dataesr/scientific-paragraphs-categorization huggingface dataset:
Spatial coverage of Google roads and GSV For the SG, we encountered several cells with no available GSV data and a high discrepancy between field and in silico data (see results). We therefore assessed the structure of the road network covered by GSV to examine its spatial variability and determine if it could explain the divergences between data sets. We developed a java script to collect information from the Google API (code available in Text S1), we then derived maps of roads included in the GSV database on a regular grid of 250 by 250 m mesh. The coordinates of each point were used to retrieve the nearest road and the nearest road available in the GSV database. We additionally computed an index to quantify the coverage by GSV within each sampling cell using the following procedure: cells were rasterized at the resolution of 250 by 250 m using the R package raster  [35].
Comparing field and Google-derived data sets Both zones led to two grids of PPM occurrences corresponding to field and Google-derived data. This was considered as a twoclass prediction problem i. e. binary classification. Cells in which the PPM was observed formed the ''positive'' class while cells where the PPM was absent formed the ''negative'' class. We computed the four possible outcomes of that binary classifier, namely the true positive (TP), the true negative (TN), the false positive (FP) and the false negative (FN)  [36] , assuming that field sampling gave true data. TP corresponded to cases where the PPM was observed both in the field and from Google database. TN corresponded to absence in the field and in the Google derived data. FP corresponded to cells where the PPM was not observed in the field but was present according to Google data.
Finally FN corresponded to field observations associated to absence according to Google data. TN, FN, TP and FP formed the confusion matrix reported in the results section (Tables  1  and 2 ). The sensitivity (rate of TP) and the specificity (rate of TN) were used to measure the proportion of good predictions derived from Google data in the case of cells where PPM is present and absent respectively. The sensitivity and the specificity were estimated as TP/P and TN/N where P and N are the total number of positive and negative cases respectively. We computed the accuracy or the rate of good predictions as (TP+TN)/(P+N). We additionally computed the Phi/Matthews correlation coefficient as a measure of discrete covariation between field and Google derived data  [37]. This coefficient ranges from 1 to +1 with 1 indicating a perfect prediction, 0 a random prediction and negative values a worse than random prediction.
Large sampling grid The field data collected for the large scale survey showed that the PPM was present all over the study area: there were 178 cells classified as ''presenc'' and 5 as ''absence''. Google-derived data   provided slightly different values with 165 cells classified as ''presence'' and 17 as ''absence''. One cell was reported as ''indistinct'' because the pictures did not allow ascertaining the presence of PPM nests (Figure  3 ). In that case, we assigned the status of absence to the cell when computing sensitivity and other indices. Off the 183 cells, 165 were true positives and 5 were true negatives (Table  1 ). The number of false negatives was 13 and there was no case of false positive (Table  1 ). This led to very high values of the sensitivity (true positive rate) and specificity (true negative rate), which were respectively 0.
Small sampling grid The field data collected over the SG consisted in 70 and 51 cells classified as ''presence'' and ''absence'' respectively. Googlederived data comprised 3 cells classified as ''presence'', 109 cells where the PPM was absent, 3 indistinct cells, and 6 cells for which no GSV data were available (Figure  4 ). As in the case of the largescale survey, the ''indistinct'' cells were classified as ''absence'' and we did not account for the cells with no data in the computation of the statistics. There were 6 true positives and 49 true negatives while the number of false negatives was 63 and there was no case of false positive (Table  2 ). As a consequence, these values led to a low sensitivity of 0. 045 while the specificity was 1. The Google-derived data showed a fairly low agreement with field data as the accuracy was 0.
Date of pictures: seasonality and other sources of uncertainty Some organisms may concentrate or be visible at particular season or times of the day and this defines the periods when census becomes possible. Because we used the Google views (pictures) as a data source, we depend on the date at which pictures where taken. This is of a particular importance in the case of the PPM because nests can mostly be observed during winter and deteriorates after larvae migration into the soil. More generally, using GSV for species assessment should be approached with caution when species census is seasonal. As evoked above, the uncertainty introduced by processing pictures taken at various dates along the year is increasing as the grain is decreasing. It is important to highlight the value of the Google data for future research.
Google database is updated and this growing body of data will constitute a tremendous amount of information in the next decades allowing for example researchers to perform retrospective analyses of species expansion processes and biological invasions. Chen et al. [42]  recently published the results of a study assessing 100 years of environmental changes in Western China based on the comparison of modern versus 100-years old pictures of 250 localities from Western China. They showed how this type of data could help detecting and characterising changes in vegetation, landscape and more globally biodiversity. We believe that GSV may contribute to such type of retrospective surveys with unprecedented power and resolution.
Potential of GSV regarding species monitoring As underlined above, only species visible from photographs or associated with conspicuous sign of presence can be surveyed using GSV imagery. This encompasses various organisms that alter significantly and specifically the colour or shape of trees as well as tree and plant species that colonize road edges or can be reliably sighted from roads. With that regards, the PPM constitutes a good biological model since its winter nests are white and easily visible in the tree foliage. In addition, PPM spatial aggregation at the edges of forest plots or along roads  [26]  increases the probability that the species be spotted if present in a given area. The present study focused on presence/absence data and the potential use of GSV for density census has not been considered.
Acknowledgements. We thank the anonymous referee for insightful comments which helped to improve the paper,  Mark Wolfire  and  Alessandra Contursi  for helpful discussion, and the  NHSC team at IPAC  for their support in the data reduction process. M. R. and S. V. acknowledge support by the research projects  AYA2014-53506-P  and  AYA2017-84897-P  from the  Spanish Ministerio de Economía y Competitividad , from the  European Regional Development Funds (FEDER)  and the  Junta de Andalucía (Spain)  grants  FQM108. This study has been partially financed by the  Consejería de Conocimiento ,  Investigación y Universidad, Junta de Andalucía  and  European Regional Development Fund (ERDF) , ref. SOMM17/6105/ UGR. FST thanks the  Spanish Ministry of Economy and Competitiveness (MINECO)  for support under grant number  AYA2016-76219-P.
Terrestrial ecosystem models can provide major insights into the responses of Earth's ecosystems to environmental changes and rising levels of atmospheric CO 2. To achieve this goal, biosphere models need mechanistic formulations of the processes that drive the ecosystem functioning from diurnal to decadal timescales. However, the subsequent complexity of model equations is associated with unknown or poorly calibrated parameters that limit the accuracy of long-term simulations of carbon or water fluxes and their interannual variations. In this study, we develop a data assimilation framework to constrain the parameters of a mechanistic land surface model (ORCHIDEE) with eddy-covariance observations of CO 2 and latent heat fluxes made during the years 2001-2004 at the temperate beech forest site of Hesse, in eastern France.
The ORCHIDEE model is then optimized for each year, and for the whole 2001-2004 period. We first find that a reduced (< 10) set of parameters can be tightly constrained by the eddy-covariance observations, with a typical error reduction of 90 %. We then show that including contrasted weather regimes  (dry in 2003 and wet in 2002)  is necessary to optimize a few specific parameters (like the temperature dependence of the photosynthetic activity). Furthermore, we find that parameters inverted from 4 years of flux measurements are successful at enhancing the model fit to the data on several timescales (from monthly to interannual), resulting in a typical modeling efficiency of 92 % over the  2001 -2004 period (Nash-Sutcliffe coefficient). This suggests that ORCHIDEE is able robustly to predict, after optimization, the fluxes of CO 2 and the latent heat of a specific temperate beech forest (Hesse site).
Some previous works focused on quantifying how much information could be retrieved from the observations in terms of reducing uncertainties in model parameters. The approach relied on the minimization of a cost function with respect to model parameters. Wang et al. (2001)  showed that about three or four parameters of a rather simplified terrestrial model could be estimated independently by assimilating 3 weeks of eddy-covariance measurements of CO 2 and la-tent, sensible and ground heat fluxes. Braswell et al. (2005)  used a 10 year record of half-hourly observations of CO 2 flux to study the influence of data frequencies on inverted parameters. It was shown that parameters related to processes operating on diurnal and seasonal scales (evapotranspiration, photosynthesis) were tightly constrained compared to those related to processes with longer timescales.
Santaren et al. (2007)  conducted a similar study, but used a complex mechanistic model (ORCHIDEE) with more than 15 parameters to be optimized. In that study, the data assimilation framework was also employed to highlight model structural deficiencies and to improve process understanding. Performances of parameter optimization schemes have been investigated in several studies  (Richardson and Hollinger, 2005; Trudinger et al. , 2007; Lasslop et al. , 2008; Fox et al. , 2009; Ziehn et al. , 2012). They could be strongly affected by the way observation errors are prescribed within misfit functions  (Richardson and Hollinger, 2005; Trudinger et al. , 2007; Lasslop et al. , 2008). Contrarily, the choice of the minimizing method amongst an exhaustive panel (locally gradient-based or global random search methods, Kalman filtering) was shown to have smaller influences  (Trudinger et al.
The ORCHIDEE land surface model The ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic Ecosystems) biogeochemical ecosystem model is a mechanistic land surface model that simulates the exchanges of carbon dioxide, water and heat fluxes within the soilvegetation system and with the atmosphere; variations in water and carbon pools are predicted variables. Model processes are computed on a wide range of timescales: from 30 min (e. g. , photosynthesis, evapotranspiration, etc. ) to thousands of years (e. g. , passive soil carbon pool decomposition)  (Krinner et al. , 2005). ORCHIDEE has been applied in a number of site-level  (Krinner et al. , 2005)  and regional CO 2 budget studies forced by climate data  (Jung et al. , 2007) , as well as for future projections of the carbon-climate system, coupled to an atmospheric model  (Cadule et al.
, 2010). The model contains a biophysical module dealing with photosynthesis and energy balance calculations every 30 min, a carbon dynamics module dealing with the allocation of assimilates, autotrophic respiration components, onset and senescence of foliar development, mortality and soil organic matter decomposition in a daily time step. A complete description of ORCHIDEE can be found in  Krinner et al. (2005). The main parameters optimized in this study are defined in Table  1 , and the associated model equations are defined in Appendix A. The choice of a subset list amongst the total number of ORCHIDEE parameters is explained in Sect. 2. 3. 4. As in most land surface models (LSMs) dealing with biogeochemical processes, the vegetation is described in OR-CHIDEE by plant functional types (PFT), with 13 different PFT over the globe.
Although we acknowledge that observation and parameter errors do not strictly follow Gaussian distributions, we have used such a hypothesis to simplify the Bayesian optimization problem, which becomes equivalent to a least squares minimization and thus analytically solvable by minimizing a cost function like Eq. (1)  (Tarantola, 1987). The temporal resolution of M(x) and Y corresponds to daily averages. We choose daily means in order to assess the ability of the ORCHIDEE model to reproduce observed CO 2 and latent heat flux variability on daily, weekly, seasonal, and interannual timescales, and not the hourly timescale. Because of data gaps, daily means have been calculated only when more than 80 % of the half-hourly data were available. R, the data error covariance matrix, should include both data uncertainties (diagonal elements) and their correlations (non-diagonal elements).
The magnitudes of prior parameter uncertainties (matrix B) were chosen (i) according to expert knowledge and (ii) to be relatively large, to minimize the influence of the Bayesian term and thus to provide the maximum leverage of the observations upon the parameters. Moreover, for the minimization algorithms, an additional important information is the range of variation for each parameter; such range is prescribed from expert knowledge and typical observed values (TRY database: http://www. try-db. org). As in  Kuppel et al. (2012) , the prior error for each parameter was thus set to 40 % of its prescribed range of variation (Table  1 ) and we do not consider correlations between a priori parameter values.
Temporal skill of the model equations The twin experiment results have shown that the genetic algorithm is a more robust method than the gradient-based BFGS algorithm in optimizing the parameters of a model with the complexity of ORCHIDEE. Therefore, we use the GA algorithm to fuse the model with real data collected at the Hesse site. To address the issue of the temporal skill of the model equations, i. e. , their ability to represent year-to-year flux variations, we have designed five cross-validation experiments where the ORCHIDEE parameters are optimized using the Hesse data for different years  : 2001, 2002, 2003, and 2004 , and for the whole period  (2001) (2002) (2003) (2004). The corresponding optimized parameter sets are called X 2001 , X 2002 ,. , X 4Y. Then, for every five optimized parameter sets, we successively run the model forward and examine the fits to the observations over each year and over 2001-2004.
Due to the complexity of ORCHIDEE, the use of flux data only in the optimization likely prevents one from constraining separately parameters related to the environmental responses of the respirations from those related to the carbon stocks themselves. The use of soil and vegetation above-ground biomass data would help to differentiate these parameters, and it would dampen the influence of bringing the model to steady state. Our choice of optimizing K soilC (scalar to the initial soil C pools) should be considered a compromise, while for the vegetation stocks, we would need to account for the true forest age in the simulation (study in preparation).
9 ), when differences can reach 12 g C m -2 /month. Note that the prior model was not able to capture the IAV during the peak of the growing season (July, August) and that the optimization drastically improves this feature. Overall, the inversion of daily means across the 4 years leads to a strong enhancement of the model fit to the NEE data on each analyzed timescale, with the exception of the IAV of the annual carbon uptake. Compared to the case of the 4 year inversion, the model run with parameters optimized with 1 year of observations shows a lower skill when simulating data of the whole period and a much lower skill when applied to specific years with different weather conditions (Sect. 3. 2. 1). A single year of observation does not likely contain sufficient information to constrain the parameters of ORCHIDEE efficiently.
Conclusions In this study, we demonstrate that the fit to NEE and LE flux data of a complex terrestrial model like ORCHIDEE can be dramatically enhanced with a Bayesian parameter calibration based on a genetic algorithm (GA). The enhanced robustness of this Monte Carlo approach compared to a variational approach using the gradient of the cost function (BFGS), highlighted in this study at one specific FLUXNET site, calls for further investigation. First, one should verify if the better performance of the GA holds at other sites with different plant functional types. Secondly and more importantly the use of multiple sites in the cost function, as in  Kuppel et al. (2012) , may "smooth" the shape of the cost function, allowing the retrieval of the global minimum of the cost function more easily with a variational approach than for a single site, possibly outperforming the performances of the GA.
These technical questions are beyond the scope of this paper and currently under investigation. For a multi-year simulation of contrasted weather regimes, the ORCHIDEE model appears to be robust enough to simulate the carbon and water fluxes, with parameters optimized against 4 years of observations. At the opposite end, parameters obtained with the inversion of only 1 year of data do not guarantee the simulation of the year-to-year variability of the NEE and LE fluxes, especially the drought event impact or the timing of the carbon uptake season. This study complements a recent study with the same model, by  Kuppel et al. (2012) , investigating the generality of the model across different FluxNet sites of the same plant functional type. They showed the benefit of including multi-site data to optimize the ORCHIDEE model, but did not focus on the temporal skill of the model.
Acknowledgements. The post-doc program of  D. Santaren  has been funded by the  Swiss Federal Institute of Technology, Zurich (ETH Zurich ). The authors thank  ANR  project  PREVASSEMBLE  ( ANR-08-COSI-012 ) for financial support and  F. Chevallier ,  N. Vuichard  and  N. Viovy  for fruitful general discussions. Computations were performed thanks to the computer team of the LSCE. Edited by:  M. Williams.
V. CONCLUSION AND PROSPECTS HTGs emphasize relevant transient and asymptotic dynamical properties. We have defined a novel algorithm enabling the compaction of state transition graphs and the generation of HTGs on-the-fly. This approach has been implemented into a development version of the software GINsim, available as a pre-release. 30 e have applied this approach to a comprehensive model for T-helper cell differentiation. Although this model still needs to be further refined and tested, the analysis presented here clearly demonstrates the assets of the HTG representation, which leads to significant graph compression and clearly emphasizes the organization of the state space into attractors and basins of attraction. Interestingly, applying our algorithm for HTG construction onto a HTG produces a further compacted graph-based representation of the dynamics, where the nodes correspond to basins of attraction.
ACKNOWLEDGMENTS We acknowledge support from the  EU   FP7  ( APOSYS large scale  project), the  EU   EraSysBio+ program  (project  ModHeart ) the  ANR  ( Project  Grant  ANR-08-SYSC-003 ),  FCT  (Project Grant  PTDC/EIA-CCO/099229/2008 ), and the  Belgian Science Policy Office (IAP BioMaGNet ).
The availability of next-generation sequences of transcripts from prokaryotic organisms offers the opportunity to design a new generation of automated genome annotation tools not yet available for prokaryotes. In this work, we designed EuGene-P, the first integrative prokaryotic gene finder tool which combines a variety of high-throughput data, including oriented RNA-Seq data, directly into the prediction process. This enables the automated prediction of coding sequences (CDSs), untranslated regions, transcription start sites (TSSs) and non-coding RNA (ncRNA, sense and antisense) genes. EuGene-P was used to comprehensively and accurately annotate the genome of the nitrogen-fixing bacterium Sinorhizobium meliloti strain 2011, leading to the prediction of 6308 CDSs as well as 1876 ncRNAs. Among them, 1280 appeared as antisense to a CDS, which supports recent findings that antisense transcription activity is widespread in bacteria.
The development of such prokaryotic gene finders allowing not only the prediction of coding sequences (CDSs), but also TSSs and non-coding (nc) transcribed genes, should provide improved transcript quantification, facilitated identification of regulatory sequences upstream of mapped TSSs and thus, easier analysis of gene regulation. Because of the higher complexity of eukaryotic gene structures and the usual availability of transcribed sequences (such as expressed sequence tags or ESTs), many eukaryotic gene finders already have the ability to integrate experimental evidence in their gene prediction process. For example, ESTs are exploited in EuGene  14  and Augustus,  15  GenomScan  16  uses similarities with known proteins, whereas SGP/ SGP2  17, 18  and EuGene'Hom  19  integrates sequence conservation with related organisms.
In this work, we adapted the eukaryotic gene finder, EuGene  14, 20  , to the specific requirements of gene identification in prokaryotes, where in particular overlapping CDSs are relatively frequent. EuGene has already been used successfully to annotate a variety of eukaryotic genomes  21 -27  and has shown its ability to quickly incorporate new types of information for enhancing its predictive power. The generic tool developed here, called EuGene-P, exploits highthroughput data, such as strand-specific RNA-Seq data, to qualitatively improve the prediction contents and to minimize manual expert annotation. The produced annotation contains previously unpredicted important gene structure features such as 5 0 and 3 0 UTRs, as well as ncRNA genes (including antisense RNAs). The mathematical model behind EuGene-P and its modular software architecture based on plug-ins facilitate the integration of a variety of other high-throughput data, such as PET-Seq, mass spectrometry data, protein similarities, DNA homologies, predicted transcription terminators and others.
Sinorhizobium meliloti genome sequencing The genome of Sm2011 was sequenced at the Genoscope (CNS, Evry, France) using fractions of 454 Titanium (46 Mb), 454 paired ends (18 Mb, insert size: 8 kb) and Illumina single end reads (1. 2 Gb, read length: 76 nt), providing a 190-fold theoretical coverage of the genome. The genome sequence was assembled as described in Supplementary Materials and Methods. The nucleotide sequences of Sm2011 and Sm1021 strains were compared using the glint software (Faraut T. and Courcelle E. ; http://lipmbioinfo. toulouse. inra. fr/download/glint/, unpublished) to identify polymorphic regions. A set of 71 mutations including 64 putative frameshifts were verified by Sanger sequencing of polymerase chain reaction (PCR) products surrounding these regions generated using either Sm2011 or Sm1021  32  DNA as a template.
Transcriptome analysis Differential expression of identified genes was calculated with R v2. 13. 0 using DESeq v1. 4. 1  46  available in Bioconductor v2. 8. DESeq utilizes a negative binomial distribution for modelling read counts per transcript and implements a method for normalizing the counts. Variance was estimated using the per-condition argument. P-values are adjusted for multiple testing using the Benjamini and Hochberg method. 47.