dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on the distributions obtained using the typing, species-balanced and distinct-species datasets, taxonomic cutoffs can be proposed for tetranucleotide dissimilarities. Values below 0. 22 indicate that two samples belong to the same species, while values above 0. 36 indicate that two samples belong to different species. The 0. 02 threshold, while not optimal for all oligonucleotide lengths up to hexanucleotides, is sufficiently close to the best value and as such can also be used for those lengths without impacting the results significantly. When using hexanucleotides, the accuracy of the PaSiT method is similar to that of ANI (represented here by OrthoANI or MASH, depending on the size of the dataset) for distinguishing bacterial types while requiring less computational resources than even the modern equivalents.
A similar case was also observed for B. stabilis type-51, but was less pronounced. While this difference is likely due to mobile genetic elements, and as such does not necessarily impact the bacterial type when it is obtained using MLST, this difference would not have been apparent when using only ANI values. We also noted that comparison of the genomes of the different L. reuteri strains revealed ANI values below even the less conservative 94% similarity cutoff when comparing L. reuteri LMG 13088 with LMG 9213 T and LMG 18238, while values above this cutoff were found for all other pairs of the L. reuteri strains of the typing dataset. For both TETRA and our PaSiT4 method, all values are within the range of uncertainty. This shows that PaSiT4 provides an added value to ANI. To conclude, we developed a new alignment-free approach for computing genomic distances, called PaSiT.
This method utilizes the entirety of the genomic information and improves on the speed and-for selected datasets-on the accuracy of existing methods. PaSiT, as well as other oligonucleotide-based methods (a full list is provided in the software help) are implemented in a dependencyfree program called GenDisCal and an intuitive companion graphical user interface. Combined these tools can be used to produce a comprehensive types of signatures and calculates distances between them based on a specified algorithm.
The Galaxy HiCExplorer provides a web service at https://hicexplorer. usegalaxy. eu. It enables the integrative analysis of chromosome conformation by providing tools and computational resources to preprocess, analyse and visualize Hi-C, Capture Hi-C (cHi-C) and single-cell Hi-C (scHi-C) data. Since the last publication, Galaxy HiCExplorer has been expanded considerably with new tools to facilitate the analysis of cHi-C and to provide an in-depth analysis of Hi-C data. Moreover, it supports the analysis of scHi-C data by offering a broad range of tools. With the help of the standard graphical user interface of Galaxy, presented workflows, extensive documentation and tutorials, novices as well as Hi-C experts are supported in their Hi-C data analysis with Galaxy HiCExplorer.
RELATED WORKS Galaxy HiCExplorer is designed as an easy-to-use online service which is accessible through a web browser. Thus, no installation is required. By embedding it into Galaxy  (17)  and the https://usegalaxy. eu environment, it facilitates reproducible, shareable research as well as easily accessible data analysis. With Galaxy HiCExplorer, researchers can focus on their data analysis without facing any computational limitation or software dependency issue. To offer more flexibility, it is also possible to install Galaxy HiC-Explorer on a local Galaxy instance. Hi-C data processing and downstream analysis are supported by many tool suites, such as Juicer  (18) , HiCUP  (19) , HOMER  (20) , HiC-Pro  (21) , HiFive  (22)  and the recently published HiCeekR  (23). Juicer, HiC-Pro and HiCeekR offer several tools but are limited to a local installation.
HiFive offers a Galaxy integration, but lacks the support of external data formats like cool file format  (24). HiCUP and HOMER support only certain parts of Hi-C data analysis. Among the above tools, HiC-Pro is the only one with the ability to analyse cHi-C and HiChIP data. scHiCNorm  (25)  and scHiCluster  (26)  provide support for single-cell Hi-C data normalization and clustering, but suffer from the lack of a tool suite to guide researchers through the workflow of processing single-cell Hi-C data from the raw FASTQ files to the clustering of cells, including methods for building interaction matrices, quality control, dimension reduction and visualization. scHiC-Norm and scHiCluster use text files to store the scHi-C interaction matrices, which are particularly space consuming, not easily shareable and prone to error accumulation.
GALAXY HICEXPLORER Galaxy HiCExplorer offers a large collection of tools to pre-process, analyse and visualize Hi-C, cHi-C and scHi-C data. In addition to its assay-specific modules, users can benefit from the external pre-processing software for quality control of raw data and mappers such as BWA-MEM or Bowtie2 which are provided on the https://hicexplorer. usegalaxy. eu web server as well as the computational resources available. Moreover, for interactive Hi-C matrix exploration we have recently integrated HiGlass  (27)  into Galaxy. In the following, we briefly describe the new modules which have been added since our original publications on HiCExplorer 1 (28) and 2  (29).
Pre-processing. hicQuickQC. The creation of Hi-C interaction matrices, as well as the investigation of the quality of the data afterwards, may require a long processing time and is also resource intensive. To get a swift insight into the quality of Hi-C data, hicQuickQC has been introduced. It computes a quick summary of the Hi-C data quality using only a small subset of reads. The computation time to create the quality report with hicQuickQC for the first 1 million reads takes <3 min. The quality report is equal to the quality report of hicBuildMatrix and the only difference is that it is based only on the first 1 million reads instead of the full dataset. hicFindRestSites. Hi-C interaction matrices with fixed size bins are not always the best representation of the data. In fact, with a sufficient sequencing depth, bins of a restriction fragment size are a better alternative.
To generate such matrices, this tool generates a list of restriction sites for user-defined enzymes. This list can be used as an input to hicBuildMatrix to create restriction site resolution Hi-C matrices. hicConvertFormat. Support for external interaction matrix data formats is missing in most Hi-C data analysis software. This makes it difficult to compare matrices which have been built with different software and to directly use them for further analysis. Instead, the matrices need to be built from scratch, which is time consuming and potentially error prone. This tool supports loading matrices of cool, HiCExplorers h5, Juicers hic, Homer and HiCPro format and can convert them to cool, h5, Homer and ginteractions (30) format. hicNormalize. Normalization is a crucial step to be able to compare the interaction matrices obtained with a different sequencing depth.
Capture Hi-C The cHi-C modules of HiCExplorer are designed for analysing Promoter cHi-C and HiChIP. HiCExplorer will also accept data from other Capture Hi-C methods, including ChiA-PET  (34). if dedicated preprocessing steps were performed to obtain compatible mapping data. Furthermore, it can be used to generate virtual 4C plots from Hi-C data. As for Hi-C data, cHi-C interaction matrices are built with hicBuildMatrix. The regions of interest in these protocols, such as the location of the promoters in cHi-C or the binding sites of the target protein for HiChIP, are referred to as reference points. In the case of HiChIP, reference points are either annotated with peak calling tools, such as MACS2 (35) using either the HiChIP mapping file or ChIP-seq data, or regions (e. g. promoters) are manually selected.
Pre-processing. chicQualityControl. This module is designed to investigate the quality of every single viewpoint, taking the sparsity of the interaction counts into account. A viewpoint will be removed if the sparsity of the data at this viewpoint is lower than a given threshold. To help users in setting an appropriate threshold, the tool generates several quality plots from which one is presented in Figure  2E. chicViewpointBackground. The background model per relative distance is computed by taking all interaction counts of a relative distance over all viewpoints and samples into account. Based on this model, interactions with higher counts than an expected count will be identified during the downstream analysis.
Single-cell Hi-C Single-cell Hi-C explores how chromatin is being folded and which elements contribute to its regulation on a single-cell scale. While analyzing Hi-C data is computationally expensive, this can increase drastically for scHi-C data. The reason for this is the increase in the number of Hi-C interaction matrices that need to be analysed from one to several thousand, with a corresponding increase in runtime and memory. The read coverage of scHi-C data is currently not high (  36 ) and 1 megabase (Mb) resolution matrices are used to avoid generating highly sparse matrices. However, as sequencing costs decline, resolutions of 10 kb may be achievable and the demand for dimension reduction techniques, such as those presented here, will be indispensable. With scHiCExplorer, a software suite is provided to process single-cell Hi-C data offering tools for demultiplexing, matrix handling, correction, dimension reduction, clustering and visualisation.
Pre-processing. scHicDemultiplex. Raw FASTQ data from a single-cell experiment usually contains reads from multiple cells which are encoded with different barcodes. This tool supports demultiplexing of an interleaved FASTQ file into one FASTQ file per cell. The demultiplexing is implemented to support the method which has been introduced in Nagano (36) for barcoding. Due to the lack of a standard method on how to encode barcodes, presently, demultiplexing is limited to FASTQ files with the same barcoding method as in  (36). Other demultiplexing tools are part of the general Galaxy tool suite. scHicMergeToSCool. Every single-cell interaction matrix can be created with hicBuildMatrix. scHicMergeToSCool can merge individual matrices into a joint matrix in multicool format  (24) , which will be used in all subsequent downstream analysis and visualization tools.
While using the API of cooler, the data is not stored with multiple resolutions as it is defined by  (24). The cool file is used as a container format for the individual cool files of the Hi-C matrices. For this reason, the format is referred to as scool. scHicQualityControl. Since scHi-C data is a very sparse, not all matrices have sufficient read coverage to be considered for the downstream analysis. Thus, the quality control module removes interaction matrices of cells with total read counts below a user-specified threshold (see Figure  2E ) or very sparse interaction matrices. scHicCreateBulkMatrix. This tool supports to pool all matrices stored in the scool file to one single Hi-C interaction matrix and enables the analysis like in regular Hi-C. Several modules of HiCExplorer are also required in single-cell Hi-C data analysis.
To provide an equal functionality at the single cell level and to support the scool file format, scHiCExplorer reuses these modules from HiCExplorer. These are scHicNormalize, scHicCorrectMatrices, scHicAdjustMatrix, scHicMergeMatrixBins and scHicInfo. scHiCExplorer adds the functionality of handling the multiple matrices stored in the scool file and distributes the computations over several threads. Dimension and clustering reduction. Clustering cells is a common approach to study the difference between them and to learn about their relations from single cell data. scHiCExplorer provides the k-means and spectral clustering methods. K-means was used on scHi-C data by  (36)  or (  26 ), but the choice of a clustering algorithm is always dependent on the data. For this reason, scHiCExplorer provides additional the spectral clustering and will continue adding standard clustering algorithms in the future.
However, reducing the dimensions of the underlying matrices is necessary to be able to cluster cells in a reasonable amount of time and to decrease the memory footprint; as shown in Supplementary Table  S1. The usage of dimension reduction is also often necessary to achieve good results  (37) (38) (39). The results in the Supplementary Figures S1-S4 confirm this. The need to reduce the dimensions becomes obvious when matrices of higher resolutions are used. The combined raw data matrix for a scHi-C dataset has a dimensionality of cells*features, where features = bins*bins for one matrix. As an example, mapping of the Nagano 2017 (36) data on the mouse mm9 genome and using it to make a 1 Mb resolution matrix, will already return a matrix of 2500*7. 3 million dimensions; this number will increase to 2500*7.
IMPLEMENTATION Galaxy HiCExplorer is implemented as a collection of Galaxy tool wrappers and is available on the Galaxy Tool-Shed. The Galaxy integration is provided for HiCExplorer as well as scHiCExplorer. HiCExplorer and scHiCExplorer are both implemented in Python 3. 6 and are available on Bioconda  (16). The Knight-Ruiz correction and the Min-Hash approximate k-nearest neighbors for the dimension reduction are implemented in C++ and are also available on Bioconda.
Installation and usage Galaxy HiCExplorer can be used as a web server and is accessible via https://hicexplorer. usegalaxy. eu. All presented tools are publicly available and may be used without any required registration. Unregistered users are provided with 11 GB storage space, while registered users are granted 250GB. Registered users have the opportunity to apply for more storage. Users are strongly encouraged to use https: //hicexplorer. usegalaxy. eu web server if high compute resources are required. Galaxy HiCExplorer is GDPR compliant; deleted datasets will be permanently removed within 14 days and the data of unregistered users is deleted after an inactivity of 90 days.
, 2013)  and many have called for "the end of model democracy" (e. g. Held, 2005; Knutti, 2010). Although there is still considerable discussion about how to constrain 100 models using observations, and then how to combine and possibly weight models depending on their overall performance or performance against a minimum set of specific criteria (e. g. Eyring et al. , 2005; Tebaldi et al. , 2005; Gleckler et al. , 2008; Weigel et al. , 2008; Santer et al. , 2009; Parker, 2013; Abramowitz et al. , 2019) , it is clear that results from systematic evaluations are central to this process. A number of papers have examined specific aspects of the FireMIP baseline simulations. Andela et al. (2017) 105  showed that the FireMIP models do not reproduce the decrease in global burnt area over the past two decades inferred from analysis of version 4s of the Global Fire Emission Database (GFED4s) data product.
, 2019) , were able to reproduce the emergent relationship with human population density. However, the treatment of the anthropogenic influence on burnt area has been identified as a weakness in the FireMIP models  (Andela et al. , 2017; Teckentrup et al. , 2019; Li et al. , 2019; Forkel et al. , 2019a) , mainly due to a lack of process understanding. 125 In this paper, we focus on quantitative evaluation of model performance using the baseline historical simulation and a range of vegetation and fire observational datasets. We identify (i) common weaknesses of the current generation of global fire-vegetation models (ii) factors causing differences between the models, and (iii) priorities for future model development.
2. 2) Model Evaluation and Benchmarking Model performance was evaluated using site-based and remotely sensed global data sets of vegetation properties, fire occurrence and fire-related emissions (Figure  1 ; Figure  S1 ). We used multiple datasets as targets for variables where they were available in order to take into account observational uncertainty. We used the GFED4 150  (Giglio et al. , 2013) , GFED4s  (Randerson et al. , 2012) , MCD45  (Roy et al. , 2008) , FireCC4. 0 (Alonso-Canas and  Chuvieco, 2015)  and FireCCI5. 1  (Chuvieco et al. , 2018)  burnt area datasets; the Global Fire Assimilation System (GFAS) fire emissions  (Kaiser et al. , 2012) ; fire size and number from  Hantson et al. (2015) ; site-level net primary productivity (NPP) and gross primary productivity (GPP) from  Luyssaert et al.
(2007)  fluxnet GPP data  (Jung et al. , 2017; Tramontana et al. , 2017) ; aboveground vegetation biomass from Carvalais et al. (  2014 ) and  Avitabile et al. (2016) ; and Leaf Area Index (LAI) from MODIS MCD15  (Myneni et al. , 2002)  and AVHRR  (Claverie et al. , 2016). A complete description of the reference data sets used for benchmarking is given in the Supplementary Information S1 and all datasets are plotted in Figure  S1. For comparison and application of the benchmark metrics, all the target datasets and model outputs were resampled to a 0. 5° grid. Although some 160 models were run at a coarser resolution, the spatial resolution at which the benchmarking was performed had only a limited impact on the scores (Figure  S2 ), which does not affect conclusions drawn here. Each model was compared to each reference dataset except in the few cases where the appropriate model output was not provided (e.
g. LAI in ORCHIDEE, GPP in MC2). Only the models which incorporate the SPITFIRE fire module provided fire size and number results. 165 While we use multiple datasets to attempt to quantify observational uncertainty, this only addresses one area of observational uncertainty. Most observation-based datasets don't provide estimates of measurement error and uncertainty. As well, most datasets haven't been independently validated and could suffer from methodological issues affecting quality. Therefore, large uncertainties still remain for most variables, even well-established ones such as burnt area (e. g. Roteta et al. , 2019). When uncertainties of datasets are known one can take this into account 170 by weight the model scores less for the less trustworthy datasets (e. g. Collier et al. 2018).
However, as the uncertainty of most reference datasets are currently unknown, we haven't attempted that here and instead treat each dataset equally. As model benchmarking techniques become more sophisticated it would be beneficial to better evaluate the datasets the models are compared against to ensure the models are being benchmarked appropriately. We use the normalised mean error (NME), which is the mean absolute difference between a simulated and 175 observed variable normalised by the observational variance, to assess each model's ability to reproduce spatial patterns in annual average burnt area and the other variables that influence burnt area. We therefore removed the influence of biases in the mean and variance between model results and reference datasets (See Supplementary Information S2) because NME can be sensitive to the simulated magnitude and this furthermore limits the impact of observational uncertainties in the reference datasets.
Modern day model performance: burnt area and fire emissions The simulated modern  (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012)  total global annual burnt area is between 39 and 536 Mha (Table  2 ). Most 215 of the FireMIP models are within the range of the remotely sensed observed burnt area (354 to 468 Mha a -1 ). With the exception of MC2 and LPJ-GUESS-GlobFIRM, the models realistically capture the spatial patterns in burnt area (Figures  1  & 2 ) and perform better than either of the null models irrespective of the reference burnt area dataset (Table  3 ). CLM (NME: 0. 63-0. 80) and ORCHIDEE-SPITFIRE (0. 70-0. 73) are the best performing models. All the FireMIP models correctly simulate most burnt area in the tropics (24-466 Mha a -1 ) compared to observed 220 https://doi.
org/10. 5194/gmd-2019-261 Preprint. Discussion started: 8 January 2020 c Author(s) 2020. CC BY 4. 0 License. values in the range 312-426 Mha a -1 (Table  2 ). The simulated contribution of tropical fires to global burnt area is in the range of 56% to 92%, with all models except ORCHIDEE-SPITFIRE simulating a lower fraction than observed (89-93%). 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 225 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 , Figure  2 ).
The FireMIP models that include a sub-annual time-step for fire calculations (CLM, CLASS-CTEM, JULES-INFERNO, JSBACH-SPITFIRE, LPJ-GUESS-SPITFIRE, ORCHIDEE-SPITFIRE) generally reproduce the 230 seasonality of burnt area (Figure  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 both null models for seasonal phase in burnt area (Table  3 ). However, all of the FireMIP models perform worse than both null models for seasonal concentration of burnt area, independent of the reference burnt area dataset. The observations show a unimodal pattern in burnt area in the tropics, peaking between November through February in the northern tropics and between June through 235 October in the southern tropics (Figure  3 ).
The only exception is LPJ-GUESS-SPITFIRE, which simulates the peak ~2-3 months too late. The observations show no clear seasonal pattern in burnt area over the southern extratropics, although the most 245 prominent peak occurs in December and January. All the FireMIP models, except LPJ-GUESS-SPITFIRE, reproduce this mid-summer 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 data sets (Table  3 ). However, there is considerable uncertainty in the observed IAV in burnt area (Figure  4 ), and the 250 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 (Figure  4 ). 255 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 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.