dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, we know of multiple consortia working on harmonisation of catalogue metadata (e. g. , the EUCAN-connect project tries to harmonise Maelstrom and LifeCycle catalogues, in collaboration with the CINECA project). However, these efforts are not linked to existing standards. Instead of creating these common data elements in isolation, it is recommended to reuse existing metadata item definitions. A good starting point is the standard website FAIR sharing  [37]  and ontology publishing websites like BioPortal [https://bioportal. bioontology. org/] and Ontology Lookup Service [OLS, http:// www. ebi. ac. uk/ols]. Ideally, the catalogue community will go through a rigorous ontology process to define all metadata types and instances while reusing existing ontologies, for example following examples from other domains  [38] , towards an ontology with high standards for quality control, for example using the standards of OBO foundry  [39].
Syntaxes and Architecture for Metadata Interoperability Standardisation of catalogue contents using common data elements is not sufficient to make data flow. In addition, the standardisation of file formats would be recommended to ease the download/upload of data between catalogues, and application programming interfaces (APIs) to automate such interactions using scripts and programs. The gold standard for FAIR data is linked data or 'semantic web'. This for example includes RDF [resource description format, https://www. w3. org/RDF/], a data model and framework that can be represented in a file format similar to HTML files but with a specific structure to describe all data as 'triples' of {subject, predicate, object} using hyperlinks/URIs to denote cross references. However, in our experience, most catalogue developers have very limited information technology (IT) capacity, and therefore prefer using CSV files, Excel spreadsheets or JSON text files.
Notably, JSON has a linked data extension called JSON-LD, which would still enable integration with the aforementioned linked data communities where desired. In addition, we must consider the architectures for data exchange. In recent years, we have implemented both push interfaces -where data submitters would push metadata into the catalogue -and pull interfaces -where the catalogue developer would retrieve data from a web location. Push interfaces are typically used for submitting/updating catalogue records (e. g. , from individual cohort into LifeCycle catalogue) while pull interfaces are more often used for moving data between catalogues (e. g. from the Dutch national catalogue, https:// catalogue. bbmri. nl, to the BBMRI-ERIC EU biobank catalogue, https://directory. bbmri-eric. eu/). In most cases, simple interfaces using the HTTP protocol were preferred, using operations such as GET, POST, PUT/ PATCH, DELETE to retrieve, add, update or remove contents respectively, ideally enabling 'batch' updates, i.
Conclusion We have summarised the landscape of data catalogues cataloguing, on the one hand, cohorts and on the other, RW data sources composed of multiple data banks, such as surveillance registries or administrative records. While these catalogues are heterogeneous, of various scope, and use different terminologies, the underlying concepts seem potentially harmonizable. Our conclusion is that cohort catalogues can be nested in RW catalogues where: (1) cohorts are a special case of a data source with a single data bank, having a set of research questions embedded in the data bank design, and having as population the set of persons whose data are included in the cohort, (2) collection events for cohorts are a special case of prompt for RW data, and (3) harmonisation of items is a special case of the conversion to a common data model, because in RW data sources additional information must be stored, that is, how to derive study variables to address research questions.
Representation of data collections. Concept Unit of data collection Data collection to conduct a study Purpose of the data collection Organisation sustaining the data collection Population Unit of observation within the data collection Timing of data collection Items collected Codes used in categorical values Domain Research Cohort, Cross sectional sample Cohort Research Organisation having research as a mission Conceptualised as the persons whose data is collected. It is a selected sample of the general population, and selection criteria are associated with a broad set of research questions Person (study subject) Once, or longitudinal (collection events) Associated with a broad set of research questions Often research-specific code systems from commonly used surveys or tests RW data Data bank Data source Other purposes (e.
Research institution often lacking any control over data collection. Could be one or more institutions with expertise to conduct studies on the data Independent of original reason for data collection Population selected using derived data Items observed or derived algorithms Concepts involved to enact interoperability across multiple data collections to address a research question. Data processing (measure/phenotype) to derive study variables on top of the common data model IMIA Yearbook of Medical Informatics 2022 Swertz et al.
Acknowledgements We acknowledge contributions from  BBM-RI-ERIC (common services for IT), BBM-RI-NL  ( NWO 184. 021. 007 ) European Commission  ( H2020 ,  FP7 ),  Innovative Medicine Initiative (IMI) ,  European Medicine Agency  and national scientific organisations (in particular  Canadian Institutes of Health Research, CIHR)  to this work, in particular collaborations with EUCAN-connect ( H2020  and  CIHR  grant  824989 ),  ConcePTION  ( IMI 821520 ), MINERVA,  ReaCH  ( CIHR OCR-144561 ),  LifeCycle  ( H2020  grant  733206 ),  RECAP Preterm  ( H2020  grant  733280 ),  LongITools  ( H2020  grant  874739 ),  ATHLETE  ( H2020 874583 ),  BioSHaRE  ( FP7 HEALTH 261433 ),  CINECA  ( H2020  grant  825775 ),  EOSC-Life  ( H2020 824087 ). We thank  Susana Perez-Gutthann  for useful discussions.
, 2019). The output datasets can also vary greatly in both their nature and format depending on manufacturer and model. Furthermore, because the provision of DFAD buoy data to scientists is still conducted at regional or national level, the characteristics of the dataset can also depend on the details of the specific data exchange agreement between industry and the respective national authority. Although some processing protocols have already been proposed  (Maufroy et al. , 2015; Orue et al. , 2019a) , they have primarily been applied to single buoy types and uniform datasets. The application of these methods can quickly become limited when faced with datasets that mix several fishing fleets, brands and models of buoys. Just like the intensive work undertaken a few years ago on VMS data filtering procedures, (e.
, 2017; Escalle et al. , 2021 Escalle et al. , , 2019;; Imzilen et al. , 2022 Imzilen et al. , , 2021)). Hence, a processing framework to transform this vast amount of industrial data into harmonized information, notably through standard procedures, independent of the characteristics of the databases, appears to be of primary importance. In this study a set of processing algorithms were proposed and applied to the raw data provided by the buoys from the two major tropical tuna purse seine fleets (French and Spanish) in the Atlantic and Indian oceans. The outcomes of the filtering algorithms were compared between the various buoy models and heterogeneous buoys location data formats that make up the two databases.
Buoy data Buoy data has been collected in the Atlantic and Indian Oceans, under specific data-exchange agreements signed between different research organizations (i. e. AZTI and IRD) and EU tuna purse seine associations (i. e. ORTHON-GEL 2  for the French purse-seine fleet, Echebastar and Atunsa companies in ANABAC 3  and OPAGAC 4  for Spanish fleets), under the framework of the EU project RECOLAPE 5. A dataset was created for each fleet and ocean. They consisted of information collected by a sample of 1000 buoys during a random month of the year 2016. Since the objective of this work was not to compare the characteristics of the databases between fleets, buoy manufacturers or research organizations, but rather to define a common processing protocol, the datasets examined for each fleet (resulting from specific data exchangeagreements) were referred to as D1 and D2.
Datasets characteristics Table  1  provides the list of the different buoy brands, as well as a description of the raw data contained in each dataset. Common information includes the buoy identification code, hour, date, position (latitude and longitude) and buoy speed. The other data types are dependent upon the buoy brand and data-exchange agreements. In each of the two oceans, large differences between the composition of buoy models available in the different fleet datasets (D1 and D2) are observed (Fig. 1 ). The D1 datasets is largely dominated by a single model of buoy, especially in the Indian Ocean, but between two and four models were present, depending on the Ocean. The D2 datasets is more heterogeneous, with roughly twice as many buoy Table  1. Description of the raw position and acoustic data received from the various buoy manufacturers and under the different data-exchange agreements (D1 and D2).
models, and two to three dominant models in each ocean. The temporal resolution of the buoy location data is also characterized by significant variability depending on the datasets. The D2 datasets is limited to a single location per buoy per day, regardless of the ocean or buoy model. This is contrasted by a higher resolution in the data of the D1 datasets, with between 3 and 9 location data per day for both oceans, depending on the buoy model (Fig. 2 ).
Stage 1: Structural errors filtering Structural errors, such as duplicate or irrelevant rows in the dataset, resulting from failures during data collection or transfer are defined at this stage. Three types of structural errors, principally related to failures during satellite communication, were flagged in the databases. (i) Filter F1: Duplicate rows Duplicate data refer to rows with identical buoy codes, timestamps and positions. Generally, all other information in the duplicate rows remain strictly identical, however in rare cases, missing values may occur for some lines. Duplicates were identified based on their identical buoy codes, timestamps and locations. In the cases of missing data, the row kept, and considered as the original, was the one with the most complete information. (ii) Filter F2: Ubiquitous rows Ubiquitous rows consisted of cases where two rows have identical buoy codes and timestamps, but different locations (Supplementary Fig.
1 ). Rows with these characteristics were identified and the distance between locations was calculated. When the two positions were separated by less than 1 km, a randomly selected row was retained while the other was considered "ubiquitous". Otherwise, the two rows were assigned as "ubiquitous". (iii) Filter F3: Isolated positions Positions separated from their nearest neighbors on a buoy track, by more than 48 h (considering the general time resolution in the data collection) or having an inconsistent speed (considering a threshold of 35 knots, a value far above speeds of both tuna purse seine vessels and ocean currents), were referred to as isolated positions (Supplementary Fig. 2 ). By addressing the buoy track (a collection of positions belonging to a single GPS buoy), this filtering step allowed the identification of a distinct series of consecutive positions (segments) separated by potential GPS failures, buoy relocations or buoy deactivation/reactivation events on a given buoy track.
Stage 2: Filtering of land positions (Filter F4) Buoys located on land (due to beaching events or active buoys brought back to port) were detected using shoreline data from the GSHHG database (Global Self-consistent, Hierarchical, High-resolution Geography;  Wessel and Smith, 1996). The influence of different shoreline resolutions on the filtering procedure was assessed through the comparisons of results from low and high-resolution shorelines (see details in  Wessel and Smith, 1996)  buffered with 0. 05°(Supplementary Fig. 3 ).
Stage 3: Filtering of "on-board"/"at sea" buoy positions (Filter F5) Echosounder buoys can be activated and transmit on-board vessels prior to their deployment. Similarly, buoys retrieved from the sea may continue to collect data on-board vessels for variable durations. In order to discriminate "on-board" from "at sea" positions, two different approaches were compared. The first approach was based on a rule-based algorithm using the buoy's speed as the main classification variable. This was referred to as the "kinetic classification algorithm". The second applied a random forest model  (Breiman, 2001)  trained using a learning dataset containing information from a single buoy type (Model 9, Tab. 1). The two algorithms classified the data into three classes: "on-board" (for buoys emitting while onboard a vessel), "at sea" (for buoys deployed in the water), and "undetermined" (a subset of positions that remained unclassified).
Conversely, if the comparison is not significantly different from "transition sequences", its status will be classified as "at sea". This classification step is first performed from neighbor to neighbor, moving along the buoy segment. The same procedure is then carried out backwards (from the end of the trajectory to the beginning), considering the remaining unclassified positions (Supplementary Fig. 5 ). (ii) Random forest approach (RF) The RF approach was derived from the procedure developed by  Orue et al. (2019a). The learning dataset was built using data from a single buoy model (model 9, Tab. 1), which have a conductivity sensor to detect when the buoy is immersed in seawater. The RF classification model was constructed using (i) distance between two consecutive points,   (ii) buoy speed; (iii) change in speed; (iv) acceleration, (v) azimuth (degree), (vi) change in azimuth (degree) and (vii) time since the first and last observation of the corresponding buoy trajectory (days), as predictive variables.
F5 outputs: on-board / at sea classifications Cross-comparisons of classifications performed by random forest and kinetic algorithms resulted in high matching coefficients for the four datasets. The two approaches showed very strong agreements for the D2 dataset in the Indian Ocean (99%), and D1 datasets in both oceans (more than 96%). The weakest agreement (94%) was observed for the D2 dataset collected in the Indian Ocean (Tab. 4). Less than 0. 5% of positions from the D1 datasets remained unclassified after processing with the kinetic algorithm. This value was considerably higher for the D2 datasets. For the Indian Ocean, 10 times more unclassified data were observed than in the D1 dataset while for the Atlantic Ocean this figure rose to more than 30 times that of the D1 dataset (Tab. 5).
The first step towards the exploitation of this data is to ensure an adequate level of quality in the data provided. Achieving this requires an appropriate protocol for processing this industrybased data, originally only intended for use at vessel or fleet level, into standardized data that can be utilized for research and management purposes. In this work, the proposed protocol Example illustrating speed changes in constant ("on-board" -"on-board", "at sea" -"at sea"), and transition states ("on-board" -"at sea", "at sea" -"on-board") from the D1 dataset in the Atlantic Ocean. Values represent the number of data in each sequences. focused on the location data provided by the buoys. It used a set of filters, which were applied to different datasets with highly varied structure and characteristics, resulting from the various data-exchange agreements between national research institutes and fleets, as well as the buoy specificities.
The first set of data processing filters (i. e. structural filters) targeted possible satellite communication errors. Despite isolated and ubiquitous positions being rare (<1%), they were present in all datasets. Conversely, duplicated data, which had to be filtered to avoid being counted twice, were only detected in the D1 datasets (both in the Atlantic and in Indian Oceans). The percentage of data recorded on land showed only minor changes with respect to the resolution of the shoreline data. Lower resolutions could potentially lead to a slight underestimation of location data collected on land, particularly in the Indian Ocean, due to the possible removal of small reefs and islands. However, due to the vast amount of data to be handled (e. g. the "Marine Instruments" buoys operated by the French purse seine fleet, represents, a raw data volume of around 150 million entries for the 2010-2018 period), and the subsequent computational costs required for their processing, the use of low-resolution data should not be excluded, should the study allow it.
e. buoy speed and its variation, see  Orue et al. , 2019a) , are also the main parameters on which the KiC algorithm is based. Nevertheless, the random forest algorithm produces a higher number of unclassified positions than the KiC algorithm, as the first or the last positions of the analyzed buoy segments are systematically unclassified due to the impossibility of calculating the predictive parameters associated with them. For the KiC algorithm, unclassified positions are due to both the presence of very short trajectories and the low temporal resolution of data. Since the KiC algorithm analyzes the buoy segment, searching for characteristic changes in speed between neighboring positions, its performance depends on the temporal resolution of the data. Consequently, the larger number of unclassified positions produced by the KiC algorithm in datasets D2, compared to D1, results from the lower temporal resolution of these datasets (only one position per day compared to the higher temporal resolution of datasets D1).
, 2016; Orue et al. , 2019b; Baidai et al. , 2020b). Finally, the main objective of this paper was to propose a standard protocol for processing buoy location data, particularly with respect to datasets that include a mix of fishing fleets and buoy brands and models. Although the provided protocol was designed from the most commonly used buoy models in tropical tuna fisheries, this work could be further improved by extending it to all existing satellite buoy data, including other brands and models for which data availability remains limited to date. However, through its main objective, as well as the definition of a standard data format for buoy location data (Supplementary Tab. 1), as a starting point for the implementation of a general standard format suitable for all the data collected by buoys, this work provides the basis for the development of a dedicated software environment for the processing, analysis, visualization, and possibly the estimation of fish biomass from echosounder buoy data, in order to simplify their use for both scientific and management purposes.
uffered shoreline data from the GSHHG database (Global Self-consistent, Hierarchical, High-resolution Geography;  Wessel and Smith, 1996). The red dashed line represents the buffer zone around the shoreline. The red points correspond to buoy positions classified as "on board" after the first step of the KiC algorithm, given their buoy speed above 6 knots. (C) The step 2 of the KiC algorithm relies on the comparison of changes in buoy speed with those found for "constant" and "transition sequences. Here, the value of the speed change between the first undetermined position following a classified position is consistent with a transition sequence. The undetermined position is therefore classified as "at sea". (D) The operation is performed along the buoy segment, classifying positions from neighbor to neighbor.
Acknowledgments. This project was funded by the  RECOL- APE  project ( MARE/2016/22  " Strengthening regional cooperation in the area of fisheries data collection" Annex III "Biological data collection for fisheries on highly migratory species "). IRD scientists were also supported by the  BLUEMED  project  ANR  project  BLUEMED  ( ANR-14-ACHN-0002 ). We would like to thank the fishing companies (ORTHONGEL, Echebastar and Atunsa companies in ANABAC and OPAGAC) that shared the buoy data and buoy providers ( Marine Instruments, Satlink and Zunibal ) for the useful exchanges on the buoys technical characteristics.
Parameter Value N 10,000 t f 100 dt 0. 01 v 0 8 µ 1 η 10 τ 5 v 0 8 F adh 7 F rep 40 R eq 1. 1 R 0 1. 6 L 364 ρ = Nπ(R eq /2) 2 /L 2 0. 07 when they form distinct slugs. The time scale corresponding to the formation of separate aggregates, rather than the asymptotic behavior of the system, is thus what we are concerned with. The self-propelled particles model was implemented through a custom Python program (https://github. com/Math ieuForget/HeteroSpeed_Aggregation. git). Model's parameters (collected in Table  1 ) were set as described in the Supplementary material to reproduce the aggregation of a fraction of particles in multiple non-motile aggregates, with some particles remaining isolated, similar to what is observed for aggregative multicellular species  (O'Connor and Zusman, 1991; Dubravcic et al.
Data Analysis Secondary metabolite gene clusters were predicted using BAGEL  [40] [41] [42] [43]  and anti-SMASH  [44] [45] [46] [47] [48]. Genome clustering was conducted on the MicroScope platform which uses an approach that originates from network science called community detection. The resulting pairwise inter-genomics distances, which are approximately equal to 1-ANI (Average Nucleotide Identity), are used to construct a tree using the neighbour-joining method  [39]. The heatmaps were constructed with the R package ComplexHeatmap (version 2. 12. 1, sourced at Bioconductor)  [49]. Hierarchical clustering of the inhibitory profiles was performed by using Euclidean distances and the average method with the R package ComplexHeatmap  [49]. Simple linear regression modelling, descriptive statistics, ANOVA, and Tukey's test were performed in base R  [34].
Accession Numbers The accession numbers of enterocin F4-9, carnobacteriocin BM1, carnobacteriocin B2, piscicolin 126, hiracin JM79, lactococcin 972, are BAR87969, AAA23014. 1, AAB81310. 1, AAX21354. 1, Q0Z8B6. 1, WP_000726502. 1, respectively. The genome sequences and annotations are publicly available on the MicroScope MaGe web portal (Table  S1 , https: //mage. genoscope. cns. fr, last accession on 21 July 2022).
29). This is due to strains with low sender degrees and high inhibition The matrix was constructed with the data produced for 73 strains by  [33]  and the three additional strains C. maltaromaticum 10040100629, 3Ba. 2. II, and DSM20590. The supernatant of sender strains (in rows) was added to cultures of the receiver strains (in columns) in order to measure their impact on the growth of the receivers. The color scale from purple to yellow represents increasing GII values. On the X-axis the strains are sorted in the descending order of sender degree from the left to the right. On the Y-axis, the strains are sorted in descending order of receiver degree, from the top to the bottom. The strains for which the genome was analyzed in this study are indicated with a grey box next to the name of the strains.