dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection and selection In the western English Channel, the PN site was sampled each month from 1978 to 1981, and then five times per year from 1982 to 1996 (March, June, August, October, and December). The RM site was sampled during the five same months from 1980 to 1996. In the southern North Sea, the GV site was sampled four times per year (January/February, March/ April, June/July, and September/October) from 1989 to 2016. The same sampling procedure was applied at the three study sites. Ten replicates were collected at each sampling event using a 0. 1 m 2 Smith-McIntyre grab. Each sample was sieved through a 1 mm mesh sieve, and the material retained was fixed with a 10% buffered formaldehyde solution. In the laboratory, the organisms retained were identified to the species level, counted, and weighed.
Biomass per species was determined as dry mass (DM) at the PN and RM sites, and as ash-free dry mass (AFDM) at the GV site. Biomass in DM was converted to AFDM by applying conversion factors  (Brey et al. 2010)  to standardize the unit of biomass at the three sites. The 10 replicates were pooled for each station and the biomass expressed in g AFDM m -2 before data analysis. The biomass of a few species with low abundance was unknown at the species level and thus excluded from the analyses. We also excluded large individuals belonging to the "megafauna" size class (e. g. , Asterias rubens, Atelecyclus rotundatus, Lutraria lutraria) and species with a frequency of occurrence of < 5% over the entire time series. The remaining taxa represented 37 species at PN, 22 species at RM, and 46 species at GV, for a mean of 93% of the total biomass sampled at the three study sites.
We retained data from 1980 to 1996. In 1991, the American jack-knife clam Ensis directus was recorded for the first time at GV and successfully settled in the area  (Luczak et al. 1993; Luczak 1996). The biological invasion of Ensis directus deeply modified the structure and dynamics of the GV community until 1996  (Ghertsos et al. 2000). Thus, we retained data from 1997 to 2016 at GV, except for 2013, when inclement weather precluded winter sampling. Overall, the time series analyzed in this study covered 17 yr at PN and RM, and 19 yr at GV, with a sampling frequency of 4-5 times per year at each site.
Estimating production-to-biomass ratio and production We estimated annual secondary production P of each species using the empirical model developed by  Brey (2012). This model uses artificial neural networks (ANN) to predict P:B from three continuous parameters (mean body mass M in Joules, mean annual bottom water temperature in C, depth in m) and 17 categorical parameters (taxonomic, functional, and environmental parameters, and a marker for commercial exploitation). All categorical parameters are binary (0 or 1). The model is implemented in an Excel spreadsheet freely available at http://www. thomas-brey. de/science/virtualhandbook/ (version 01-2012, downloaded on 28 March 2017). To estimate each species' mean body mass M, we divided its mean annual biomass by its mean annual abundance and converted it to Joules using energy densities (kJ g -1 ) from a global database of conversion factors  (Brey et al.
2010). When no conversion factor was available at the species level, we used the conversion factor for the next higher taxonomic rank. Information about taxonomy and functional traits used as input parameters were collected from the literature and online resources, including the Integrated Taxonomic Information System database (https://www. itis. gov/), the Biological Traits Information Catalogue of The Marine Life Information Network (http://www. marlin. ac. uk/biotic/), and the World Register of Marine Species (http://www. marinespecies. org) (last accessed on 04 May 2018). The marker for commercial exploitation was always set to zero since no species has been commercially exploited at the three study sites. Depth was previously reported  (Fromentin et al. 1997). We estimated mean annual temperature from time series of sea bottom temperature recorded monthly or bimonthly at a fixed station near each study site  (Dauvin et al.
2005; Beukema and Dekker 2013). These production estimates were used as a reference and thus were implicitly assumed to have no error. Then, we mimicked the procedure usually followed by users of the Brey model by calculating annual production based on data from a single sampling event (e. g. , March, June, August, October, or December). We used mean annual sea temperature in all cases, since it is usually known, even when biological data come from a single sampling event (e. g. ,  Fuhrmann et al. 2015). Estimating P:B requires filling an Excel spreadsheet for each study site and sampling scenario (monthly data or annual mean data). Overall, we estimated 5384 P:B ratios, equivalent to 246 spreadsheets, which would have been extremely time-consuming to fill iteratively using Excel. Instead, we implemented the  Brey (2012)  model in R to optimize the procedure and calculated all production estimates directly in the R environment (R code and associated data supplied as Supporting Information).
Seasonal patterns at the population level Similar to those at the community level, a significant and consistent seasonal pattern was observed in 18 of the 30 populations analyzed in this study (Table  2 ). Seasonality was detected across a wide range of taxonomic groups (annelids, crustaceans, bivalves, and gastropods) and trophic guilds (suspensivores, deposivores, carnivores, and scavengers). The model 2 explained a large part of the variability in the data (adjusted R 2 = 0. 731). The seasonal pattern in biomass had the same general shape as that observed at the community level for all the populations that showed significant seasonal variations (Fig. 3 , Supporting Information Fig. S5 ). However, they differed greatly in amplitude and, to a smaller extent, in timing. Minimum biomass was almost always recorded in late winter (March-April), unlike peak biomass, which varied from early summer (June-July) to late summer/early fall (September-October).
g. , in December at PN and June at RM), and high and positive in summer/early fall (overestimation of P) (Supporting Information Table  S4 ). If community biomass was sampled at its annual minimum, underestimates of P reached 47%, 25%, and 58% at PN, RM, and GV, respectively. Conversely, if biomass was sampled at its annual maximum, overestimates of P reached 46%, 23%, and 29% at PN, RM, and GV, respectively (Supporting Information Table  S4 ). We found that bias could differ significantly among sites even for the same sampling month. For instance, P estimates based on data collected in August were slightly underestimated at PN (MPE = -7%) but significantly overestimated at RM (MPE = 23%). Both bias and inaccuracy were lower at RM than at the other two sites (Fig. 4 , Supporting Information Table  S4 ).
Acknowledgments We acknowledge  Electricité de France (EDF) and Elvire Antajan (IFREMER)  for providing the data from Gravelines, which were collected in the framework of the research program  IGA  ( Impact des Grands Aménagements ) conducted by  IFREMER  with financial support from EDF. Data from the Bay of Morlaix were collected in the framework of the  Programme GDR Manche  of the CNRS, supported by  IFREMER  ( Réseau National d'Observation ) contracts. We thank the captain and crew of the ship " NO Mysis ," the staff of the  Marine Biological Station of Roscoff  for the field work, and many students for their technical help. We greatly thank  Thomas Brey  for precious advice on the use of his empirical model, and  Verena Trenkel  and  Etienne Rivot  for fruitful discussions on statistical analyses and theoretical considerations.
Two of the most cited tools for de novo motif discovery, MEME-ChIP and RSAT peak-motifs provide a userfriendly interface and have been used to successfully identify transcription factor binding sites  [18, 20]. Deep-SEA also offers an online web search interface, but input sequences are currently limited to 1000 base pairs (bp) and only queries against the Human Genome (hg19)  [22]. Trawler_standalone is one of the fastest motif discovery tools available, while still providing accurate predictions  [5] , however it is currently only available as a command-line standalone version  [6]. Here we present TrawlerWeb, which streamlines de novo motif discovery with NGS datasets from a wide range of species. This web-based version provides three new unique features that allow it to streamline and facilitate the analysis of predicted motifs: 1) in addition to FASTA-formatted sequences, it accepts direct input from ChIP-seq experiments in BED format, 2) it automatically generates a set of background sequences matching the input sequences in terms of genomic location and 3) it allows the ranking of predicted motifs by conservation score to select those more suited for downstream experimental validation.
Input file The web interface for TrawlerWeb offers users the option of uploading input sequences as BED indexed format, in addition to FASTA files, which is the most common form of input offered by most motif discovery tools. BED files are lists of genomic intervals and are the direct output from ChIP-seq experiments. Users can therefore directly input the data of their ChIP-seq experiments in TrawlerWeb, without having to retrieve corresponding FASTA sequences. Note that PSCAN  [21]  also offers BED file input but performs motif discovery on known motifs rather than de novo. DeepSEA  [22]  accepts input files as BED format, however, currently only one genome (hg19) is supported and input sequence length is limited to 1000 bp. Since peaks obtained from ChIP-seq on histone modifications, for example, could exceed 1000 bp, this option might be restrictive to users.
At present, TrawlerWeb remains the only online motif Regions can be either uploaded as BED files or directly pasted into the website, with users required to select the genome assembly of their desired model organism. To date, TrawlerWeb supports 16 genome assemblies (Table  2 ), downloaded from EnsEMBL  [26]. To streamline analysis of data generated from ChIP-seq experiments and other DNA-binding assays, FASTA sequences corresponding to the input BED regions are automatically extracted from locally stored chromosome assemblies. Prior to retrieving FASTA sequences, the BED regions are processed using BEDtools' merge tool with default settings to avoid duplicated locations  [27]. FASTA sequences are repeat-masked to prevent the discovery of repetitive motifs produced from lowcomplexity and repeat regions  [5].
Background file For each set of input BED regions, a randomised background specific to the dataset is automatically generated to match the distribution of the genomic locations of the input regions. This input-matched background allows TrawlerWeb to account for sequence biases present in certain genomic regions (for example promoter regions are known to be enriched in CpG islands  [29] ). To generate a customised background dataset, first, TrawlerWeb calculates the distribution of the distances of the input regions with respect to the nearest transcription start site (TSS). Each input region is associated to a 'nearest gene' and the distance of this region to the gene's TSS is calculated using gene coordinates downloaded from EnsEMBL using BioMART  [26, 30]  for the given organism. Distances are then plotted across discrete ranges (e.
g. -5000 bp to 0, 0 to 5000 bp, etc. ) to produce a frequency table representing the input regions. Next, genes are randomly selected from the entire genome and genomic regions are extracted upstream or downstream of the TSS, so that the distribution of the selected regions match the frequency table generated for input regions. This frequency table is displayed on the results page as TrawlerWeb is running. The amount of randomly selected regions to generate this background dataset is eight times the amount of regions in the input, which we have previously demonstrated to robustly provide adequate background dataset  [5, 6]. As a new background will be generated for each new input submission, users have the option to download the background FASTA sequences generated for a specific FASTA input, should they need to re-run the exact same analysis using the same background.
Output files and data download The final results are summarised as a web page displaying all discovered motifs in a table along with zscores for statistical over-representation  [5]. Putative matches against known TFBSs and maximum sequence conservation of the motif 's instances will be displayed in this summary web page. By default, motifs are ordered in decreasing order of conservation score, however any of the columns can be used for sorting the output. For each identified motif, further information is available upon clicking on the motif name of logo. First, the distribution of the motif locations within the input sequences can be visualised as a histogram. Consensus sequence, length and identity of the TFBS match are displayed in the next table where a mouse-over on the column titles will provide a description of the properties of the match.
A link to the original TFBS will allow to visualise the putative hit from either UniPROBE, Jaspar and HOCO-MOCO databases  [31] [32] [33] [34]. The final table lists the location of every instance of the predicted motif in the input sequences, along with an average and maximum conservation score within the instance. Each location is linked to a dynamic view of the region in the UCSC genome browser  [35]. Similarly, a mouseover on the column's title will provide further information. All tables in this detailed page are sortable by column, searchable and are dynamically filtered for the searched term. The entire results webpage and files generated by TrawlerWeb are also available for download in a single zip file.
TrawlerWeb runs the fastest amongst popular web-based motif discovery tools We aimed to compare the performance of TrawlerWeb with the popular web-based de novo motif discovery tools RSAT peak-motifs  [20, 36]  and MEME-ChIP  [18]. For this, 11 users were given five different ChIPseq datasets from five commonly used model organisms in FASTA format (Table  3 ). The same FASTA input file was used across the three different programs with the same background file used for TrawlerWeb and RSAT peak-motifs, default background was used for MEME-ChIP as it does not allow for custom background FASTA input. All other options were kept as default. Running time was recorded from when the "Submit Query", "GO" or "Start Search" button was clicked for TrawlerWeb, RSAT peak-motifs and MEME-ChIP respectively, until the final list of motifs was returned.
This also includes the queuing time which realistically reflects the actual waiting time experienced by users. For all five species tested (Table  3 ), TrawlerWeb ran the fastest (Fig. 1a ) in accordance with its standalone version  [5, 6]. TrawlerWeb generally returned the discovered motifs in less than 2 min (min) for the four smaller datasets Danio rerio (Dr), Arabidopsis thaliana (At), Mus musculus (Mm) and Drosophila melanogaster (Dm). Only for the human dataset (Hs), being the largest input file tested, running time ranged from 4 min to over 21 min, averaging 10 min overall. RSAT peak-motifs identified motifs after 2-10 min with only a few outliers. Motif discovery for the human dataset ran for 17 to 22 min, however, one run was completed in under 7 min. Nonetheless, we experienced larger variations in processing times with RSAT peak-motifs compared to TrawlerWeb.
MEME-ChIP had the longest running time among the three tested tools. Typical motif discovery was completed on average after about 30 min, with the exception of Hs which finished after up to almost 1 h. Of note, MEME-ChIP provides by default two different algorithms, MEME and DREME. DREME  [37]  discovers short and ungapped motifs. Since MEME did not find any motifs for the zebrafish dataset (Dr) we used the results provided by DREME, which can explain the shorter time compared to the performance of MEME. In conclusion, in all tested cases, TrawlerWeb outperformed popular web-based de novo motif discovery tools in terms of speed.
TrawlerWeb accurately identifies the expected binding site To measure TrawlerWeb's accuracy against other online motif discovery tools, we tested TrawlerWeb against a yeast ChIP benchmark dataset  [38]. This same dataset was previously used to test Trawler_standalone algorithm against other software  [5, 6]  but lacked RSAT peak-motifs  [20] , which we have included in this run. On this yeast dataset, TrawlerWeb outperformed all other methods in terms of accuracy, identifying 54 out of 65 motifs previously found (85%; Fig. 1b  and  Additional file 1: Table  S1 ). These results were expected since TrawlerWeb and Trawler_standalone share the same underlying algorithm. In order to further characterise TrawlerWeb's accuracy to identify the expected binding sites, we compared the motifs predicted by each tool, across eight users, to the known binding site of the transcription factor of interest, for the five ChIP-seq datasets (Table  3 ).
To identify which predicted motif best matched the known binding site, all predicted motifs with the two highest z-scores (Trawler-Web) or lowest e-values (RSAT peak-motifs, best of 6 nucleotide or 7 nucleotide length, and MEME/DREME) were aligned and clustered with the expected binding sites (Table  3 ) using STAMP (default options)  [39]. For each ChIP-seq dataset, and for each program, motifs with the shortest distance to the expected binding site were identified as the closest motif. Across all users, the closest primary and secondary motifs (Fig. 2a-e ), and corresponding distance to the expected binding site (Fig. 2f ), were downloaded from STAMP in Newick format and visualised with iTOL using the expected motif as a reference. In two cases out of five, TrawlerWeb and RSAT predicted the known binding sites (for Zic3.
1, the motifs identified by MEME-ChIP have low similarity to the known binding sites. For Zic3. 2 MEME-ChIP did not find any motifs, hence we used DREME, which found two motifs that are quite distant from the expected binding site. Of note, in three out of five datasets, TrawlerWeb identifies the predicted motif from the highest number of input sequences. When comparing over-represented motifs (based on the percentage of motif occurrences of the highest score) discovered using DREME, MEME, RSAT peakmotifs, and TrawlerWeb (Fig. 1c  the highest percentage occurrence in dm3, hg19 and tair9 test datasets (65. 5%, 37. 6% and 36. 6% respectively), and delivers occurrences comparable to both MEME and RSAT peak-motifs in the mm9 and zv9 datasets (1. 6% and 0. 3%) (Additional file 2: Table  S2 ). Variations in motif occurrence could be due to the presence of a secondary motif corresponding to a co-factor of the TF of interest  [11].
We have implemented this feature in TrawlerWeb whereby when input files are provided in BED format, corresponding genomic coordinates are matched against the reference genome selected. This provides the unique advantage for Traw-lerWeb over other tools to display conservation scores for every instance of motifs discovered. To display conservation scores, PhastCons scores were downloaded from UCSC  [35]  and stored along with the chromosome sequences for each organism. For each instance of the identified motif in the input sequences, the average and maximum conservation score is calculated using the "bigWigOverAverage" tool provided in kentUtils  [42]  package from UCSC. Version 305 of kentUtils source used as it offers "minMax" option to calculate the maximum value for conservation. The average and maximum conservation scores calculated from PhastCons scores are automatically displayed in the output (Fig.
However, it can also be extended for identification of microRNA targets  [6, 43] , RNA-binding protein targets  [44]  or coexpression groups  [45]. In conclusion, TrawlerWeb will appeal to a wide range of fields as the breadth of genome assemblies supported include commonly studied model organisms (Table  2 ). However, this list is currently limited to organisms for which conservation scores are available. To circumvent the need of a conservation score, users are still provided with the option to use FASTA formatted files with TrawlerWeb. Using FASTA input expands the range of analysis that can be performed with TrawlerWeb, by permitting analysis of datasets from partially sequenced genomes and from non-model organisms.
Availability of data and materials The ChIP-seq datasets analysed in this study are available in the GEO repository under ascension numbers GSM1017643, GSE66225, GSM1377538, GSM878068, GSM1536045,  [40, [46] [47] [48] [49]  and collected under https://github. com/Ramialison-Lab-ARMI/Trawler-2. 0/tree/master/test_data/bedfiles. The datasets generated during the study for testing purposes are available on GitHub at https://github. com/Ramialison-Lab-ARMI/Trawler-2. 0/tree/ master/test_data.
In the last decades the microbial food safety community has developed a variety of valuable knowledge (e. g. , mathematical models and data) and resources (e. g. , databases and software tools) in the areas of quantitative microbial risk assessment (QMRA) and predictive microbiology. However, the reusability of this knowledge and the exchange of information between resources are currently difficult and time consuming. This problem has increased over time due to the lack of harmonized data format and rules for knowledge annotation. It includes the lack of a common understanding of basic terms and concepts and of a harmonized information exchange format to describe and annotate knowledge. The existence of ambiguities and inconsistencies in the use of terms and concepts in the QMRA and predictive microbial (PM) modelling necessitates a consensus on their refinement, which will allow a harmonized exchange of information within these areas.
A C C E P T E D M A N U S C R I P T 6 based on key words and filter functions. A conceptual metadata framework can be represented in the form of a metadata schema that may require a list of controlled vocabularies for certain metadata concepts  (Sowa, 2000). This paper follows the metadata framework and aims to to provide the foundation for the harmonization of key terms and concepts and to establish a conceptual framework for consistent annotation of knowledge from the QMRA and PM modelling domain. The harmonized terms are important for the creation of an open information exchange format what we call Food Safety Knowledge Markup Language (FSK-ML).
Establishment of a coherent metadata schema On the basis of a common understanding of the high-level terms and concepts, a comprehensive list of metadata concepts was created that allows to annotate with sufficient detail any type of model or data in the QMRA or PM modelling domain. To accomplish this, at first, existing software tools like FDA-iRISK®, ICRA (Interactive online Catalogue on Risk Assessment; URL: http://icra. foodrisk. org/) and PMM-Lab (Predictive Microbial Modelling Lab, URL: https://foodrisklabs. bfr. bund. de/pmm-lab/) were analysed on the metadata required to annotate QMRA or PM data or models. In addition, other relevant sources like Dublin Core Metadata Initiative (URL: http://dublincore. org/), RIS format specifications (URL: goo. gl/AuPxQU) and vCard data format (URL: https://tools.
A C C E P T E D M A N U S C R I P T 15 Product/Matrix. A total of 386 terms are available to describe precisely this metadata concept according to its origin such as animal or vegetal among others. In addition some vocabularies used in domain-specific software tools like PMM-Lab, ICRA and FDA-iRisk®, were also included. Some terms for specific metadata have been taken from other ontologies or public standards. For example for the Publication type/status metadata, we use the Bibliographic Ontology Specifications (URL: http://bibliontology. com/); for metadata on parameter distributions the probONTO  (  https://goo. gl/wbFoZU).