dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was partially funded by the  Polish Ministry of Science and Higher Education  ( N N519 401537 ),  National Center for Science  ( 6435/B/T02/2011/40 ),  European Union -Human Capital Programme  ( GRANT/II/37/2009 , MLODA KADRA),  Wroclaw University of Technology  (no. 332123) and  British Council Young Scientists Programme  ( WAR/342/108 ). A part of the computations was performed in the  Wroclaw Centre for Networking and Supercomputing at Wroclaw University of Technology. The authors would like to thank the anonymous Reviewers for their constructive comments which contributed to improving the final version of this paper.
31 Figure 3 313 Figure 3 Performance comparison with standard methods. Average AUC ROC of classifiers in 4-fold CV (horizontal axis) and when trained in the PDBTM150NR dataset and tested in WDG150NR dataset (vertical axis). Left: unweighted average, right: weighted average of AUC ROC. Sample method designations: gconcat -global HMM/concatated helices scanned together; l2parts best3 -local HMM/2 parts of a helix pair scanned independently; best3 -best classifiers out of 3trained. In the case of the PCFGs, accessibility grammars are used for c1 and c4, and van der Waals grammars are used for c2 and c3.
We implement them within a system based on the architecture proposed by Aaron et al. [3] , which is also at the basis of the popular distributed coding for video services (DISCOVER)  [10]  and VISNET I/II  [11]  DVC systems. In such a scheme (shown in Figure  1 ), the input images are split into key frames (KFs) and Wyner-Ziv frames  (WZFs). The KFs are encoded with a still image coding technique; at the decoder, they are used to produce an estimate of the current WZF, i. e. , the SI. This estimate is then corrected by requesting a suitable number of bits from a channel code. Therefore, the encoder need not to know the SI to produce these bits: the encoding of KFs and WZFs is actually distributed. The  Wyner  and quantized, and the channel code produces the parity bits for the coefficients' bit planes.
In this communication we introduce marmap, a package designed for downloading, plotting and manipulating bathymetric and topographic data in R. marmap can query the ETOPO1 bathymetry and topography database hosted by the NOAA, use simple latitude-longitude-depth data in ascii format, and take advantage of the advanced plotting tools available in R to build publication-quality bathymetric maps. Functions to query data (bathymetry, sampling information…) are available interactively by clicking on marmap maps. Bathymetric and topographic data can also be used to calculate projected surface areas within specified depth/altitude intervals, and constrain the calculation of realistic shortest path distances. Such information can be used in molecular ecology, for example, to evaluate genetic isolation by distance in a spatially-explicit framework.
Introduction Marine ecologists still lack simple, highly-customizable tools for building and using marine charts, open-source tools being particularly lacking. Currently, apart from commercial solutions, marine ecologists can use GIS tools such as GRASS GIS  [1] , or the graphical user interface provided by GeoMapApp  [2]  and Google Earth  [3]  to prepare publication-quality bathymetric charts, but these tools either require knowledge in the field of GIS, or are limited in the number of functions, datasets, and analyses available. The R environment  [4]  is a convenient platform for generating maps, thanks to its built-in functions for data matrix manipulation and advanced graphics. The package marmap  [5] , introduced herein, takes advantage of these built-in functions, as well as packages for spatial analysis developed by others, to provide a simple and flexible tool for importing, manipulating, plotting and exporting bathymetric data.
Importing and Plotting Bathymetric Data marmap offers different ways to upload bathymetric information, which should provide flexible data preparation, depending on need and access to online resources. First, bathymetric data can be downloaded directly from within R by querying the ETOPO1 database  [7]  hosted by the NOAA. Second, three-column data frames (with longitude, latitude, and depth data) can be imported from different sources, such as the US National Ocean and Atmospheric Administration (NOAA)  [8]. Third, marmap has functions to prepare and use a local SQL database, stored on the user's hard drive. Use of a personalized SQL bathymetry database is convenient for querying subsets of very large bathymetric datasets such as the 5Go ETOPO1. Finally, data can be imported like any other dataset into R, and converted using marmap functions.
Outputs of least-cost path analysis can be in the form of great-circle distance, taking into account the curvature of the Earth, and in the form of geographical paths that can be plotted on marmap maps, and used to compute two-and three-dimensional depth cross-sections. Distance data can be easily exported for direct use in landscape genetics software such as TESS  [12]. Data can also be used directly from within R to perform Mantel tests or other analyses  [13] [14] [15].
Interactions with other Packages marmap interacts with multiple existing R packages for visualization and analysis, such as lattice for building threedimensional plots (Figure  1C-D ), and gdistance for least-cost path calculations (see above). marmap also contains functions to ease interactions with other packages dedicated to the analysis of spatial data. Data from class bathy can be transformed into raster objets for use in the raster package  [10]  (functions trans. mat and as. raster). Bathy objects can also be converted into the class SpatialGridDataFrame for use in the sp package  [16, 17]  (function as. SpatialGridDataFrame). These transformed objects can be converted back into objects of class bathy using the as. bathy function. In the example below, the package raster is used to change the projection of a dataset extracted from ETOPO1.
is an open-source, open data molecular interaction database populated by data either curated from the literature or from direct data depositions. IntAct has developed a sophisticated web-based curation tool, capable of supporting both IMExand MIMIx-level curation. This tool is now utilized by multiple additional curation teams, all of whom annotate data directly into the IntAct database. Members of the IntAct team supply appropriate levels of training, perform quality control on entries and take responsibility for long-term data maintenance. Recently, the MINT and IntAct databases decided to merge their separate efforts to make optimal use of limited developer resources and maximize the curation output. All data manually curated by the MINT curators have been moved into the IntAct database at EMBL-EBI and are merged with the existing IntAct dataset.
INTRODUCTION Experimental approaches to determine molecular interactions often require that interactions are determined in non-physiological conditions which can result in significant false positive and false negative rates. Knowledge of experimental details, such as the experimental technology used, cellular context, protein modifications and expression systems are essential to evaluate the reliability of an interaction report, or to combine multiple reports into an overall reliability assessment. Experiments often determine additional valuable biological information in addition to the identity of the interaction partners, for example, interacting domains or sites of post-translational modifications. Curating interactions to the required level of detail to capture this information requires both a relatively complex data structure and frequent updates, including the mapping of binding regions, point mutations and post-translational modification to a specified sequence within a reference protein sequence database.
An update to a predictive gene model may result in a corresponding change to the protein sequence(s) derived from it. Interactions involving domains and/or residues of that protein sequence within an interaction database will then require a corresponding update to ensure the mapping to the updated sequence is correct. Update pipelines need to be run regularly, in line with the release cycle of the sequence database, namely every 4 weeks in the case of UniProtKB  (1). Similarly, controlled vocabularies (CVs) used to annotate interaction data need to be refreshed with every new release. The systematic capture of detailed information requires both a complex underlying data model and a curator-friendly, efficient data capture system using web services to provide on-demand data import from external resources and frequently updated CVs.
Such complex curation environments are much more expensive in terms of development and operational cost than the public facing, read-only website components of a typical molecular interaction database. The IntAct molecular interaction database has existed since 2002 to serve richly curated molecular interaction data provided in community accepted standard formats to a broad user community  (2). Data users range from cell biologists exploring the fine details of the mechanism by which a specific protein binds to protein partners to analysts utilizing the entire known interactome of a particular model organism to perform network analysis on the results of a large-scale 'Omics experiment. The IntAct database is based on a relational database management system (Oracle; Postgres version available), Hibernate object-relational mapping, Java-based middleware, Lucene/SOLR indexing and a web-based front end for public search, visualization and download of the data.
From its earliest implementations in 2002 onwards, the IntAct curation system has been developed as a webbased platform, to allow collaborative curation by physically remote partners, originally mainly a small group of UniProtKB/Swiss-Prot curators based in Geneva, Switzerland. While the IntAct data model is capable of describing interactions between any biomolecule type, the primary curation focus has always been on experimentally verified protein-protein interactions. IntAct curators capture this data from the literature, in a series of curation projects directed by grant funding and the requirements of collaborating experimental groups. In addition, the direct submission of experimental data as part of the publication process is actively encouraged by an increasing number of journals and is supported by the IntAct curation team.
Any submitted data are kept confidential until the accompanying publication is released but formatted data files can be provided to the submitter directly from the editorial tool to ease input into tools such as Cytopscape (3). IntAct is released at least monthly, and all existing publications are available from the IntAct ftp site in PSI-MI XML and MITAB 2. 5, 2. 6 and 2. 7 formats as well as via direct download from the website where data are additionally available in RDF and XGMML formats  (2, 4, 5). Data can also be queried using the PSICQUIC web service, and IntAct IMEx data are also accessible via the IMEx website  (6). All IntAct software, including the web-based curation tool, is free and open source, and can be used, modified and redistributed under the terms of the Apache Software License.
The MINT (Molecular INTeraction) database was created in 2002 at the University of Tor Vergata, Rome  (7) , independent of IntAct in funding and organization, but with very similar aims, curating experimentally derived protein interactions, with specific curation targets determined by the requirements of the large experimental activity of the group, for example, SH3 domainbased interactions  (8). A major activity was and is the close collaboration with the FEBS Letters Journal, where MINT curates all protein interactions published in the journal  (9, 10). MINT developed its own software infrastructure for curation as well as data dissemination, but already in 2006 decided to adapt the IntAct relational database implementation to reduce the overhead of database development and maintenance. The MINT web interface remained distinct and based on an IntActindependent code base.
Unlike protein structure or sequence databases, where single, collaborative database projects such as PDBe  (11)  and UniProt (1) provide the vast majority of public data, curation of molecular interaction data is mainly performed by a relatively large number of small to medium, independently funded projects  (12). Often the driver for a curation effort is not general resource provision, but local expertise in a specific scientific domain, which is exploited for the curation of reference datasets, often in direct support of experimental activities in the same group. As an example, MatrixDB (Universite de Lyon, France) (  13 ) is highly specialized in the curation of extracellular domain interactions. While domain-specific, expertcurated interaction datasets are likely to be of high quality and scientific relevance, this distributed approach also has significant risks.
Recognizing the risks of data fragmentation, redundant curation and redundant software development, many leading interaction databases, including IntAct, MINT and DIP  (14) , have since 2002  (15)  engaged in increasingly closer collaboration, moving from definition of common data representation [Human Proteome Organisation Proteomics Standards Initiative (HUPO PSI)-MI] (4) to co-ordinated curation strategies (IMEx)  (6)  and common computational query interfaces (PSICQUIC)  (5). It is an essential part of the IMEx Consortium agreement that, should a member database lose funding or cease activities due to transfer of group interests, the data be passed to another IMEx member database for long-term data stewardship. As of January 2012, the MPIDB database (  16 ) ceased active curation and since that date the IMEx records for that database are present within and updated by IntAct.
COMMUNITY ACCESS TO A WEB-BASED CURATION TOOL As previously described (  2 ), IntAct has invested heavily in the development of a sophisticated web-based editorial tool, enabling the systematic capture of the complexities of a molecular interaction experiment to either IMEx  (6)  or MIMIx-level  (17)  as determined by the curator. Part of the motivation for providing the entire IntAct code base as open source was to reduce redundant development of interaction database platforms. It was originally envisaged that individual groups would emulate the original MINT model and locally install versions of IntAct to curate and maintain their own interaction data. However, as described earlier, this still leaves such groups with a heavy data maintenance overhead to keep records synchronized with sequence database and CV updates.
Over the last few years, several groups have therefore preferred to curate directly into IntAct and make use of the existing IntAct data maintenance pipeline. This is not 'community annotation' per se, as these all represent highly trained M. Sc. and Ph. D. level curators, with IntAct staff providing the appropriate level of training and also quality control of all records produced to ensure curation standards and data quality remain consistent across the entire dataset. When requested, IntAct makes interaction records available in specific download formats for each contributing group, and indeed for other databases wishing to display interaction data. For example, a number of UniProtKB/Swiss-Prot curators at both the Swiss Institute of Bioinformatics and EBI have for several years annotated molecular interactions directly into IntAct.
IntAct then scores and filters all interaction evidences in the database and exports a high confidence subset, with a high degree of probability that the molecule pairs described physically interact with each other, back to UniProtKB (1), neXtProt  (18)  and the Gene Ontology annotation project  (19)  in a distinct format for each. Other databases such as I2D (Interologous Interaction Database)  (20) , which curates interaction protein interaction data relevant to the development of cancer and InnateDB  (21) , capturing both protein and gene interactions relevant to the process of innate immunity, choose to select appropriate records directly from the ftp site for subsequent import into their own resources. Both databases use the IntAct editor to perform IMEx-level curation  (6). The contract curation company, Molecular Connections (www.
molecularconnections. com/), perform pro bono public domain data curation via the IntAct database. AgBase, a curated resource of animal and plant gene products, captures data subsequently imported into their host-pathogen database (http://www. agbase. msstate. edu/hpi/main. html). The Cardiovascular Gene Ontology Annotation Initiative at University College London is building an interactome of cardiovascular associated proteins (http://www. ucl. ac. uk/cardiovasculargeneontology/). Several of the groups contributing data to IntAct are interested in molecule types other than proteins. MatrixDB  (13)  database focuses on interactions established by both extracellular proteins and polysaccharides, and uses the IntAct curation tool to input interaction data which is subsequently downloaded and transferred into their own database environment.
Similarly, a very new collaboration with the Norwegian University of Science and Technology, Trondheim is further extending the initial efforts of the IntAct curators into capturing transcription factor interactions with the genes to which they bind. To meet both these additional use cases, the IntAct editorial tool has been extended to facilitate access to both small molecule data from ChEBI  (22)  and information on the gene derived from Ensembl  (23). In each case, web services enable key information to be downloaded from an appropriate reference database and stored internally within IntAct, enabling curators to annotate interaction data relevant to those entities. In the near future, the increasing amount of RNA-based interaction data will also present new challenges to the molecular interaction curation community and the development of reference resources such as RNAcentral  (24)  will be critical to the capture of these important interactomes.
Each group is provided with its own PSICQUIC web service (5) running from within the IntAct database so that each data provider can choose to embed its own web service within a web page or tool, completely independently of other data present in the database. IntAct has also made several changes to enable additional credit to be given to external contributors. In addition to minor cosmetic changes to the website, such as displaying the logos of these groups and also the source of each data entry in the database, a new Institute Manager facility has been added to the editorial tool, to enable each set of curators to be linked and statistics generated on request, to enable each institute to support internal and grant-driven data requirements. The model of a shared curation environment and common data dissemination formats minimizes the development of redundant curation platforms and ensures compatibility of the curated data generated by all partners, while each partner can contribute their domain-specific curation expertise.
THE MINTACT PROJECT The use of IntAct as a shared curation platform has been boosted in Summer 2013, when IntAct and MINT joined forces to optimize curation and software development efforts. IntAct and MINT shared many commonalities and much of the basic maintenance and, as described earlier, update work to the underlying database infrastructure was largely redundant between the two groups. The two databases collaborated closely to work on the data formats and standards produced in collaboration with other members of the Molecular Interaction work group of the HUPO-PSI. This also included the development of curation standards and CV terms in addition to tools and services such as the PSICQUIC Web service. However, despite these shared efforts and their common infrastructure, both databases existed as physically separate entities and independently improved and updated their respective installations.
It was therefore agreed, early in 2013, to merge this common effort, with a view to making optimal use of limited developer resources and maximize the curation output. To achieve this, the entire set of experimentally derived data manually curated from published literature was transferred from MINT to IntAct at EMBL-EBI. Locally maintained CVs such as the cell line and tissue lists used to describe the host organism in which experiments are undertaken, were manually aligned and merged into a single reference list. Centrally maintained CVs, for example, the PSI-MI CV were refreshed (4). Underlying protein sequences, and features mapped to them, were updated to the latest UniProtKB release before the two datasets were merged. Although the IMEx Consortium rules now prevent redundant curation of the same publication by multiple member databases, there were many examples of papers curated prior to 2006 which were present in both databases.
In each case, the most richly annotated paper was selected as the dataset retained within the combined dataset. A new tagged data subset, Virus, was created to tag the manually curated papers from VirusMINT  (25) , and supplemented by additional papers containing virus-virus or virus-host interactions already present in the IntAct database. The final merged dataset (11 879 publications, 430 134 binary interactions) was released in August 2013, resulting in a huge increase in the data available. As detailed in Figure  2 , the number of publications in IntAct almost doubled from 6600 to 12 000. While the joining of the two datasets was a huge effort for all involved curators and developers, it ultimately took only a little over 1 month from the original transfer of the MINT dataset end of August to the first joint release in early September.
Taking into account the huge size of the MINT dataset, this is an impressive demonstration of the usefulness of common curation strategies and data representation between independent databases in the same or closely related domain. As from June 2013, the MINT curators curate new publications directly into the IntAct database, and the input into ongoing MINT projects has been transferred to the IntAct platform. This includes the creation of the Structured Digital Abstracts for FEBS letters which are now generated by IntAct, with the agreement of the editorial board of that journal. The MENTHA interactome browser  (26)  will continue to be built on the PSICQUIC web services of IMex databases  (6)  and BioGRID  (27) , as before. The merger of curation activities gives users access to an improved and continually updated single dataset, and the European tax payer gets an improved return on investment as improvement to the underlying database structure and website development efforts need only be funded once, at a single site with both groups contributing to long term data maintenance efforts.
FUTURE PLANS Over the last few years, IntAct has focused very much on data input, developing a sophisticated curation tool, curation life cycle and working with many other groups to extend the effective coverage of the literature achieved by the database. While this will continue, it is intended in the immediate future to focus on the presentation of the resulting data to those members of the IntAct user community who access the data via the website-improving search, filtering and graphical capabilities. New ways to display data need to be explored to cope with the evergrowing volume available. It is estimated that the volume of data held by the IntAct database alone may grow to 750 000 binary interaction evidences in the next 5 years, and while this can be presented using current simplistic network views, it is almost impossible to derive useful information directly from the resulting graphic.