dataesr/scientific-paragraphs-categorization huggingface dataset:
There is a wide range of metadata schemata and application profiles used for the description of content and resources. This variety is, to a great extent, due to the diverse needs and requirements of the communities for which they are developed. Thus, schemata for publications originally came from publishers, librarians and archivists. Currently, we also witness the cross-domain activities of the various scientific communities, as the objects of their interest expand to those of the other communities, e. g. in order to link publications of different domains, publications and the supplementary material described in them, or services which can be used for processing publications and/or other datasets. Differences between the schemata are attested at various levels such as: • types of information (e.
The most recent 3 , defines a set of XML elements and attributes for tagging journal articles both with external (bibliographic) and internal (tagging the actual textual content of an article) metadata. In the life sciences area, DC and JATS are supported by PubMed Central 4 (PMC) as formats for metadata retrieval and harvesting, as well as for authoring, publishing and archiving. DataCite  (14)  represents an initiative for a metadata schema, along the same lines as JATS, aspiring to cover all types of research data, while it is more tuned to metadatabased description of publications. It places a strong emphasis on citation of research data in general, not only including publications, and for this reason it strongly supports the use of persistent identifiers in the form of digital object identifiers (DOIs, see 'Mechanisms used for the identification of resources' section).
Similarly, CrossRef (15) is a registry for scholarly publications, stretching out to research datasets, documented with basic bibliographic information and heavily also relying on DOIs for citation and attribution purposes. BibJSON is a convention for representing bibliographic metadata in JSON facilitating the sharing and use of such metadata. The Comprehensive Knowledge Archive Network (CKAN)  (16)  is essentially an open data management software solution, very popular among the public sector open data communities. Intending to be an inclusive solution, CKAN features a generic, albeit limited, set of metadata elements covering many types of datasets. Similarly, Common European Research Information Format (CERIF)  (17)  proposes a data model catering for the description of research information and all entities and relationships among them (researchers, publications, datasets, etc.
The Text Encoding Initiative (TEI) (18) represents a 'standard for the representation of texts in digital form', currently the most widely used format in the area of the humanities. To some extent similarly to JATS, the TEI P5 guidelines 8 include recommendations both for the bibliographic-style description of texts as well as for the representation of the internal structure of the texts themselves (form and content) and their annotations. The Common Language Resources and Technology Infrastructure (CLARIN) Research Infrastructure  (19)  has proposed CMDI, a flexible mechanism for creating, storing and using various metadata schemata, in an attempt to accommodate the diverse needs of language technology and text mining research and to promote interoperability. Along the same lines, the META-SHARE metadata schema  (11)  is used in the META-SHARE infrastructure  (20)  to describe all kinds of language resources including datasets (e.
g. corpora, ontologies, computational lexica, grammars, language models, etc. ) and language processing tools/services (e. g. parsers, annotators, term extractors, etc. ). A subset of these metadata components is common to all resource types (containing administrative information, e. g. contact points, identification details, versioning, etc. ), while metadata referring to technical information (e. g. text format, size and language(s) for corpora, requirements for the input and output of tools/services, etc. ) are specific to each resource type. Finally, the LRE Map (21) features a minimal, yet practical for its purposes, metadata schema that is used for crowdsourcing metadata information for language resources, including datasets and software tools and services, directly by authors who submit their publications to the LREC Conferences 9.
To facilitate interoperability between metadata schemata and the repositories that use them, including those described above, the World Wide Web Consortium (W3C) has published the Data Catalog (DCAT) Vocabulary 10. DCAT is an RDF vocabulary catering for the description of catalogues, catalogue records, their datasets as well as their forms of distribution, e. g. as downloadable file, as web service that provides the data, etc. DCAT is now extensively used for government data catalogues and is also growing in popularity in the wider Linked Data community. Vocabularies and ontologies for describing specific information types Metadata schemata are not enough for comprehensive description of resources, as we also need to know what particular fields mean. For example, different metadata schemata for scientific articles may include a field called 'subject', or 'domain' but this raises questions: Are 'subject' and 'domain' intended to codify the same information? Are the values for these fields provided freely by the authors, or do they have to be selected from a controlled vocabulary or an ontology? Such questions are usually addressed when designing application profiles where inter alia vocabularies and/or ontologies associated with particular fields are specified.
Mechanisms used for the identification of resources Identification systems present the researcher with a means of assigning a persistent identifier to a resource (usually under their ownership). In contrast to simple identifiers, a persistent identifier is actionable on the Web and can be distributed to other researchers who can also use the same identifier to refer to the original resource. This facilitates deduplication, versioning and helps to indicate the relation between resources (e. g. raw and annotated text, scholarly articles before and after review, corrected or enriched versions of a lexicon, etc. ). Although persistent identifiers have so far been assigned primarily to publications, they are recently also applied elsewhere: e. g. datasets, software libraries or even the individual researcher.
All three cases of aggregators rely on the widely used OAI-PMH protocol for harvesting publication data. Specifically focusing on the area of Life Sciences are the repositories of MEDLINE, PubMed and PubMed Central. MEDLINE  (34)  is the U. S. National Library of Medicine (NLM) bibliographic database, containing >22 million references to journal articles in life sciences with a focus on biomedicine. Records in MEDLINE are indexed with MeSH. PubMed includes >26 million citations for biomedical literature from MEDLINE, life science journals and online books. PubMed citations and abstracts include the fields of biomedicine and health, covering portions of the life sciences, behavioural sciences, chemical sciences and bioengineering. In some cases, citations may include links to full-text articles from PubMed Central and other publisher web sites where the articles were originally published.
Almost all the resources mentioned in Table  7  are manually curated, which means that they are the result of a process involving humans reading relevant publications or other knowledge sources and extracting necessary information. The only exception is Uniprot, which includes a section (UniProtKB/TrEMBL) of automatically annotated and classified entries. Automatic and semi-automatic solutions permit a biocurator to increase the coverage with reduced human effort, but also result in lower annotation quality because of limited accuracy of automatic methods. In the general domain (as shown below), most of the knowledge resources that we have covered are automatically extracted from textual databases, particularly from Wikipedia.
g. license, popularity). This information is generally materialized on a repository's home pages as drop-down menus and further accessible through facets during the search process. META-SHARE proposes no < 19 criteria to filter out resources. In repositories and directories collecting data from various sources, a key challenge is the mapping of original metadata fields into a common meaningful schema. The growing uptake of standard metadata schemata by both resource producers and stores combined with the achievements of international initiatives and infrastructure projects like META-SHARE or CLARIN make the mapping work easier. However, value lists associated to some metadata fields are still stumbling blocks. While lists for languages or countries are widely shared, building consensus on subjects, resource types, media types, or formats is still work in progress, through initiatives like COAR.
In some cases, identifying which part of the software relates to which of the above categories is difficult. For example, in Stanford CoreNLP, which does offer a type system and a workflow engine, the separation is not as clearly reflected in the system architecture and advertized separately as in UIMA or GATE. In the course of the present section, we will present a variety of WMSes that can be used to help the researcher in TM. In the 'Text mining workflow management systems' section, we show WMSes which are designed specifically for the purpose of TM. 'General purpose workflow engines' section presents a further list of WMSes which are designed for general purpose research. It is possible to use these for TM and this could be appropriate for a researcher who has previous experience in one of these platforms.
Component collections Component collections represent a piece of software that sits in between a processing framework and an analytics tool. The software acts as an adapter that allows combining analytics tools coming from different software packages and created by different providers into workflows. Often, component collections wrap third-party tools that are also separately available as software packages, but occasionally analytics are provided only in the form of a component for a specific framework. Component collections typically focus on a particular area of language analysis and are centered around an annotation scheme which models this area in particular. Some collections are focused on a very specific use-case, e. g. cTAKES  (76)  on the analysis of clinical text, Bluima (77) on the extraction of neuroscientific content and ClearTK (78) on adding machine learning functionality to UIMA.
Others host different tools for the general domain of life sciences, e. g. JcoRe  (79) , the NaCTeM (National Centre for Text Mining) collection (  9 ), BioNLP UIMA (80) and the Semantic Software Lab collection. The third category, including collections like DKPro Core or ClearTK, provide a broad range of rather low-level analytics tools that act as a toolkit for the implementation of many different kinds of use-cases. Giving a clear indication of the size of a component collection is difficult. For example, if one component can be parametrized with three different models for three different languages, should it be counted three times or only once? Some component collections offer separate wrappers for individual functions of a tool, e. g. for a tool that combines part-of-speech tagging and lemmatizing.
Workbenches Using analytics software or components programmatically in the sense of a software library requires programming skills. This is a major problem for the larger adoption of NLP/TDM technologies in less programming-oriented domains. Workbenches aim to facilitate the use of analytics components by providing a graphical user interface that allows a user to browse components, assemble them into workflows, execute these workflows, and inspect the results. The workbenches listed here were created with a particular focus on language analytics and build on one or more of the processing frameworks presented before. The more recent LAPPS Grid workbench is based on the generic Galaxy WMS and integrates components across multiple processing frameworks. If two components from the same processing framework are adjacent to each other, they communicate in their native formats, while a small piece of interfacing code called a 'shim' is inserted when two adjacent components come from different frameworks.
GATE components can be installed into the GATE Developer application from external websites hosting GATE component repositories. UIMA Ruta can be used in conjunction with components obtained from Maven repositories. The way the projects are driven also differs greatly. WebLicht (  81 ) is a part of the CLARIN-D effort, a largescale infrastructure in Germany and part of the multinational EU CLARIN effort aiming for a European infrastructure for language resources and technology in the social sciences and humanities. The LAPPS Grid project has a similar goal in the US but is comparably much smaller. The U-Compare system was superseded by Argo (9) at NaCTeM. The vision of Argo is to create an easy to use but highly functional WMS for the life-sciences community and beyond to engage in a variety of tasks around text mining, including biocuration  (82).
It provides a powerful mechanism to obtain and process multiple documents in a user-friendly environment. A variety of export options are available to obtain the final results of processing, including type systems tailored to a particular application  (83)  and web services supporting interoperable formats  (84). Argo is accessible and usable via the web, where a large collection of ready-to-use components can be combined by a novice user to build a workflow. NaCTeM have also used Argo as a tool in collaborations, installing separate instances at partner institutions to enable others to benefit from the software. The system has been applied in discovering phenotypes in clinical records  (85) , implementing state-of-the-art chemical recognition algorithms  (86)  and semi-automatic curation of disease phenotypes  (87).
The GATE framework  (42)  is mainly developed at the University of Sheffield with partners such as Ontotext. However, it is developed as an open source project hosted on Sourceforge with a public code repository. They also accept code contributions from the community at large. Additionally, there are community-provided repositories of GATE components, such as the Semantic Software Lab at Concordia University in Montreal, Canada. UIMA Ruta  (88)  is part of the Apache UIMA project hosted at the Apache Software Foundation. Like all Apache projects, it is an independent volunteer-driven community project providing its software under the liberal conditions of the Apache Software License which suits research and education as well as commercial use. Contributions from the community are welcome.
In contrast, ELKI  (93) , KNIME  (94)  and Triana  (95)  were originally created to perform data mining tasks; hence, their power resides in implementing and executing statistical algorithms. Other workflow engines were created for specific domain experiments and later also applied to other domains as well. The next important classifying feature is the creation of new components for a workflow engine. All of the mentioned tools except Pipeline Pilot  (96)  and SADI (97) are implemented using Java or Python. This makes them platform independent, and also facilitates the implementation of new components. SADI is not a typical workflow engine, but rather a set of design patterns that help to achieve interoperability and let users combine different tools into a pipeline. It is using the well-known standards of semantic web: each component is a RESTful service communicating using OWL, RDF and SPARQL.
Interoperability is a topic which has already been discussed and studied at length over multiple large research projects. The CLARIN Project  (19)  produced a review of accepted standards which were designed to promote interoperability in multiple fields. The META-NET project (112) produced a language resources sharing and discovery infrastructure, META-SHARE (20), along with associated metadata standards  (11). The FLaReNeT project, aiming to create a European strategy for the area of language processing and resources, prepared an assessment of the current standards' landscape  (113). Other ongoing efforts include the Research Data Alliance 18  initiative for promoting openness in research data sharing, which has several working groups which are interested in interoperability and FutureTDM  19  , which tries to assess and reduce legal and policy barriers for the growth of the text mining field.
Finally, both the annotated corpus and the term inventory encoded in OBO format (see 'Formats for knowledge resources' section) were published in the META-SHARE repository (see 'Language resources repositories' section). Employing open standards, formats and services for publishing annotated content, created vocabularies and workflows, makes it more likely that such a study will be useful for related research projects. The on-going OpenMinTeD project is at the forefront of promoting text and data mining amongst the communities that need it most. The project is currently working on several fronts to further the cause of text and data mining. First a platform will be produced, which will allow a novice TM user to come and experiment with some standard tools and their own data. Second, a set of flagship applications will be developed using the platform to demonstrate the power of TM and promote TM within the communities that the applications are developed for.
Molecular biology We used pDG1730 (Genbank U46199,  (37) ) that we obtained from the Bacillus Genetic Stock Center (http://www. bgsc. org) as our backbone plasmid for B. subtilis integration into the amyE locus. All plasmids used in this study were derived from this vector and fragments assembled using onestep isothermal assembly  (38)  or restriction enzymes following standard molecular biology procedures. Restriction enzymes were purchased from New England BioLabs (NEB, Ipswich, MA, USA). PCR were performed using Q5 PCR master mix (NEB), primers and Gblocks were purchased from IDT (Louvain, Belgium; Carlsbad, USA). Plasmid extraction and DNA purification were performed using kits from Biosentec (Toulouse, France). Sequencing was realized by GATC Biotech (Cologne, Germany). All primers sequences and details on molecular biology protocols are available in supplementary materials.
Voltages used were FFS: 440, SSC: 340, BL1: 490, YL2: 620. Data were analysed using the Attune NxT software. Flow-Jo (Tristar) was used for data representation. Statistical values for each channels of the sample were calculated and exported. For each independent experiment, the median fluorescence intensity of the bacterial population for each replicate was extracted. Then, the mean fluorescence intensity was calculated from the three replicates. The mean values and standard deviation from three independent experiments were then calculated. Relative expression units were calculated for each independent experiment by dividing the mean fluorescence intensities values measured from the synthetic constructs by the mean fluorescence intensity measured from the reference construct. All raw data files are available in supplementary materials.
485/20 nm, em. 528/20 nm) and absorbance (at 600 nm) every 10 min. Absorbance at 900 nm and 977 nm (Abs 900 , Abs 977 ) were read once at the beginning of each experiment in order to correct the OD 600 to an optical path length of 1 cm using the following equation: (Abs 977 -Abs 900 )/0. 18  (40). Fluorescein was present on the microtiterplate at two different concentrations (1 and 10 nM) in duplicates. Each culture was performed in triplicates. Polynomial and exponential functions were used to fit the experimental datasets of GFP and biomass, respectively  (26) , and to deduce the rates of biomass and GFP productions along the growth. GFP concentration was estimated as GFP per OD 600 , ( G F P OD ), at each time point. In steadystate growth (μ = constant), G F P OD is constant. GFP concentration (also referred to as activity) was expressed in Relative Expression Units (REU) using our reference construct.
Individual cells in each FOV were contoured automatically with manual correction using the Patrack software  (43). For each FOV, fluorescence fluctuations (δF) from the average intensity over 50 scans (<F>) were first calculated at each pixel, providing pixel-based maps of the true (shot noise corrected) molecular brightness of the diffusing fluorescent particles, ⑀: ε (x, y) = δ F 2 (x, y) -F (x, y) F (x, y) For each FOV, the average molecular brightness ⑀ FOV was determined using only the M pixels encompassed within all the cells of the FOV, and the number of fluorescent particles detected in the excitation volume within each cell was calculated: n cell = 1 M M j = 1 F 2 ( j ) δ F 2 ( j ) ε FOV + 1 ε FOV The molecular brightness of GFP ( GFP ) was estimated for each experimental day by averaging ⑀ FOV measured for strains expressing moderate amount of GFP (i.
Choice and characterisation of ten B. subtilis constitutive promoters We aimed at designing synthetic libraries of constitutive promoters spanning a wide dynamic range of transcriptional efficiencies in B. subtilis. Such constitutive promoter libraries are essential tools for precise engineering of genetic circuits. For example, in metabolic engineering, the expression level of the different enzymes of the pathway has to be precisely tuned  (50). In order to identify a first set of natural constitutive promoters from the B. subtilis genome, we used data recently produced by the BaSysBio consortium  (27). This consortium mapped the transcriptional architecture, metabolic and networks behaviour of B. subtilis at a large scale and over 100 different conditions. We searched the mRNA expression database (http:// genome.
ACKNOWLEDGEMENTS We thank  Emmanuel Margeat ,  Luca Ciandrini ,  Joachim Rambeau ,  Guillaume Cambray , and members of the  Bonnet and Jules lab  for fruitful discussions. We acknowledge  France-BioImaging  infrastructure supported by the  French National Research Agency  ( ANR-10-INSB-04-01 , Investments for the future ) and the  GIS IBiSA : Infrastructures en Biologie Sante et Agronomie. All sequences and raw data are available in supplementary materials and supplementary data files. All libraries have been deposited and are available at the  Bacillus Genetic Stock Center  (http://www. bgsc. org/).
Methods All experiments were performed at room temperature (RT). Experimental buffer was Hepes 10 mM, pH = 7. 4, KCl 150 mM, EDTA 2 mM except for QCM-D binding experiments. All images were acquired with a Zeiss LSM 780 microscope (Carl Zeiss, Inc. ) using a 63x NA 1. 4 oil objective and quantified using Image J software (NIH, MD, USA). Detailed materials, methods for model membranes (LUV, GUV, SLB) preparation and for protein purification are in SI. Binding experiments. K p were determined on basic lipid composition (EPC 68%, BPS 30% & PI(4,5)P 2 2% for LUVs or POPC 68%, POPS 30% & PI(4,5)P 2 2% for SLBs). Methods used were either cosedimentation assays for LUVs or QCM-D experiments on SLBs. Co-sedimentation assays were made at 1 μM protein concentration with varying concentrations of total accessible lipids from 0.