dataesr/scientific-paragraphs-categorization huggingface dataset:
org), KEGG (https://www. genome. jp/ kegg/) and MetaCyc (https://metac yc. org). Many other resources are also useful to perform expert curation of genome annotation, such as pipelines of the PGAP-6. 3 family (https://github. com/ncbi/pgap/releases). However, accuracy will depend heavily on the quality of manual annotation of reference genomes such as the one described here. Gene expression begins with transcription, and a variety of RNAs have significance in this process: BSGatlas integrates and unifies multiple existing annotation resources involving 'non-coding' RNAs (ncRNAs), while improving the positional annotation for 70% of the ncRNAs and focusing on precise identification of operons  (Geissler et al. , 2021). Several other RNA-centred databases provide further information of specific features of the genome.
For example, the MODOMICS database updates RNA modifications annotation (de  Crécy-Lagard et al. , 2020). Some but far from all RNA genetic objects (mainly riboswitches and well identified regulatory RNAs) have been introduced in the present annotation. Besides the B. subtilis instantiation in MetaCyc, BsubCyc (https://bsubc yc. org), GapMind is a Web-based tool for annotating amino acid biosynthesis in the Bacteria and the Archaea clades (http://papers. genom ics. lbl. gov/gaps). GapMind incorporates many variant pathways and 130 different reactions, and it analyses a genome in just 15 s. To avoid error-prone transitive annotations, GapMind relies primarily on a database of experimentally characterized proteins  (Price et al. , 2020). In the domain of transcription, the ReGPrecise database captures, visualizes and analyses the transcription factor-dependent regulons as reconstructed by comparative genomics in a variety of prokaryotic genomes, B.
subtilis included  (Novichkov et al. , 2013). Machine learning can be used to identify prophage and horizontally transferred regions  (Sirén et al. , 2021). Finally, a database collecting metabolites (small molecules but also some macromolecules) that interact with transcription factors can be used to improve annotation by associating a particular transcription factor with its effectors  (Koch et al. , 2018). The corresponding knowledge has been used here as much as possible.
, 2021). For this reason, some genes are missing from databases. The lack of identification of some CDS start sites affected a few entries in the B. subtilis genome annotation. The lysC aspartokinase gene encodes a heterodimeric enzyme composed of two subunits, alpha and beta. The CDS of the alpha subunit (lysCA) begins with an upstream AUG codon, while the CDS of the beta subunit (lysCB) begins with a codon located downstream within the same frame  (Kalinowski et al. , 1991). A similar situation arises for a second aspartokinase, DapG: two CDSs, dapGA and dapGB code for the two subunits of the protein. Both are now properly annotated in the sequence. Metabolic accidents are the rule, not the exception  (Danchin & Sekowska, 2015; Lerma-Ortiz et al. , 2016)  and this constantly generates non-canonical amino acids in the cell.
MsrC (YtsP) is a widespread reductase experimentally identified in E. coli and other Enterobacteria. It is specific for reduction of the free form of Met-(R)-sulfoxide, complementing other methionine sulfoxide reductases (e. g. MsrA and MsrB) that act on proteins or peptides. MsrC belongs to the UPF0067 family and is similar to the Saccharomyces cerevisiae enzyme  (Lin et al. , 2007). This enzyme may be particularly important to protect B. subtilis against oxygen stress in the phylloplane. Finally, PepA(YuiE) is a leucyl aminopeptidase demonstrated in Pseudomonas aeruginosa and E. coli where its 3D structure is known. It is conserved in M. mycoides Syn3. 0. Unexpectedly, the enzyme belongs to a recombination complex in E. coli. This enzyme is also involved in UV tolerance in a cyanobacterium where it exhibits cysteinyl-glycinase activity.
These ions apparently circulate in the water channels generated by the macroscopic wrinkles of the biofilm. They may thus behave as propagated signals triggering vegetative in situ cell multiplication or sporulation  (Azulay et al. , 2022). CalJ(YetJ) is the B. subtilis bacterial homologue of human Bax-inhibitor 1 protein (hBI-1) involved in calcium leak control between the endoplasmic reticulum, organelles and cytoplasm. AceP is the counterpart present in E. coli, identified as an acetate transporter, belonging to the UPF0005 family. Structural characterization of the protein reveals different conformations depending on the pH. It forms a closed compact structure at pH 8 and an open conformation at pH 6, forming a pore through the lipid bilayer. Overexpression of CatJ in E. coli and with CatJ proteoliposomes reveals a pH-dependent calcium-leak activity.
CONCLUSION: TOWARDS A R ATIONAL MINIMUM BAC I LLUS SU BTI LIS GENOME Here we have proposed an eclectic representation of the annotation progress that has been associated to the reference genome of B. subtilis 168 deposited at the INSDC under accession number AL009126. 4. Approximately one-third of the genes of unknown function are clustered in 25 genomic regions containing at least 5 genes. They correspond mainly to prophage regions. Their contribution may be important for industrial uses of this organism, and we can expect a better understanding to develop in the coming years. The region encoding ICEBs1 is important for the management of interactions between B. subtilis and other organisms, plants in particular. Understanding the details of the behaviour of this element, particularly during biofilm formation, is greatly affected by the ability of the donor and recipient to form a biofilm.
Agnieszka Sekowska: Investigation (equal); resources (equal); validation (equal); writing -review and editing (equal). David Vallenet: Conceptualization (equal); data curation (equal); methodology (equal); project administration (equal); resources (lead); software (lead); supervision (lead); validation (equal); writingreview and editing (equal). Abril Zuñiga: Investigation (equal); validation (equal). Cristal Zuñiga: Data curation (equal); supervision (equal); validation (equal); writingreview and editing (equal). A C K N O W L E D G E M E N T S The French Bioinformatics Institute national infrastructures (funded as part of Investissement d'Avenir program managed by Agence Nationale pour la Recherche, contracts ANR-11-INBS-0013 and ANR-21-ESRE-0048) is acknowledged for support within the MicroScope annotation platform.
The manuscript style has been reviewed by Marc Abrams, publication coordinator of B. O. P laboratory. Note that because we consider accurate genome sequence annotation as critical for the future of biology we did not prioritize the role of each of the authors of this paper and named them in alphabetic order. F U N D I N G I N F O R M AT I O N This work was supported by a grant from the National Institutes of Health (R35GM122461) to J. D. H. and grants from the Agence Nationale pour la Recherche (ANR-11-INBS-0013 and ANR-21-ESRE-0048) to C. M. and D. V. C O N F L I C T O F I N T E R E S T S TAT E M E N T None of the authors revealed a conflict of interest in this work devoted to a model organism.
A detailed description of the model can be found in the Supporting Information (S3 and S4, Tables  S1-S3 ). Food web dynamics are calculated using Julia (version 1. 6. 1,  Bezanson et al. , 2017)  and the DifferentialEquations package  (Rackauckas & Nie, 2017) , utilizing a solving algorithm based on the fourth-order Runge-Kutta method. The code used in this study is available at https:// github. com/Georg Alber t/Spati alFoo dWebBEF.
Funding information Agence Nationale de la Recherche , Grant/Award Number:  ANR-17-CE32-0002/ECOSTAB ;  Deutsche Forschungsgemeinschaft , Grant/Award Number:  202548816  and  319936945 ;  National Natural Science Foundation of China , Grant/Award Number:  31988102 AC K NO W L E DGE M E  N T S We thank the members of the  EcoNetLab  for helpful discussions. GA was supported by the  International Research Training Group TreeDì  funded by the  German Research Foundation  (DFG;  GRK 2324/1 -2018 (P9 ),  319936945 ). ET was supported by the  Agence Nationale de la Recherche  ( ANR-17-CE32-0002/ECOSTAB ). SW was supported by the  National Natural Science Foundation of China  ( 31988102 ). GA, BG, RR and  UB  gratefully acknowledge the support of  iDiv  funded by the  German Research Foundation  ( DFG-FZT 118 ,  202548816 ).
Statistical postprocessing of medium-range weather forecasts is an important component of modern forecasting systems. Since the beginning of modern data science, numerous new postprocessing methods have been proposed, complementing an already very diverse field. However, one of the questions that frequently arises when considering different methods in the framework of implementing operational postprocessing is the relative performance of the methods for a given specific task. It is particularly challenging to find or construct a common comprehensive dataset that can be used to perform such comparisons. Here, we introduce the first version of EUPPBench (EUMETNET postprocessing benchmark), a dataset of time-aligned forecasts and observations, with the aim to facilitate and standardize this process.
Some of these methods have been adapted to deal with ensemble forecasts and also calibrate the associated forecast probabilities, like the ensemble MOS (EMOS) method  (Gneiting et al. , 2005). Recently, machine learning-based methods have been proposed  (Taillardat et al. , 2016; Rasp and Lerch, 2018; Bremnes, 2020) , which were shown to improve upon the conventional methods  (Schulz and Lerch, 2022). Systematic intercomparison exercises of both univariate (e. g. ,  Rasp and Lerch, 2018; Schulz and Lerch, 2022; Chapman et al. , 2022)  and multivariate (e. g. ,  Wilks, 2015; Perrone et al. , 2020; Lerch et al. , 2020; Chen et al. , 2022; Lakatos et al. , 2023)  postprocessing methods exist, often based on artificial simulated datasets mimicking properties of real-world ensemble forecasting systems or based on real-world datasets consisting of ensemble forecasts and observations for specific use cases.
However, there is no comprehensive, widely applicable benchmark dataset available for station-and gridbased postprocessing that facilitates reuse and comparisons, including a large set of potential input predictors and several target variables relevant to operational weather forecasting at meteorological services. The aim of the present work is to pave the way towards achieving these aims, with the publication of an extensive -analysis-ready -forecast and observation dataset, consisting of both gridded data and data at station locations. By an analysis-ready dataset, we mean that the dataset formatting is tailored to obtain the most optimal match between observations and forecasts. In practice, this means that the observations are not provided as conventional time series but rather at the times and locations that match the forecasts.
Recently, the need for a common platform based on which different postprocessing techniques of weather forecasts can be compared was highlighted  (Vannitsem et al. , 2021)  and extensively discussed between several members of the expert team of the postprocessing module running within the programs of the European Meteorological Network (EUMET-NET). Here, we introduce the first step in the development of such a platform, in the form of an easily accessible dataset that can be used by a large community of users interested in the design of efficient postprocessing algorithms of weather forecasts for different applications. As stated in  Dueben et al. (2022) , comprehensive benchmark datasets are needed to enable a fair, quantitative comparison between different tools and methods while reducing the need to design and build them, a task which requires domain-specific knowledge.
In this view, common benchmark datasets facilitate the collaboration of different communities with different expertise, by lowering the energy barrier required to embark on specific problems which would have otherwise required an excessive and discouraging amount of resources. Many datasets related to weather and climate prediction were released during the last 3 years, emphasizing the need and appetite of the field for ever more data. For instance, datasets have been published related to sea ice drift  (Rabault et al. , 2022) , to hydrology  (Han et al. , 2022) , to learning of cloud classes  (Zantedeschi et al. , 2019) , to subseasonal and seasonal weather forecasting  (Rasp et al. , 2020; Garg et al. , 2022; Lenkoski et al. , 2022; Wang et al. , 2022) , to data-driven climate projections  (Watson-Parris et al.
, 2022) , and -most relevant to the present work -to the benchmarking of postprocessing methods  (Haupt et al. , 2021; Ashkboos et al. , 2022; Kim et al. , 2022). Haupt et al. (2021)  distribute a collection of (partly preexisting) different datasets for specific postprocessing tasks, including ensemble forecasts of the Madden-Julian Oscillation, integrated vapor transport over the eastern Pacific and western United States, temperature over Germany, and surface road conditions in the United Kingdom. By contrast,  Ashkboos et al. (2022)  provide a reduced set of global gridded 10-member ECMWF ensemble forecasts for selected target variables. Providing weather-or climate-related datasets to the scientific community in a standardized and persistent way remains a challenge, which was recently simplified by the introduction of efficient tools to store and provide data to the users.
We can mention, for example, xarray  (Hoyer and Joseph, 2017) , Zarr  (Miles et al. , 2020) , dask (https://github. com/dask/dask, last access: 24 April 2023) and the package climetlab (https:// github. com/ecmwf/climetlab, last access: 24 April 2023), recently developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). The dataset introduced in the present article is, for instance, provided by a climetlab plugin (https://github. com/EUPP-benchmark/ climetlab-eumetnet-postprocessing-benchmark, last access: 24 April 2023) but also accessible through other means and programming languages (see the Supplement). The EUPPBench (EUMETNET postprocessing benchmark) dataset consists of gridded and point ECMWF subdaily forecasts of different kinds (deterministic highresolution, ensemble forecasts and reforecasts) over central Europe (see Fig.
1 ). EUPPBench encompasses both stationand grid-based forecasts for many different variables, enabling a large variety of applications. This -complemented Earth Syst. Sci. Data, 15, 2635 Data, 15, -2653 Data, 15, , 2023  https://doi. org/10. 5194/essd-15-2635-2023 by the inclusion of reforecasts -enables a realistic representation of operational postprocessing situations, allowing users and institutions to learn and improve their skills on this crucial process. These operational aspects are, to our knowledge, missing in the currently available postprocessing benchmark datasets. The forecasts and reforecasts of EUPPBench are paired with station observations and gridded reanalysis for the purpose of training and verifying postprocessing methods. To demonstrate how this dataset can be used, a benchmark of state-of-the-art postprocessing methods has been conducted to improve medium-range temperature forecasts.
Although limited in scope, the outcome of this benchmark already emphasizes the potential of the dataset to provide meaningful results and provides useful insights into the potential, diversity and limitations of postprocessing over the study domain. Additionally, performing the benchmark for the first time with a large community also allows us to address the usefulness of the established guidelines and protocols and to draw conclusions, which are important assets for the delivery of many more benchmarks to come. This article is structured as follow. The dataset structure and metadata are introduced in Sect. 2. The design and the verification setup of the benchmark which was carried out upon publication of this dataset is explained in Sect. 3, while in Sect. 4 the benchmarked methods are detailed.
EUPPBench v1. 0 dataset The EUPPBench dataset is available on a portion of Europe covering 45. 75 to 53. 5 • in latitude and 2. 5 to 10. 5 • in longitude. Therefore, this domain includes mainly Belgium, France, Germany, Switzerland, Austria and the Netherlands. It is stored in Zarr format, a CF-compatible format (https://cfconventions. org/, last access: 24 April 2023)  (Gregory, 2003; Eaton et al. , 2003) , which provides easy access and allows users to "slice" the data along various dimensions in an effortless and efficient manner. In addition, the forecast and observation data are already paired together along corresponding dimensions, therefore providing an analysis-ready dataset for postprocessing benchmarking purposes. EUPPBench includes both the 00:00 Z (midnight) subdaily ensemble forecasts and reforecasts  (Hagedorn et al.
This includes the forecast control run which is assumed to have the closest initial conditions to reality. The choice of the years 2017 and 2018 was motivated by the relatively small number of model changes in the ECMWF forecast system during that period and, most importantly, the absence of model resolution modifications, as shown by Table 1. This implementation constraint is crucial to ensure that no supplementary model error biases are introduced in the datasets, as those biases can lead to a more-or-less severe degradation of the postprocessing performances  (Lang et al. , 2020; Demaeyer and Vannitsem, 2020). The forecast and reforecast time steps are 6-hourly (including the 0th analysis time steps) up to a lead time of 120 h (5 d). The variables considered are mainly surface variables and can be classified into two main categories: instantaneous or processed.
General data structure The EUPPBench dataset consists of observations and forecasts in two types: a gridded dataset and a dataset at station locations. All forecasts are based on the ECMWF IFS forecasts. However, while the observational dataset at the station locations is based on ground measurements, the reanalysis ERA5 is taken as the gridded observational dataset. All forecast and reforecast datasets are provided for 31 variables, and additionally, the forecast dataset includes 9 EFI variables. The observations, on the other hand, include only 5 and 21 variables for the station-location and gridded datasets, respectively. Additionally metadata on the model and observations are provided. How to access the datasets is documented in the Sect. 6. We now detail in the following subsections the sources and properties of both dataset formats.
Gridded data All gridded EUPPBench data are provided on a regular grid of 0. 25 • × 0. 25 • , corresponding roughly to a 25 km horizontal resolution at midlatitudes. As mentioned before, the forecasts and reforecasts are provided by the ECMWF forecasting model in operation at the moment of their issuance. They have both been regridded from the ECMWF original ensemble forecasts O640 (or O1280 for the deterministic forecasts) 2  grid to the regular grid using the ECMWF MIR interpolation package  (Maciel et al. , 2017) , provided automatically by the MARS archive system. This regridding was done to be in line with the resolution of the ERA5 reanalysis  (Hersbach et al. , 2020) , which provides the gridded observations of the EUPPBench dataset. We recognize that gridded observational datasets over the study domain exist for specific variables that are more accurate than ERA5.
For instance, in the case of precipitationrelated variables (like the total precipitation contained in the dataset at hand), ERA5 has been shown to provide -compared to other datasets -a poor agreement with station observations  (Zandler et al. , 2019) , mixed performances when used to derive hydrological products  (Hafizi and Sorman, 2022 ), yet good results when using perfect prog downscaling methods  (Horton, 2022). Notwithstanding, we emphasize that the goal of this gridded dataset is to provide a representative "truth" for the purpose of benchmarking of postprocessing methods. Additionally, the availability of a wide range of variables in ERA5 and the spatiotemporal consistency among different meteorological variables (a very important aspect in the present context) cannot be provided by gridded observational datasets.
EUPPBench data at station locations Subdaily station observations have been provided by many national meteorological services (NMSs) participating in the construction of this dataset, and a big part of the station data can be considered open data (see Sect. 6). The observations of 234 stations cover the entire 22-year time period 1997-2018 necessary to match the reforecasts and forecasts. The elevation of these stations varies from a few meters below the sea level up to 3562 m for the Jungfraujoch station in Switzerland. These stations constitute the most authoritative sources of information about weather and climate provided in each of the involved countries, being constantly monitored and having the quality of the data checked. The EUPPBench dataset at station locations consists of the ECMWF forecasts and reforecasts at the grid point closest to the station locations and the associated observations, matched for each lead time.
Static data and metadata In addition to the forecasts and reforecasts, auxiliary fields are provided, such as the land usage and the surface geopo-tential which is proportional to the model orography (see Fig. 2 ). Table  5  synthesizes this part of the dataset. These constant fields have been extracted and are also provided in the station metadata. Depending on the kind of dataset, dimensions and different information are embedded in the data. For gridded data, the metadata available in the forecast, reforecast and observation datasets are detailed in Table  6. For station data, the forecast and reforecast metadata are detailed in   the observation metadata are detailed in Table  8. For all data, attributes specifying the sources and the license are always provided.
Postprocessing benchmark To illustrate the usefulness of the EUPPBench dataset, a benchmark of several state-of-the-art postprocessing methods -many of which are currently in operation in NMSswas performed, including some more recent and more advanced methods. This first exercise was based on a small subset of the dataset. Along the same line, the verification process of this benchmark also focused on some general aspects typically considered for operational postprocessing. In this section, we describe the general framework that we used to conduct this benchmark. The following sections will be devoted to the methods and the results obtained. We begin by detailing the design of this experiment.
Experiment design The postprocessing benchmark at hand considers the correction of the ensemble forecasts of the 2 m temperature at the nearest forecast grid point from every station available in the dataset, spanning several European countries and the whole EUPPBench area. We note that this area includes orographically difficult regions, nearly flat plains, and also stations close to the sea or located on islands. Discrepancies may therefore occur between forecasts and observations due to poor observational representativity at the scale of the model or due to challenges in the model representation of a wide range of physical processes. We note also that, due to the coarse nature of the gridded forecast dataset, the forecast grid points are not evenly situated with respect to the stations they represent, with sometimes huge differences in elevation or situation (e.