dataesr/scientific-paragraphs-categorization huggingface dataset:
This follows from FireMIP models tending to underestimate burnt area in Africa and Australia, although burnt area in South American savannas is usually overestimated (Table  2 ). All of the FireMIP models, except LPJ-GUESS-GlobFIRM, capture a belt of high burnt area in central Eurasia. However, the models overestimate burnt area across the extratropics on average by 180 % to 304 %, depending on the reference burnt area dataset. This overestimation largely reflects the fact that the simulated burnt area over the Mediterranean basin and western USA is too large (Table  2 , Fig. 2 ). The FireMIP models that include a subannual time step for fire calculations (CLM, CLASS-CTEM, JULES-INFERNO, JSBACH-SPITFIRE, LPJ-GUESS-SPITFIRE, ORCHIDEE-SPITFIRE) generally reproduce the seasonality of burnt area (Fig.
3 ), particularly in the tropics. The models capture the timing of the peak fire season reasonably well, with all of the models performing better than mean null model for seasonal phase in burnt area (Table  3 ). The models also frequently perform better than the random null model, with all models performing better against GFED4. However, all of the FireMIP models perform worse than mean null model for seasonal concentration of burnt area, inde-pendent of the reference burnt area dataset. The observations show a unimodal pattern in burnt area in the tropics, peaking between November and the end of February in the northern tropics and between June and the end of October in the southern tropics (Fig. 3 ). The models also show a unimodal pattern in both regions. However, all the FireMIP models except ORCHIDEE-SPITFIRE show a ∼ 2-month delay in peak burnt area in the northern tropics, and the period with high burnt area is also less concentrated than observed.
Some https://doi. org/10. 5194/gmd-13-3299-2020 Geosci. Model Dev. , 13, 3299-3318, 2020 The observations show no clear seasonal pattern in burnt area over the southern extratropics, although the most prominent peak occurs in December and January. All the FireMIP models, except LPJ-GUESS-SPITFIRE, reproduce this midsummer peak. LPJ-GUESS-SPITFIRE shows little seasonality in burnt area in this region. The FireMIP models have problems representing IAV in global burnt area, with some models (CLASS-CTEM, MC2) worse than the random model and most models performing worse than the mean for most of the target datasets (Table  3 ). However, there is considerable uncertainty in the observed IAV in burnt area (Fig. 4 ), and the scores are therefore dependent on the reference dataset considered, with generally worse scores for FireCCI5.
1 and GFED4s compared to the other datasets. Observational uncertainty is most probably underestimated as the burnt area products are not independent, since they all rely on MODIS satellite imagery. Despite the failure to reproduce IAV in general, most of the models show higher burnt area in the early 2000s and a low in 2009-2010 after which burnt area increased again (Fig. 4 ). The spatial patterns in simulated fire-related carbon emissions are in line with the reference data, with most FireMIP models except LPJ-GUESS-GlobFIRM, MC2 and LPJ-GUESS-SPITFIRE performing better than the mean null model. CLM, JULES-INFERNO and JSBACH-SPITFIRE are the best performing models with NME scores < 0. 8. Seasonality in fire emissions mimics the results for burnt area with good scores for seasonal phase, but all models perform worse than the mean null model for seasonal concentration.
Present-day model performance: vegetation properties Fire spread and hence burnt area is strongly influenced by fuel availability, which in turn is affected by vegetation primary production and biomass. Simulated spatial patterns of GPP compare well with estimates of GPP upscaled from FLUXNET data  (Jung et al. , 2017) , with scores (0. 39-0. 67) considerably better than both null models. However, performance against site-based estimates of GPP  (Luyssaert et al. , 2007)  are considerably poorer (1. 09-1. 49) and worse than the mean null model. Only LPJ-GUESS-SPITFIRE, LPJ-GUESS-SIMFIRE-BLAZE and ORCHIDEE-SPITFIRE perform better than the random null model. There is no clear relationship between model scores for the two datasets; models performing better when compared to the Jung dataset do not necessarily show a higher score when compared to the Luyssaert GPP dataset.
The two GPP datasets are very different. The upscaled FLUXNET dataset is a modelled product but has global coverage (see Methods), while the Luyssaert dataset has local measurements but only at a limited number of sites, largely concen-trated across the northern extratropics. Thus, the better match between the FireMIP models and the upscaled FLUXNET dataset may reflect the broader spatial coverage or the fact that climate and land-cover data are used for upscaling. Only the upscaled FLUXNET data provide monthly data and can thus be used to assess GPP seasonality. The FireMIP models are able to represent the seasonal peak timing in GPP, with all models performing better than the mean and random null models. However, models have difficulty in representing the length of the growing season, with the scores for seasonal concentration in GPP (1.
08-1. 23) above the mean null model but below the random null model for all FireMIP models. Model performance is better for site-level NPP than sitelevel GPP. All of the FireMIP models perform better than the mean null model, independent of the choice of reference dataset (Table  3 ), except for CLASS-CTEM against the Luyssaert dataset. JULES-INFERNO, JSBACH-SPITFIRE and MC2 are the best-performing models. The FireMIP models generally capture the spatial pattern in LAI, with all models performing better than the mean null model (0. 44-0. 81), independent of the reference dataset considered. JULES-INFERNO has the best score for both reference datasets. Although the overall global pattern in LAI is well represented in all the FireMIP models, they have more trouble representing LAI in agricultural areas such as the central USA or areas with low LAI such as drylands and mountain areas (Fig.
87 and 0. 94 respectively), but this difference is largely the result of including GlobFIRM in the empirical model ensemble; removing this model results in a mean score of 0. 87 for these  models. The intermodel spread in scores within each group is much larger than the difference between the two types of model. Only one empirical model simulates fire seasonality, but this model performs worse than each of the process-based models, independent of reference dataset considered. There is no difference in the performance of process-based and empirical models with respect to IAV in burnt area, seasonal phase in burnt area or fire emissions. The FireMIP simulations include three models in which versions of the same process-based fire module (SPITFIRE) are coupled to different vegetation models. These three models produce a wide range of benchmarking scores for burnt area, with mean benchmarking scores of 0.
79, 0. 85 and 0. 72 for JSBACH, LPJ-GUESS and ORCHIDEE respectively. There are also large differences between these models in terms of other aspects of the fire regime (Table  3 ). As there are only moderate differences between the different SPIT-FIRE implementations  (Rabin et al. , 2017) , this suggests that the overall difference between the models reflect interactions between the fire and vegetation modules. Models using prescribed vegetation biogeography (CLM, CLASS-CTEM, JSBACH-SPITFIRE, ORCHIDEE-SPITFIRE) represent the spatial pattern of burnt area better than models with dynamic vegetation (JULES-INFERNO, LPJ-GUESS-SPITFIRE, LPJ-GUESS-GlobFIRM, LPJ-GUESS-SIMFIRE-BLAZE, MC2), with respective mean benchmarking scores across all burnt area datasets of https://doi. org/10. 5194/gmd-13-3299-2020 Geosci.
Model Dev. , 13, 3299-3318, 2020 0. 79 and 0. 97. This difference is still present even when LPJ-GUESS-GlobFIRM and MC2 are not included (0. 90). It seems likely that models using prescribed vegetation biogeography have a better representation of fuel loads and flammability. This can also partially be seen in the positive relationship between the benchmarking scores of vegetation carbon and burnt area spatial patterns for at least the GFED4, FireCCI4. 0 and FireCCI5. 1 burnt area reference datasets (mean of R 2 = 0. 31; range of 0. 19-0. 38). Areas where the FireMIP models represent vegetation carbon poorly coincide with some of the regions where models have trouble representing the spatial pattern of burnt area such as dryland regions (Fig. 1 ). Although there is no relationship between GPP/NPP and burnt area benchmarking scores, there is a positive relationship between simulated burnt area scores and the seasonal concentration of GPP (R 2 = 0.
Discussion There are large differences in the total burnt area between the FireMIP models, with two models (LPJ-GUESS-GlobFIRM and MC2) falling well outside the observed range in burnt area for the recent period. In the case of LPJ-GUESS-GlobFIRM, this is because GlobFIRM was developed before global burnt area products were available, resulting in a general poor performance  (Kloster and Lasslop, 2017) , in combination with the fact that structural changes were made to the vegetation model without a commensurate development of the fire module. In the case of MC2, this probably reflects the fact that MC2 was developed for regional applications but was applied globally here without any refinement of the fire model. The other FireMIP models used the burned area datasets to develop and tune their models.
Figure1. Reference datasets, the mean of all models and the percent of models for which the estimate falls within 50 %-200 % of the (mean) reference data are presented for a set of fire relevant variables. Results for the following variables are given as follows: (a) fraction burnt area, (b) seasonal timing of burnt area (as measured by mean phase), (c) burnt area season length (as measured by seasonal concentration), (d) fire C emissions (g C m -2 yr -1 ), (e) vegetation carbon (Mg ha -1 ) and (f) leaf area index (LAI) (m 2 m -2 ). Stippling in the second column indicates where variance between models is less than the FireMIP model ensemble mean. Purple in the third column indicates cell where the majority of the FireMIP models produce poor simulations of the variable, while green areas indicate that the majority of the FireMIP models perform well for that aspect of the fire regime.
Code and data availability. The benchmarking code is archived at https://zenodo. org/record/3879161#. Xtq-py-z2fU (last access: 5 June 2020) (https://doi. org/10. 5281/zenodo. 3879161, Kelley, 2020) , which also contains the code to produce the figures presented here. The FireMIP model output is archived at https://zenodo. org/ record/3555562#. Xell3C2ZOcY (last access: 22 November 2019) (https://doi. org/10. 5281/zenodo. 3555562, Hantson et al. , 2019). Data availability for each reference dataset is provided in Table  A1. Author contributions. SH and DK analysed the model results; SH, DB, MF, GL, FL, SM and JRM provided global fire-vegetation model outputs; SH, DK, SPH and AA wrote the paper with input from all authors.
5 1. 3 1. 0 GFED4 349 27 319 3 14 218 34 5. 2 0. 8 0. 9 MCD45 348 33 312 4 13 232 25 7. 0 2. 0 0. 9 FireCCI40 345 23 320 2 8 237 25 6. 8 1. 1 0. 8 FireCCI51 387 37 347 3 14 230 38 10. 2 1. 3 1. 1 CLM 454 77 362 15 36 194 15 7. 9 9. 3 3. 4 CLASS-CTEM 536 41 466 28 46 172 20 2. 0 4. 3 9. 5 JULES-INFERNO 381 76 292 13 26 128 23 5. 0 11. 0 7. 7 JSBACH-SPITFIRE 457 114 318 25 21 166 17 15. 5 9. 5 9. 7 LPJ-GUESS-GlobFIRM 39 14 24 1 3 7 3 0. 6 0. 6 0. 5 LPJ-GUESS-SPITFIRE 393 99 280 14 51 135 2. 8 12. 5 14. 5 6. 1 LPJ-GUESS-SIMFIRE-BLAZE 482 86 381 15 72 146 27 3. 4 7. 9 14. 9 MC2 97 40 54 3 2 17 2 0. 9 5. 0 2. 2 ORCHIDEE-SPITFIRE 471 16 435 19 13 246 81 2. 4 2. 4 0. 3 Figure 2. Simulated versus observed burnt fraction (% yr -1 ) for the present day(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012), where "combined" indicates the mean of the different burnt area datasets considered.
Stippling indicates where variance between burnt area datasets is less than the observed ensemble mean. models (ORCHIDEE-SPITFIRE, LPJ-GUESS-SPITFIRE) estimate peak burnt area ∼ 1-2 months too early in the southern tropics, while others simulate a peak ∼ 1 month too late (JULES-INFERNO, CLM, CLASS-CTEM) or have a less concentrated peak (JSBACH-SPITFIRE, JULES-INFERNO) than observed. The seasonality of burnt area in the northern extratropics shows a peak in spring and a second peak in summer. Only CLM reproduces this double peak, while all of the other FireMIP models show a single summer peak. Most of the models simulate the timing of the summer peak well. The only exception is LPJ-GUESS-SPITFIRE, which simulates the peak ∼ 2-3 months too late.
ERIC, the European research on CLL Chronic lymphocytic leukemia (CLL) is an age-related malignancy of mature B lymphocytes. 1 While the diagnosis of CLL is relatively straightforward, the clinical course and outcome are highly heterogeneous. 2 Moreover, despite remarkable therapeutic advances achieved in recent years, the disease is mostly incurable. ERIC, the European Research Initiative on CLL (http://www. ericll. org) is a Scientific Working Group (SWG) of the European Hematology Association (EHA) aimed at improved management of CLL through collaborative research. Thanks to the active participation of its members, now exceeding 1300 from all over Europe and beyond, ERIC engages in projects extending from basic to (mainly) translational and clinical research. Capitalizing on such initiatives but also on our expertise in the collection, management and analysis of heterogeneous clinical and biological data,  [3] [4] [5]  we have developed and present here the ERIC CLL database, a registry of clinical and biological data of patients with CLL.
Challenges of gathering high-quality real-world data Collection and analysis of real world data (RWD) can prove both effective and efficient for advancing precision medicine and improving the quality and delivery of medical care, provided these come along with data quality. 6, 7 The amount of biomedical data continuously increases due to technological advances, thus raising the necessity for designing and developing standardized approaches and methodologies to be implemented in clinical practice. 8 ata acquisition is usually a process distributed among different health professionals potentially leading to data quality problems across datasets, such as data redundancy (ie, repeated information), heterogeneity (eg, different date format) and inconsistency (eg, a date of diagnosis after the date of treatment), mainly resulting from lack of standardization and data curation processes.
Such problems are particularly pertinent in the case of multi-institutional efforts, where multilevel and multi-originated data are collected. Furthermore, the rapid increase of data complexity captured during patient care, especially data produced by the application of novel methodologies (eg, next generation sequencing), poses challenges that cannot be addressed with standard computational approaches. Thus, there is an imperative to improve real-world evidence generation by optimizing the integration of the heterogeneous information through automated and thorough quality control and curation mechanisms; and, support analysis and compatibility with established ontologies. This will provide unified and standardized access to valid, accurate and comparable datasets. Practical and feasible tools are required, capable of providing easiness in use, flexibility and simplicity, in order to facilitate the data entry procedure and encourage the registration and organization of clinically relevant data from the daily practice.
The ERIC CLL database The ERIC CLL database is a data management system that supports research and medical knowledge discovery in CLL implementing the aforementioned methodologies. The database is designed in expandable modules that allow the rapid introduction of additional categories and values when and if needed, based on the type of project run at any single moment in time, making it flexible and adjustable. All new information eventually ends up in the central dataset as stable asset of the database. Currently, the ERIC CLL database includes data from 9147 cases coming from 19 centres in 10 different countries. The main objectives of the ERIC CLL database are to (1) collect and transform clinically relevant RWD into evidence and correlate with biological data in order to provide accurate information about the state-of-the-art diagnosis; (2) generate hypotheses regarding important disease characteristics, laboratory studies and therapies; and, (3) define relevant parameters influencing CLL impact on health systems.
The data categories composing all the relevant information to form an accurate and complete representation of the disease course include basic demographic data, disease-related information, treatment options and response, laboratory results and relevant outcome data. The data model has been designed in order to meet the requirements for an accurate description of diagnosis, prognostic assessment and management of patients with CLL. A relational database developed in PostgreSQL has been designed in a way to provide data integrity and promote data correlations and statistical analysis. To the benefit of the scientific community at large, open-source tools have been used for the development of the ERIC CLL database. A standards-based approach is used to determine efficacy of data registration and integration, providing useful, accurate and valid information, increasing the availability of clinically relevant structured data and fulfilling the data quality assurance requirements.
A web-based user interface has been developed for prospective data collection as part of patient's routine care, designed to ensure data protection, security and availability. The interface allows for controlled database login, real-time registration with data validation mechanisms, data retrieval and management. Moreover, a retrospective data registration and import tool has been developed in order to efficiently and effectively load into the database retrospective patient data, collected in purpose-specific template registration spreadsheets. The tool deploys data cleaning processes based on certain rules ensuring content validation and detecting data inconsistency and redundancy errors. A mapping mechanim is then applied using transformation rules to convert data to predefined types and import them into the database in the appropriate form.
Accordingly, the tool can be configured and applied to transform exports of data coming from different sources, regardless the diversity of the software currently utilized in each single institution (ie, different databases), thus enabling interoperability. Personal identifying data are not requested or stored in the ERIC CLL database. Concerning the collection of retrospective data, anonymization of data takes place during the registration and validation processes; anonymized datasets are then saved and imported into the ERIC CLL database, conforming to EU regulations. Moreover, a user management system has been developed and configured for authentication and authorization of users to ensure data confidentiality and privacy, providing efficient and secure handling and exchange of information.
Results: CAPRIB has a user-friendly graphical interface and uses databases in SQL, making it easy to compare several genomes without the need for programming or thorough knowledge in bioinformatics. This intuitive software narrows down a list of amino acid changes that are concomitant with a given phenotypic divergence at the genus scale. Each permutation found by our software is associated with two already described statistical values that indicate its potential impact on the protein's function, helping the user decide which promising candidates to further investigate. We show that CAPRIB is able to detect already known mutations and uncovers many more, and that this tool can be used to question molecular phylogeny. Finally, we exemplify the utility of CAPRIB by pinpointing amino acid changes that coincided with the emergence of slow-growing mycobacteria from their fast-growing counterparts.
The software is freely available at https://github. com/BactSymEvol/Caprib. Conclusions: CAPRIB is a new bioinformatics software aiming to make genus-scale comparisons accessible to all. With its intuitive graphical interface, this tool identifies key amino acid changes concomitant with a phenotypic divergence. By comparing fast and slow-growing mycobacteria, we shed light on evolutionary hotspots, such as the cytokinin pathway, that are interesting candidates for further experimentations.
These point mutations have also helped establish the evolutionary relationships between several lineages and were even suggested to be useful as broad phylogenetic markers  [15, [21] [22] [23]. Compared to the study of gene flow, where several bioinformatics tools exist, studying the impact of small modifications is often perilous. This kind of investigation usually involves mapping reads or genomic sequences against a close reference and then looking at the impact on the coding sequences. Tools such as snippy (https:// github. com/tseemann/snippy) and snpEff  [24]  (used by snippy) allow this analysis efficiently. Other tools, for example kSNP3. 0 [25]  and the Harvest suite  [26] , integrate an evolutionary approach involving a phylogenetic reconstruction. However, these tools make it difficult to quickly analyze different datasets and to infer the impact of the AA changes without advanced expertise in bioinformatics.
More importantly, they are impractical to identify SNPs or single amino acid polymorphisms (SAPs) that are concomitant to a specific phenotypic change that arose within a bacterial genus. The more ancestral these subtle modifications are (such as those that arise at the birth of a specific genus), the more difficult their detection will be due to time effects. Here, we describe a new bioinformatics tool, CAPRIB (Comparative Analyses of Proteins In Bacteria), which efficiently finds through an easy-to-use graphical interface amino-acid changes that are correlated with the emergence of a phenotype. By comparing two groups of species separated by a phenotypic switch, this program can pinpoint the evolutionary events, at the genus scale, that may have been involved in this transition. This tool, whose core is in Java, uses a relational database in SQL to store raw information, allowing users to quickly change the structure of groups and save projects.
In order to help users decipher the impact of AA changes, CAPRIB integrates statistical values (Grantham's distance  [27]  and exchangeability score  [28] ) that predict the potential impact of one amino acid change for another. It is also possible to put in relation the detected AA changes and the Conserved Domain Database (CDD) of the NCBI to have an insight into the structural involvement of the permutations. Herein, we are specifically exemplifying the application of CAPRIB using the Mycobacterium genus from the Actinobacteria phylum. This genus is an ideal candidate to study bacterial evolution through amino-acids changes because of its genotypic and phenotypic diversity. First of all, mycobacteria species can be pathogenic, commensal or saprophytic in a variety of hosts and ecological niches  [29, 30].