dataesr/scientific-paragraphs-categorization huggingface dataset:
Other than the above-listed possibilities, it is also possible to click on an element and gather specific information on that element, based on the associated attribute tables. Furthermore, it is possible to visualize images which are assigned to the nearest building element as a source of restitution for a better understanding. As an example, in Figure  12 , the identify option is applied to a building element within the colonnaded portico in order to understand how that space was converted. This allows both the information on that building element and archival photograph belonging to the colonnaded portico to be discovered. Another data access possibility is offered through a web-based platform, due to a PHP-based web GUI, which allows a common and user-friendly interaction with the PostgreSQL tables.
Accordingly, it provides a web-based browser interface that gives the possibility to visualize, edit, and export tabular data from the database (in csv, pdf, and xls). The 3D model of each restitution state (Figure  13 ) can also be exported as an X3D format to be visualized in external viewers (for example, in Meshlab  [54] ). The X3D files, however, only contain geometries, that is, no semantics. Furthermore, a pull-down menu allows the user to reach tables of other significant data, (for example, images) which are linked to the corresponding state of the building chronology (Figure  14 ). In addition, on the main page, there is a tab titled "external data", in which sources of information, published and unpublished accounts, descriptions, and other archival documents are included. This also gives the possibility to individually view and edit each item as well.
Finally, it is possible to export data from PostgreSQL to Microsoft Excel by means of the pgJDBC (Java Database Connectivity) interface, in order to exploit its well-known data analysis capabilities and functionalities for creating reports and generating graphs or charts. However, it should be noted that this would just be a method of quantitative comparison of either building elements or spaces according to the different parameters. Nevertheless, a historic building requires complementary qualitative analysis for a full understanding and some data extraction possibilities are provided. The aforementioned are not aimed to cover every notable aspect (Figure  15 ).
Results The prototype of the implemented data repository provides a centralization of the geometric and historical data within a single database, thus, it allows for the complete digital archiving of the building's chronology. This is structured around digital 3D reconstructions of historical phases, allowing access through two different interfaces: the semantically enriched 3D model (for example, via ArcGIS) and a graphical user interface (via web browser), which allows access to information in tabular format. In the former, it is possible to do the inspection of the building's stratigraphy and to apply diverse queries, such as: (i) 3D visualization of different hypothesis historically, (ii) 3D querying of the reliability of these hypothesis, (iii) accessing corresponding written or visual historic sources, (iv) demonstration of historical changes in the construction technique and material culture, (v) learning the different uses/functions with associated sources, and (vi) setting diverse queries for a clearer understanding.
Consequently, the visualizations, in terms of the semantically enriched 3D model created in this research, are neither just a volume of spaces nor geometric features. Rather, it is a holistic information container of non-geometric data housed behind the imagery. In contrast to this method, the latter can be reached through a web-based GUI, where the following can be explored: (a) the list of restitution states representing building chronology, their content, and 3D geometric model of each phase, (b) the list of spaces making up each restitution state, (c) the list of building elements making up any space within a restitution state, and (d) the external datasets (for example, historic and legal documents and photographs) related to the building's different restitution states, as primary sources.
In addition, those data can be exported, if needed, to software with spreadsheet functionality (for example, Microsoft Excel) for the generation of reports, graphs, or charts. The proposed prototype described in this article provides systematic archiving and access possibilities for CH researchers interested in the chronology of a historic building. Through this method, it can support the decision-making process for future interventions pertaining to the qualities and characteristics of the CH place. Correspondingly, data exploration and retrieval, which is a crucial step for understanding analysis, is made easier in this systemized and holistic environment, and markedly faster due to the integrated data repository. Consequently, the system not only allows for data management purposes, but also allows for the reuse of content and knowledge.
This requires storage of a huge volume of heterogonous datasets and the capability to manipulate and manage them. As such, the developed prototype may be an effective tool for the research activities of CH specialists' by making historical information more visible and readable, which in turn both supports the comprehensive understanding and gives way to a novel (re)thinking. The creation of such a holistic information management approach has crucial importance, as it gives CH specialists the possibility of using systemized and structured information that provide an entire perception of the building's history. Last but not least, the methodology proposed in this research is a sum of the (re)interpretation of the values and changes specific to the tested archetype, reflecting the notion of Ottoman culture, their way of life, workmanship, construction tradition, and their continuity.
Furthermore, the methodology can be expanded to include any case-specific investigations for conservation, or any other case studies' applications, while the basics of the methodology will remain the same. The prototype was developed particularly for conservation-architects as the primary actors in architectural heritage conservation, allowing a comprehensive understanding thanks to the holistic repository. Indeed, interpretation by the general public is also important, as public awareness is noted to be of importance in the charters of ICOMOS  [28, 55]. A future research direction raised here is, thus, to allow new levels of access, especially for the public, in order to develop conservation consciousness and employ e-learning facilities. Besides, we acknowledge the necessity of a tighter integration of the visualization and querying capabilities without relying on external commercial software, like ArcGIS or Microsoft Excel.
Acknowledgments: This paper is an outgrowth of  Gamze Saygi 's PhD thesis, which dealt with a number of aspects of information management of CH assets, and has been possible thanks to research fellowship allowing collaboration with  Giorgio Agugiaro  (first at  3DOM  ( 3D   Optical Metrology) Research Unit of FBK , and later at  Austrian Institute of Technology (AIT) ). The authors are thankful to FBK and  AIT  for providing the IT resources to temporarily host the database and the webserver;  Fabio Remondino  ( FBK ) for his constructive criticism, suggestions and support;  Ömür Saygın  ( IYTE ) for the useful discussions, and  Architect Temel Aktürk  on behalf of  ARK  ˙Inşaat for providing 2D documentation and complementary datasets of the case study.
We tried the following methods for GWAS: Bayes factors with the mixture model BayesCPi:  (Habier et al. 2011)  fixing a priori that only 0. 1% of the markers have an effect (see  (Legarra et al. 2015)  for a full description). This method provides BFs, although our implementation only considers single markers. Single marker and multiple marker EMMAX tests: as presented in this work, computed via MCMC, up to segments of 100 consecutive markers. Bayes factors from SNP-BLUP: as presented in this work, computed via MCMC, up to segments of 100 consecutive markers. EMMAX was fitted using blupf90  (Misztal et al. 2002)  and homemade scripts, whereas the other used our software GS3 (available at https://github. com/alegarra/gs3), using "OPTION Bayes Factor". After completion of the analysis, we produced Manhattan plots based on BF and the other statistics; for EMMAX we used Bonferroni corrections to claim genowide significance; for Bayesian procedures, we did not address thresholds for declaring detection; this point will be addressed in the discussion.
Matrix C aa contains a posteriori covariances of marker effects, which reflect allelic frequencies (i. e. , a rare SNP is more difficult to estimate) and linkage disequilibrium across markers (two markers in strong LD will have correlated estimates a posteriori, and any of them will be less accurate than a marker not in LD with any other). If estimates are obtained by MCMC, C aa can be estimated as the covariance matrix of the samples of the posterior distribution, pðajyÞ. The R package RRBLUP  (Endelman 2011 ) produces sdðâ i Þ, and our software GS3  (Legarra et al. 2011 ) can produce parts of C aa. GBLUP: the equivalence between GBLUP and SNP-BLUP implies that marker solutions (â) can be backsolved for individual solutions  (ûÞ (VanRaden 2008; Strandén and Garrick 2009) VarðâÞ ¼ 1 2 P p i q i Z 0 G 21 À Gs 2 u 2 C uu Á G 21 Z 1 2 P p i q i where C uu is the element of the inverse of the left hand side matrix corresponding to u; this inverse can be computed by inversion or, again, by MCMC.
ACKNOWLEDGMENTS  INRA SelGen metaprogram is acknowledged for financing in its project EpiSel. Project partly supported by the  Toulouse Midi-Pyrenees Bioinformatics platform. Luis Varona  acknowledges the  CGL2016-80155  project of  Ministerio de Economía y Competitividad of Spain. Editor and reviewers of preliminary and current submissions made very useful comments and pointed out the Bernal Rubio et al. study.
OrthoInspector is one of the leading software suites for orthology relations inference. In this paper, we describe a major redesign of the OrthoInspector online resource along with a significant increase in the number of species: 4753 organisms are now covered across the three domains of life, making OrthoInspector the most exhaustive orthology resource to date in terms of covered species (excluding viruses). The new website integrates original data exploration and visualization tools in an ergonomic interface. Distributions of protein orthologs are represented by heatmaps summarizing their evolutionary histories, and proteins with similar profiles can be directly accessed. Two novel tools have been implemented for comparative genomics: a phylogenetic profile search that can be used to find proteins with a specific presence-absence profile and investigate their functions and, inversely, a GO profiling tool aimed at deciphering evolutionary histories of molecular functions, processes or cell components.
More than 30 resources have been developed to address the challenges of orthologous relation inference and community efforts have been directed towards standardization and benchmarking of these resources, in the form of the Quest for Orthologs consortium  (9). OrthoInspector  (10, 11)  was shown to be one of the three most balanced methods of orthology inference in terms of precision and recall in a standardized benchmarking test  (12)  and performed well in other comparative studies  (13). The previous release of OrthoInspector  (11)  provided two precomputed databases (Prokaryotes and Eukaryotes) that could be queried from its website, however since the last release the D412 Nucleic Acids Research, 2019, Vol. 47, Database issue number of available annotated genomes has significantly increased and standards for web interfaces have evolved.
Here, we present the third release of OrthoInspector that includes a number of important developments. First, we report a major increase in the number of species represented in the OrthoInspector precomputed databases across the three domains of cellular life, including both in-domain and cross-domain relations, making the OrthoInspector databases the most exhaustive orthology resource to date in terms of covered species. Second, to manage the massive increase of data, the OrthoInspector website has been entirely redesigned to provide a streamlined and intuitive experience for users, including a summary visualization of ortholog distributions and novel tools allowing powerful comparative genomics analyses.
Improved coverage of the tree of life Proteome selection. When designing the OrthoInspector databases, we focused on providing a broad coverage of the tree of life, with a selection of organisms that are representative of the taxonomic diversity. In order to meet this goal, we used the Uniprot Reference Proteomes  (14) , which result from an effort to efficiently sample the tree of life and limit redundancy. Incomplete genomes, mispredicted or fragmentary protein sequences constitute an important source of errors in orthology inference. Therefore, we used a combination of filters (see supplementary materials and methods) to exclude proteomes with abnormally small proteome size, a high proportion of small proteins (<100 amino acids) or of proteins that do not start with a methionine. Specifically, we excluded proteomes of Archaea and Bacteria with >20% of small proteins and/or 10% of false-start proteins and/or >10% proteins annotated as fragments.
For Eukaryotes, we kept the same threshold for small proteins and excluded proteomes with >55% of false start proteins. Starting from the 5443 Reference Proteomes, the quality filtering step resulted in the exclusion of 690 proteomes (13%). The percentages of excluded proteomes were similar across domains: 119 out of 830 eukaryotes (14%), 537 out of the 4400 Bacteria (12%) and 34 out of 213 Archaea (16%). In one case, we privileged the coverage of the tree of life over quality measures and kept the proteome of Lokiarchaeum sp. GC14 75 owing to the general interest for representatives of the Asgard group in comparative genomics  (15, 16). Database architecture. The OrthoInspector 3. 0 databases cover 4753 organisms (+144% compared with the previous release): 3863 bacteria (+146%), 711 (+174%) eukaryotes and 179 archaea (+49%) (Figure  1 ).
This is, to our knowledge, the widest coverage available for an orthology inference resource in terms of species (excluding viruses). The database architecture is designed to cover the essential use cases for orthology data. It relies on three main databases, one for each domain of life. Each database provides all the orthologous relations between proteins of each species within the domain. This exhaustive coverage of each domain is suitable for fine grained studies, as it provides a good resolution at low taxonomic levels. We designed a fourth database to provide orthologous relationships across a wider evolutionary spectrum and specifically, to cover the three domains simultaneously. To facilitate handling and interpretation of these cross-domain comparisons, we defined a subset of significant species that we will refer to as 'model species' (see Supplementary Table  S1 ).
We selected these species according to their importance in the biological field (e. g. model species such as Mus musculus or Caenorhabditis elegans) and/or to ensure a good taxonomic sampling (Figure  1 ). This selection corresponds to 317 species: 144 eukaryotes, 142 bacteria and 31 archaea. OrthoInspector can thus be used to find intra-domain orthologs in a large number of species and to find interdomain orthologs in fewer, well-studied, species. Users interested in orthology relationships between non-model species from different domains can find them by transitivity, by first finding orthologs in close 'model species'. This original implementation involving the co-existence of databases with different levels of granularity implies that orthologs can be found in all our available species without the huge computational burden a 'full' inference would require.
A new information design To cope with the massive increase in the number of species available in the OrthoInspector databases and the corresponding increase in the number of orthology relationships, we implemented a new website interface providing a smooth navigation in the new datasets. Access to protein entries. The OrthoInspector website offers two main ways to access the data: by protein identifier and by sequence similarity searches. The protein identifier search is accessible from the main page, or anywhere on the site using the navigation bar. The user should define the appropriate database by selecting the domain of life of the query protein. Typing in the search bar triggers autocompletion and dynamically proposes a list of clickable protein entries available in the selected OrthoInspector database.
The identifier search currently supports both Uniprot identifiers and Uniprot access numbers. A sequence similarity search is also available from the OrthoInspector webpage or by selecting 'BLAST search' on the database tab. This launches a BLASTp  (17, 18)  search against all protein sequences in the OrthoInspector databases. The result is a formatted BLAST output of the 50 best hits along with their corresponding local alignments and links to the corresponding protein pages in OrthoInspector. Protein page. The data in OrthoInspector can be explored from protein pages. The protein page header gives a quick summary of the protein (gene name, description, organism). All Gene Ontology  (19)  terms associated with this protein are displayed in an extendable panel when available, as well as the protein sequence and a schematic view of InterPro  (20)  domains found in the protein.
Each tile of the heatmap corresponds to a major clade (Figure  1 ) of the selected domain, defined either from the NCBI taxonomy  (22)  or in some cases from the consensus in the literature (for example, 'Excavata ' appears in the cross-domains banner and is widely accepted by the community despite not existing as such in the NCBI taxonomy). For each clade, the corresponding tile is colored in green if orthologs are found in all its representatives and red if no orthologs are found, with intermediary states between these two colors if orthologs are found in a subset of representatives. The number of species in which orthologs are found and the total number of species belonging to the clade represented in the OrthoInspector database are both displayed when hovering over the tiles. The clades on the heatmap are ordered according to the taxonomy: clades close to each other are side by side on the heatmap.
Searching for proteins with similar evolutionary histories. The OrthoInspector protein page can be used to find other proteins of the same species with similar phylogenetic profiles. This information is available under the 'Proteins with similar distribution' section on the Protein page. The data available in these sections are based on the Jaccard distance between all phylogenetic profiles of proteins in the same species (see supplementary materials and methods). The identifiers of proteins exhibiting a phylogenetic profile distance <0. 4 are shown, along with a short description of their functions and the exact value of the distance. For clarity, only the five closest proteins are shown; additional proteins can be visualized by clicking 'See more'. Distances are available both from a domain centric point of view (calculated on profiles limited to species of the same domain) or from a cross-domain point of view.
47, Database issue and absent in others. This tool is available from the home page and under the 'Access/Search by profile' tab. Users should select their query species on the dropdown menu and then interact with a dynamic representation of the NCBI taxonomic tree to define the profile. Clicking once on a clade imposes the presence of orthologs in at least one species of the clade, double clicking imposes the absence in all species, a third click removes the constraints. Once the constraints are set, the database is queried to find all proteins meeting the user's requirements (Figure  3 ). The resulting proteins are displayed as panels in the result windows with their distribution summary (see above) to facilitate identification of distribution subcategories within the results. Each protein panel also contains a short description of the protein along with the associated Gene Ontology terms.
For a functional analysis of the complete protein list, a button can be clicked to run a GO term enrichment analysis using the Panther webservice  (24). The full list of proteins obtained can be exported using the 'Download list' button, for further analysis. Using this tool, we performed a phylogenetic profile search on the cross-domain database. Our objective was to identify Eukaryotic Signature Proteins (ESP) that were also present in Asgard Archaea, a clade whose discovery sparked interest due to its unexpected similarity to Eukaryotes  (15, 16). We searched for orthologs of Homo sapiens proteins present in Archaea of the Asgard group but absent in other Archaea and in Bacteria (Figure  3A ). This operation resulted in a total of 69 proteins with the required distributions (Figure  3B ).
Data and software accessibility This database update is complemented by the release of the new version 3. 0 of the OrthoInspector software suite, developed in Java, and available for download on the website in the download section (http://www. lbgi. fr/orthoinspectorv3/ download Package). This release does not involve changes to the main algorithm  (10)  but provides several software improvements. Software improvement. Several code modifications were performed to optimize the management of the massive quantities of data. This implies type changes to handle larger datasets, code optimization by reducing loop redundancy, the use of more optimized data structures (library Fastutil, arXiv:1601. 06919) and more efficient database access from the software (fewer SQL queries). This version of OrthoInspector runs faster than the precedent for large computations and can still be parallelized when installing a large database.
Improved accessibility. Following feedback from users, the new OrthoInspector version provides an easier accessibility for small datasets. Until now, fully supported database systems included MySQL and PostgreSQL, which require prior experience of SQL management systems. This version comes with full support for SQLite database, which eliminates most of the preliminary steps for computing a local database since no database server configuration is required. We recommend the use of the easily accessible SQLite database option when installing small local databases and, for performance reasons, the use of Post-greSQL and MySQL systems for larger databases (several hundred of species). Updated tutorials for the installation procedure are available on the website. Precomputed databases. All four precomputed databases (Eukaryotes, Bacteria, Archaea, Cross-Domain) can be accessed via the website interface.
Due to the data volume (up to multiple terabytes in a single database), the database dump is not available for direct download but could be made available on demand. Quest for Ortholog consortium reference proteome. The Quest for Ortholog (QFO) consortium is part of an ongoing effort from the community pushing for standardization in orthology inferences. The QFO consortium published a list of 78 reference proteomes representing high quality proteomes and recommend using it for benchmarking purposes. The precomputed orthology relationship made using this benchmark are available on http://www. lbgi. fr/ orthoinspectorv3/QFO. Webservices. In addition to the web interface, a programming access is a major requirement for modern databases, as it allows more flexible use of data. In this release, we introduce a Representational State Transfer (REST) API providing access to most data available from the website, through the Swagger framework (https://swagger.