dataesr/scientific-paragraphs-categorization huggingface dataset:
Data exploration All assigned sequences present in the system are searchable via the exploration interface, which makes it possible to work simultaneously on any combination of projects from the selected database. Color codes are applied to sequence-level, samplelevel, and assignment-level fields for quick identification. This versatile interface provides means to combine filters on any of the fields added via project imports. Various kinds of advanced filtering widgets are thus proposed depending on the field's data type: r plain lists for text fields containing ≤1,000 distinct values; r autocompleting lists for text fields containing >1,000 distinct values; r minimum-maximum ranges for numeric and date fields; r tree-based selector for the taxonomy field; r visual geographic map selector for the sample collection lo- cation field, based on Leaflet  [21] , OpenStreetMap  [22] , and Carto  [23]  technology.
Data export and phylogenetic assignment Once a dataset of interest has been selected, it can be downloaded in the same formats as supported for imports: a FASTA sequence file, and tab-delimited text files providing sample metadata, sequence composition, or assignment information. Data can also be exported in the popular BIOM  [25]  format, thus allowing easy manipulation of exported data in a variety of visualization or analysis tools such as Phinch  [26]  or Calypso  [27]. Because this format enforces a precise and limited set of taxonomy ranks, sequence metadata are enriched with a field named "full taxonomy" that can include ranks beyond those defined in the BIOM format, e. g. , several ranks associated with virus classification. Exports are automatically compressed into zip archives and may be either directed to the client computer for direct download or temporarily materialized as physical files on the web server.
In the latter case, a download URL is provided, making it easy to share with collaborators or feed into external systems. Indeed, next to the export button, a "sharing" icon provides means to configure "online output tools" to which metaXplor will be able to push exported data. As an example, this feature is compatible with Galaxy  [28]  data sources and thus allows any exported file to be transferred into a Galaxy history by a simple button click. The metaXplor instance administrator can configure  ≤5  default output tools, and each user can define a custom one for his personal purpose. This feature will facilitate conducting online analyses from selected datasets. When a FASTA file is exported to the web server, the application offers to run a phylogenetic assignment on its contents. The user is then invited to either select a reference package  [29]  among those provided by the system (de novo generated or obtained from paprica  [30] ) or upload a custom refpkg archive.
A nucleotide sequence alignment is first applied using MAFFT  [31]  v7. 313 before pplacer  [32]  v1. 1. alpha19 proceeds with positioning exported sequences onto the existing reference tree. Then, guppy  [32]  v1. 1. alpha19 is used for sequence classification ("classify" option) and to generate an XML version of the pplacer tree ("fat" option). Last, Archeopteryx. js [33] is invoked to display an interactive solution for the end-user to investigate the results. After classification is performed, users with write permissions on any involved project have the facility to save newly found assignments to the database, thus enriching its contents for the benefit of all users. Figure  2  illustrates the user-friendliness of the phylogenetic placement feature.
Running BLAST or Diamond against database contents Another section in the application provides means to search for similarity between an external set of sequences and those present in the system, the latter being used as a reference bank. Available algorithms are BLAST v2. 6. 0 and Diamond  [34]  v2. 0. 4. Job results consist of a standard BLAST output file per selected target project, which can be investigated online in an interactive manner thanks to the BlasterJS  [35]  library, as shown in Fig. 3. Matching sequences can also be downloaded in FASTA format for further analyses (e. g. , alignment, viral genome reconstruction). Several BLAST types are supported: BLASTx (comparison of a DNA query sequence, after its translation into the 6 possible frames, with a protein sequence database) with Diamond as a faster alternative, BLASTp (comparison of a protein query sequence with a protein sequence database) with Diamond as a faster alternative, BLASTn (comparison of a DNA query sequence with a DNA sequence database), tBLASTn (comparison of a protein query with a DNA database, in the 6 possible frames of the database), and tBLASTx (comparison of the 6-frame translations of a nucleotide query sequence with the 6-frame translations of a nucleotide sequence database).
Application architecture outline The software architecture of metaXplor (Fig. 4 ) can be described as follows: r A standard HTML/Bootstrap/jQuery  [36, 37]  interface allows users and administrators to conveniently interact with the system; r One or several MongoDB  [38]  servers are used as a persis- tence layer for data that are searchable via the "Explore" interface, i. e. , all data except actual nucleotide sequences. Because MongoDB is a scalable solution that provides means to index >60 fields per collection, fast response times can be ensured even when running highly combined queries on large amounts of data; r A high-performance computing (HPC) server running Ora- cle/Sun Grid Engine (SGE)  [39]  holds nucleotide and protein BLAST banks for each set of sequences involved in a project.
This entity is responsible for running all CPU-intensive jobs except those that are database-related: BLAST/Diamond bank creation and query, phylogenetic assignment;    plor and orchestrates data flow by interpreting user input, building database queries and sending them to MongoDB, invoking SGE via Opal Toolkit  [41]  web services, building GUI views and contents, compiling export files, and so forth. This component also keeps an indexed fasta file per project to allow quick access to nucleotide sequences when browsing/exporting data. For metaXplor design, we focused on durability, maintainability, and extendibility by electing an industry development paradigm based on proven open-source standards such as the Spring Framework and Apache Tomcat. Regarding database needs, NoSQL seemed to be the best-suited solution for handling large datasets, and, more precisely, the MongoDB choice was found relevant because of its robustness, scalability, and schemaless design, which proved helpful in supporting userdefined fields.
To our eyes, its large developer community also gives it status as a standard. To ease deployment, the system is made available as a set of Docker  [42]   r HPC container: based on the official Docker image for Apache Tomcat, it embeds all tools required for detaching CPUintensive jobs from the main web application. Thus, it features additional software such as SGE for job management (via an integration based on the docker-sge Dockerfile  [43] ), Opal Toolkit for interfacing with the latter, and all abovementioned bioinformatics programs; r Web application container: also based on the official Docker image for Apache Tomcat, it features the main metaXplor web application (Java back-end, HTML/Javascript interface). This solution offers much flexibility in the sense that metaXplor can be straightforwardly configured in accordance with available hardware, from a minimal set-up on a workstation for testing purposes to a production environment where each container would run on a machine optimized for its purpose.
Data model In metaXplor, structured data (examples of the content of collections involved in the exploration functionality being given in Fig. 5 ) are organized in MongoDB as follows: r A single "commons" database per instance contains collec- tions holding reference data shared by all projects: NCBI taxonomy, accession-to-taxon mapping cache (described below), and reference package descriptions; r Each metagenomic database added via the system consists of the following collections: projects (with attributes specified at import time), dbFields (list of dynamically added fields according to import file contents), samples, sequences (unassigned), assignedSequences (embedding assignments), and various cache collections (1 for BLAST results, 1 for phylogenetic assignment results, 1 for taxonomic trees, and 1 for each searchable field).
The central model entity in metaXplor's database structure is the "sequence. " Each one originates from ≥1 "samples," as defined in the sequence composition file. For instance, a singleton read sequence would originate from only 1 sample whereas contigs may have been assembled using reads from various samples. Operational taxonomic unit representatives would also relate to the various samples in which they were detected. Each sequence comes with ≥0 "assignments. " Those that have none are stored separately as unassigned sequences and can only be BLASTed against, but not searched via the exploration interface. For those linked to several assignments, the presence of a best hit flag per assignment method is required for 1 of these assignments. This is then taken into account when exporting in the BIOM format, and, as mentioned before, for build-  ing taxonomic trees or pies, which require a single taxon to be associated with each sequence.
Imported assignments may be directly provided with a taxonomy id field. If not, they are required to be linked to ≥1 NCBI accession IDs. When a single ID is provided, the system attaches its corresponding taxon to the assignment record. In the case of multiple accession IDs, which typically occurs with metabarcoding data, the first common ancestor of their associated taxa is selected. To efficiently perform this mapping task while supporting large data imports, the following mechanism was designed: r The taxonomy associated to each of our ∼860,000 cached accession numbers is taken from SILVA-curated  [44]  when available, otherwise from NCBI's taxonomy database; r Accession-to-taxonomy associations are stored as a cache that is first consulted when assignment records are imported; r For accessions not found in the cache, their details are pulled from Entrez web services and added to it; r Finally, assignment records are persisted with accession and taxon information.
e. , the so-called dark matter  [45] , referring to sequences not having any detectable similarity with existing classified sequences, this functionality provides means to confront both classified and unclassified sequences from distinct projects. Finally, as an opensource, web-oriented multi-user platform, the system is adapted for collaborative work and data sharing as illustrated by the possibility to push exported data into external tools such as Galaxy. Thus, at a time when making scientific data FAIR (Findable, Accessible, Interoperable, and Reusable) is becoming a priority, we believe that metaXplor will prove useful in many ways. In future versions, we will consider adding support for further visualiza-Downloaded from https://academic. oup. com/gigascience/article/10/2/giab001/6126034 by INRAE Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement user on 06 May 2021.
Methods: The systematic map will follow the Collaboration for Environmental Evidence (CEE) Guidelines. We will collect the relevant peer-reviewed and grey literature in French and English language. The scientific literature will be retrieved with the use of a search string in two publication databases, one environmental and one social science database (Web Of Science Core Collection, and Cairn. info). We will also perform supplementary searches (search engines, call for literature, search for relevant reviews). All references will be screened for relevance using a three-stage process, according to a predefined set of eligibility criteria. Our study will concentrate on urban areas at the infra-city scale in cities of the temperate biogeographical zone. The subject population will include terrestrial species and ecosystems, except for archaea and bacteria.
The exposure will consider all types of urban forms described by any urban descriptors or measures including heterogeneity, fragmentation, housing density, organisation of urban matrix, urban fabric) and all types of urban features (e. g. size, age of the buildings, materials, urban artefacts). All relevant outcomes will be considered (e. g. species richness, abundance, behaviour). We will provide an open-access database of the studies included in the map. Our results will also be presented narratively, together with tables and graphs summarising the key information coded from the retained articles (e. g. study characteristics, types and areas of research that has been undertaken, types of exposure, population concerned, etc. ).
Search string The search string based on PECO (Population, Exposure, Comparator, Outcomes) keywords in English was built based on a scoping exercise conducted in "Web of Science Core Collection" database in June 2019. A detailed description of the PECO elements that we used for the systematic map is presented in Table  1. Search terms were initially identified from articles of a test-list and then discussed and validated by the review team. They were organized into four blocks [see Additional file 2 search string complete with excluded words].
Comprehensiveness of the search To check the performance of the search strategy, a test list of 30 scientific articles (presented in Additional file 3) was initially obtained from experts of the review team, from various existing reviews, by searching on Google, and in grey literature sources. On the basis of the criteria listed above, we chose to do the bibliographic search on two databases: • "Web of Science Core Collection" on the Web of Science platform (Clarivate) to which the review team had access via the team's members institution (INRA subscription of HS). As explained above, a scoping exercise was conducted using this database. Our search string was run on 6th of June 2019 and covered SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI and CCR-EXPANDED, without any timespan restriction.
• "Cairn. info" to which the review team had access via the team's members institution. This social sciences database includes studies published in French and in English. Another scoping exercise has been conducted. The search string was made using the following terms: (urban form OR urban morphology OR infra-city OR city OR town) AND (biodiversity OR animals OR plants). Our search string was run on 7th of June 2019 without any timespan restriction. A total of 29,523 articles were listed in both databases. 26,672 of them were referenced in WOS CC and 2851 of the articles of the test list were indexed in Cairn. Consequently, combining the two databases, the search comprehensiveness was 100% [see Additional file 3].
Bibliographic databases We first listed the peer-reviewed databases to which the members of our review team had access, databases that covered ecology and also humanities and that guaranteed reproducibility. The following publication databases have been listed and will be included in the search strategy: • WOSCC • Cairn. info (https:// www. cairn. info/) • Persée (https:// www. persee. fr/) • Archinform(https:// www. archi nform. net/) • Urbamet (http:// www. urbam et. com/) • ArchiRès (https:// www. archi res. archi. fr/) Due to the project's resource limitations (in terms of time, and financial resources), we were not able to search more than two web-based search engines. Because of the position of the subject at the science-society interface, we chose to explore at least one environmental database and one social science database.
The operation will be repeated until reaching a Kappa score ≥ 0. 6. Any disagreements will be identified and discussed between the reviewers. The criteria for rejection of an article will be decided, discussed, and validated by the review team. During the scoping stage conducted in the "Web of Science Core Collection", the three stages of the screening process were tested by one reviewer (MF) in order to refine the eligibility criteria. A second reviewer will examine the rejected articles during each sorting stage to assess the consistency of the inclusion/exclusion decisions. Thus, an a posteriori cross check will determine if articles are re-included or not. The details of the final decision of inclusion or exclusion of articles selected at the full-text screening stage (with the reason of exclusion) will be provided in an Excel database [see Additional file 5].
Data coding strategy Key data from the selected articles after the three screening stages will be extracted based on the PECO structure using an a priori specified Codebook, a predefined spreadsheet that was discussed and validated by the review team [the articles used to assess the effectiveness of the data coding strategy are listed in Additional file 6]. Each article will be coded based on the full text using keywords and expanded comments fields describing various aspects of the study [See the codebook in Additional file 5]. In case studies lack detail on the information required or if details need to be clarified during the review process, the corresponding authors will be contacted by email.
Study description • Basic bibliographic information (authors, title, journal, publication date, etc. ) • Study site, country of the study and region Each article selected at full-text stage will be double coded by two members of the review team to ensure the consistency and accuracy of data extraction. If this type of coding is not possible due to limited resources, four members of the review team will independently carry out an a posteriori crosscheck on the meta-data of 10 publications randomly chosen at the first stage of the screening process. To test agreement between reviewers, Kappa scores will be calculated. Any disagreement will be discussed and the eligibility criteria will be revised to improve the data coding strategy. This process will be carried out until reaching Kappa score ≥ 0.
Categories of exposure Description Example reference from test list or scoping exercise Morphological characteristics Description of street widths and its spatial organization, building heights  [32]  Connectivity Connectivity between urban items and vegetated areas (green spaces, urban gardens, remnants, etc. )  [33]  Urban development scenarios Use of urban or ecological modelization  [34]  Urban projects Comparing spaces before and after the construction of urban projects  [35]  Urban density Study of sectors characterized by a variable density of housing, buildings and population  [37]  Major types of urban areas Either large sectors such as "dense urban", "suburban", etc. , in terms of degree of compactness or land-use patterns or description of the urban form through function (e. g. residential, business areas, industrial)  [38]  Urbanization gradient Description of types of urban form even if the discriminating element is the percentage of urban land use by impervious surfaces  [39]  Study mapping and presentation We will produce an open-access database (Microsoft Excel sheet) including all the studies that meet our inclusion criteria and coded metadata.
It will be included as an appendix to the systematic map. Where there is more than one study included in an article, each study will be recorded as a specific entry in the database i. e. one line per study. There will be several lines for each article if necessary. The systematic map report will include summary figures and tables. Possible knowledge gaps (un-or underrepresented subtopics that warrant further primary research) and knowledge clusters (well-represented subtopics for full synthesis by a systematic review) will be identified e. g. by cross-tabulating key meta-data variables in heat maps (e. g. biological groups and outcomes). We will use our categorization of urban descriptors and outcomes to identify where there is a lack of knowledge and for which species. Interactive maps will be produced to present the data according to urban forms and make them more accessible and easily viewed.
The state-of-the-art: bibliometric analysis To track the current critical topics and trends in the literature relating to digital interoperability in LSCM, the methodology of bib-liometric analysis is adopted for statistical analysis of such trends, which is well established in the literature  (Leal et al. , 2019; Dolgui et al. , 2020). The methodology in this work consists of four steps: 1) define research questions; 2) data collection; 3) descriptive analysis; (4) categorization analysis and reporting results. The software VOSviewer is used for results visualization, which is commonly used in the literature (van Eck and Waltman, 2014).
, 2020 ). • The second task is to define the list of keywords and construct database queries. We first queried the combination of "interoperability" OR "digital connectivity" AND "physical internet" in article title and keywords. Only few papers were found. It is then necessary to extend the scope by replacing "physical internet" by "supply chain" or "logistics". Combining with the criterions in previous step, the query string in Scopus is as follows: (TITLE("interoperability"or "digital connectivity" and"supply chain"or"logistics") OR KEY("interoperability"or "digital connectivity" and"supply chain"or"logistics")) AND PUBYEAR > 2009 AND (LIMIT-TO (LANGUAGE,"English")) AND (LIMIT-TO (SRCTYPE,"p") OR LIMIT-TO (SRCTYPE,"j")). Step 3 -Paper selection A total of 261 papers were identified after step 2.
Descriptive analysis Using software to conduct bibliometric statistics and visualize results is a common approach in the literature (van Eck and Waltman, 2014). In this work, VOSviewer is selected to analyze the dataset of 208 papers identified above and visualize the results. The software uses the association strength to calculate similarity of two items, and their own techniques for clustering, mapping and viewing. More technical information can be found in  (van Eck and Waltman, 2014). Note that, in the software setting, full counting in the statistics and association normalization for result visualization are selected. Other settings are by default. In regard to Sub question (SQ2), special attention has been paid to several analyses, i. e. , authors, sources, documents per year, most frequent keyworks.
Co-authorship analysis The first analysis is to identify the most influential authors and groups in the dataset. 631 authors have been identified from the 208 documents. After applying a filter of minimum 2 documents and 1 citation, 62 authors are selected; and the co-authorship links are displayed in Fig. 4. Five important clusters can be observed, which are Grilo (green), Jardim-Gocalves (blue), Panetto (pink), Bicocchi (purple), and  Wang & Wong (orange). The clusters Jardim-Gocalves and Panetto are weakly connected. The top 10 productive and most-cited authors out of the 62 are listed in  influential researchers are, Grilo, Jardim-goncalves, Espadinhacruz, and Panetto. The information given by the figure and table could help readers (especially young scholars) identify the most influential researchers and their works in an efficient manner.