model. Model-data fusion, or data assimilation (DA), allows the calibration, or optimisation, of these values by minimising a cost function that quantifies the model-data misfit while accounting for the uncertainties inherent in both the model and data in a statistically rigorous framework. The C cycle component of most LSMs is complex and contains a large number of parameters; luckily however, there are an increasing number of in situ and remote-sensing-based data streams that can be used for parameter optimisation. These data bring information on different spatial and temporal scales, such as atmospheric CO 2 concentration data: measured at surface stations at continental to global scales, providing information from synoptic timescales to inter-annual variability (IAV) and long-term trends; eddy covariance net CO 2 (net ecosystem exchange -NEE) and latent (LE) and sensible heat fluxes: measured at half-hourly intervals at many sites across different ecosystems/regions, providing information at seasonal to inter-annual timescales; satellite-derived measures of vegetation dynamics, including "greenness" indices (i.
e. the Normalised Difference Vegetation Index -NDVI), fraction of absorbed photosynthetically active radiation (FAPAR) and leaf area index (LAI): provided at global scales, and up to daily time steps spanning more than a decade, thus capturing IAV and long-term trends (though usually with a trade-off between spatial and temporal resolution); satellite-derived measurements of soil moisture and land surface temperature: measured at the same temporal and spatial scales as the satellite-derived observations of vegetation dynamics; aboveground biomass measurements: currently taken at only one or a few points in time at plot scale up to regional scale from aircraft and satellite data, or are estimated from allometric relationships at each site; soil C stock estimates: usually only taken at one point in time at plot scale; ancillary data on vegetation characteristics such as tree height or budburst (such data are only measured at certain well-instrumented sites).
Mathematically, the optimal approach is the simultaneous, but complications may arise due computational constraints related to the inversion of large matrices or the requirement of numerous simulations, particularly for global datasets (e. g. Peylin et al. , 2016)  and/or due to the "weight" of different data streams in the optimisation (e. g. Wutzler and Carvalhais, 2014). On the other hand, in a step-wise assimilation the parameter error covariance matrix has to be propagated at each step, which implies that it can be computed. If the parameter error covariance matrix can be properly estimated and is propagated between each step, the step-wise approach should be mathematically equal to simultaneous. However, many inversion algorithms (e. g. derivative-based methods that use the gradient of the cost function to find its minimum) require assumptions of model (quasi-)linearity and Gaussian parameter and observation error distributions  (Tarantola, 1987, p.
Extra constraint from multiple data streams Most site-based carbon cycle data assimilation studies have used eddy covariance measurements of NEE and LE fluxes to constrain the relevant parameters of ecosystem models. However, a few studies have also made use of chamber flux soil respiration data and field measurements of vegetation characteristics (e. g. tree height, budburst, LAI) or estimates of litterfall and carbon stocks as ancillary information (e. g. Fox et al. , 2009; Keenan et al. , 2012; Thum et al. , 2016; Van Oijen et al. , 2005; Richardson et al. , 2010; Williams et al. , 2005). Two recent studies combined high-resolution satellite-derived FA-PAR data with in situ eddy covariance measurements to optimise parameters related to carbon, water and energy cycles of the ORCHIDEE and BETHY LSMs at a couple of sites  (Bacour et al.