dataesr/scientific-paragraphs-categorization huggingface dataset:
More technical details can be found in  [8]. The VAMDC Portal uses the token to get the persistent identifier of the request from the Query Store. Once it has received it, it displays the "Citation link" in the result page of Figure  4 , this link goes to the landing page at https://cite. vamdc. eu/persistentId, as represented in Figure  6. This landing page (Figure  6 ) is the typical human readable Query-Store landing page that a user reaches when he resolves the persistent identifier associated with a given query. The landing page stores the persistent identifier (named "Query identifier" in Figure  6 ) associated with the query, the query itself, the name and version of the node answering the query, the dataset produced by the node while processing the query, together with the bibliographic references extracted from the dataset.
This bibliographic information displays the references cited in the the Source element of the VAMDC-XSAMS file, and those references might be associated to any type of data included in the dataset. For a finer-grained understanding of how these references span the different elements of the dataset, one must investigate the VAMDC-XSAMS file generated by the query. In the future we will improve the management of this fine granularity. Currently the citation feature is implemented on about a third of the VAMDC connected databases as displayed in real-time on Figure  5  below "Accessed resources". Once a node has implemented the Query Store feature, any requests to that node are registered in the Query Store. The Query Store content may be directly explored at the url "https://cite. vamdc. eu".
By clicking on the Query tab, the results represented in Figure  5  are displayed: the user may filter the results by date or by database (Accessed resources) and search for a persistent identifier to which is attached the corresponding landing page. With the Query store, the VAMDC infrastructure has a way to remember queries permanently. The user can then use the uniquely generated and persistent link to view the detail of his query at a later date and even download the data again. Human readable Query-Store landing page, obtained while resolving the persistent identifier associated with a Query. In the present case the persistent identifier is: "17053a9a-e56e-451b-9bd2-8e0cddda0d5d". The database is STARK-B  [14].
Beyond the Actual Data Citation Processes Used by Editors: The Scholix Initiative and the Query Store Nowadays editors have essentially two basic mechanisms for linking articles to data repositories. One concerns entity linking, where the journal picks up a unique identifier or code in an article and establishes a deep link to the underlying data deposited elsewhere. The other one is banner linking, where an automated query is sent each time a new article is published to the external database in order to check whether there is data available for this article. We explain the limitation of these approaches with the following examples:.
• Let A be an article referencing a dataset D. If the data repository containing D has no ab initio idea that the article A exists, it has to search through all the Internet and through all the services of the existing journals if a paper citing D exists. The data trafic worldwidely generated by this approach is enormous, if we consider that there exists a lot of data centers containing thousands of datasets and that each datacenter, for each data-set it contains, will ask the same question to the same journals (currently approximatively 8M articles are registered into the different editor online services).
• Let us consider a datacenter where a dataset D contains references to an article A. How may the publisher of A know that D exists? These two examples show that the data-citation model currently adopted by the editors is not sustainable and does not meet the data-driven science community. The Scholix initiative  [15]  succeded in establishing a high level interoperability framework for exchanging information about the links between scholarly literature and data. It has been adopted by existing hubs or global aggregators of data-literature link information such as DataCite, CrossRef, OpenAIRE, EMBL-EBI, together with Eslevier and Springer editors. The Scholix recommendation is not implemented directly on the Query Store, but is a consequence of the interlinking between the Query-Store and the Zenodo open science repository  14.
The link between the Query Store and Zenodo is implemented using the Zenodo public REST API  15  in the Query Store software. The landing page of Figure  5  displays a button "Get a DOI" if the query has not already been assigned a DOI. By clicking on this button, the user triggers the Zenodo registration process: the file associated with the query is uploaded to Zenodo using the Data Set upload type and all the query-associated metadata are copied to corresponding Zenodo fields. When the upload process finishes successfully, Zenodo provides the Query-Store with a DOI which is stored in the Query-Store and associated to the query. When a user displays a landing page/query which has already been copied to Zenodo, the button "Get a DOI" is replaced by a DOI badge. Zenodo is indexed in OpenAIRE  16  , OpenAIRE implements Scholix through its Data-Litterature Interlinking Service  17  , therefore all the VAMDC queries registered by the Query Store in Zenodo are included in those infrastructures.
When some data extracted from VAMDC are cited (in papers and/or other datasets) through the DOI obtained by the couple (Query Store/Zenodo), the authors of the works referenced by the VAMDC data receive credit automatically. Indeed the scholarly links harvested from the Data-Litterature Interlinking Service 18  flow to the hubs implementing Scholix (e. g. , CrossRef and DataCite). In addition members from the SAO/NASA Astrophysics Data System (ADS) are active members of the RDA-Scholix Working Group and are working at implementing Scholix in ADS; therefore one implemented credits will flow automatically to the ADS system.
Analysis of Astrophysical Spectra An example of usage is the need to query atomic or molecular lines on a given frequency or wavelength range for Local Thermodynamic Equilibrium analysis. The VAMDC infrastructure facilitates greatly the obtaining of large quantities of data to which an equally large quantity of references are attached. The usual strategy of users is to cite the databases that have been queried, but rarely the original authors of the papers. Before VAMDC it was very painful to collect all the references, so it was understandable. With VAMDC the references are readily available, but the number of citation pages would outnumbered the content of the paper. We believe that this problem is now solved with our system. Users can query any databases, they can download the data files, use them in various applications, and prior to publication of their spectra analysis, he will decide which spectroscopic data have been the most relevant to their study.
A Scientific Use Case: Intercomparison of Databases We provide below a scientific use case that could be attractive to atmospheric physicists and planetologists. These communities usually use the HITRAN 160-character file format  [13]  as an input for their radiative transfer or atmospheric modeling codes. Thus, the original VAMDC-XSAMS output format produced by the data-nodes  19  is not directly nor easily useable by the different users. Within this context, we developed a new tool for converting to the HITRAN file-format any VAMDC-XSAMS file produced by any data-node containing molecular data; this application follows the VAMDC-XSAMS consumer protocol 20  standard of VAMDC. In order to reach this new level of interoperability, several databases had to perform some adjustments to their contents.
For instance, the HITRAN intensity unit (at 296 K) was added to the VAMDC-XSAMS standard; the CDMS  [16] [17] [18]  and JPL  [18, 19]  databases added a new field for this intensity unit in their VAMDC-XSAMS processor. As already mentioned, the HITRAN conversion (see Figure  4 ) is available for all the molecular databases already integrated into the VAMDC infrastructure (e. g. , HITRAN  [20] , CDMS  [16] [17] [18] , JPL  [18, 19] , MeCaSDa  [21, 22] ,. ): this allows quick and direct comparisons between them. To achieve this aim, a graphical chart can be produced by loading two HITRAN output files, either coming from the VAMDC portal thanks to the above-mentioned converter or from the HITRAN On line service  21. The following on-line tool  22  might be used for that purpose. Such combination of tools may help data producers to check the consistency of their data and to point out database content differences.
For instance, some databases include experimental, fitted or calculated ab initio data only, or mixings between these different sources, etc. Also, some databases include isotopic abundance factors, some other not. Figure  7  gives an example of database comparison between the HITRAN and JPL databases: such comparison would have been very cumbersome in the past. We mention that we intend to provide the same tools for the GEISA  [23]  file format as soon as possible. If VAMDC already provided interoperable access to all its data-nodes, the described HITRAN processor provides the community with an easy tool for comparing and crossmatching data coming from heterogeneous molecular databases. Molecular-scientists may particularly welcome these new features. As the potential adoption of this tool is wide, embedding the citation feature into the VAMDC portal is very important for data producers and providers: for a given analysis, the relevant and interesting data can be uniquely identified through the VAMDC-citation feature.
Prospective Use of VAMDC in Numerical Codes Packages: Example of Cloudy and PDR Code Cloudy is a non-local thermodynamic equilibrium spectral synthesis and plasma simulation code designed to simulate astrophysical environments and predict their spectra. A recent effort  [24]  has been to move Cloudy's atomic and molecular data into external databases. They use external databases such as CHIANTI  23  and LAMDA  24. For some ions they indicate using data from version 7. 1. 4 of the CHIANTI database. For versioning of LAMDA it is indicated that they downloaded the LAMDA data on the 30 June 2015. For data citation they advise users to go to the website of the CHIANTI  [25]  and of the LAMDA  [26]  databases. It should be noted that CHIANTI is part of VAMDC, when LAMDA is not. Nevertheless the collisional data of LAMDA are in the BASECOL  [27]  database.
The BASECOL  25  database has a version attached to each of its dataset and can be queried via the VAMDC portal or with the SPECTCOL tool  [2, 3]. BASECOL will be referenced in the Query Store. The Meudon PDR code 26   [28] , that can be used to study the physics and chemistry of diffuse clouds, photodissociation regions (PDRs), dark clouds, is another example of numerical code where the atomic and molecular data are externalized. By using VAMDC facilities to query data, it will be possible to uniquely identify the data in time and to cite the DOI in the code; again the producers of atomic and molecular data will be acknowledged through the pipeline that we have put together.
Prospective Use of VAMDC for Secondary Databases The VAMDC e-infrastructure could be heavily used to produce secondary databases, for example to prepare the analysis of specific space missions or for other specific purposes. For example the Belgian repository of fundamental atomic data and stellar spectra (BRASS)  [29]  aims to provide the largest systematic and homogeneous quality assessment of atomic data to date in terms of wavelength, atomic and stellar parameter coverage. To do so they retrieved atomic data from repositories and did cross-matching of data. They mention that the majority of repositories were retrieved via the VAMDC e-infrastructure and that they are grateful for the current efforts of the VAMDC team in homogenising the repositories as this has helped to expedite their comparisons and cross-match work.
Conclusion and Future Work From the start of the VAMDC project in 2009 one of our goals has been to increase the citation impact of data producers. Indeed we find that the current status of citing spectroscopic data is to cite the database. For example a search of the ADS NASA system with the word "CDMS" in the text shows that only the CDMS database's  [16] [17] [18]  URL and/or reference are provided in the majority of the astrophysical papers. It should be stressed that atomic and molecular data require months to be either measured or calculated, and therefore it is a loss of visibility and recognition that only databases be cited in users' papers. We believe that the Query Store coupled to the VAMDC portal now allow this flaw to be overcome, even if additional refinements need to be carried out.
Another interesting remark of one of the referee was the absence of selection in the references list as the references might be attached to different quantities in the VAMDC-XSAMS files, and the user might not want to use all of them. This issue will be addressed in the future. Currently only a few VAMDC connected databases have implemented the Query Store feature, Figure  6  shows the list of implemented databases at the time of publication. Future work includes the implementation of the citation feature on all the VAMDC connected databases by the end of 2019. Some additional future technical work will be to group the queries so that only one DOI is assigned to several queries. Finally we are currently implementing the Query Store feature into SPECTCOL  [2, 3] , another VAMDC tool that can query molecular spectroscopic and collisional databases, and then display and match the molecular data.
21 We created R code  22  (given in Appendix E) that uses Equations (11) and (12) to compute the adjusted effect sizes and standard errors and, then, uses R package "meta" to synthesize them. 23 We have also created a Stata 24 command that will become available through "mtm. uoi. gr" and would be an extension of the recently developed command metamiss2. 25 t is not always straightforward how to embed an expert's beliefs into a statistical model. We may have data on intermediate time points that show a very different effect across time points. It could be the case, depending on the field, that there is a seemingly significant effect during the first weeks that is lost at the end of the trial (transient effect). In depression trials, this may be the case when placebo or a suboptimal treatment is involved.
The full transparency of methods and techniques supporting a project of virtual reconstruction and the critical necessity to explicitly indicate information sources are highlighted, along with the need for alternative sources for comparison. It is underlined that complementary information gathered through prior research and investigation should be represented through the visual reconstructions, for the systematic analysis of both the architectural and historical data. This is a crucial problem, as many 3D reconstruction of historic buildings may not be fully documented despite containing a large, varied number of hypothetical, uncertain elements  [29]. In uncertain visualizations, there is yet to be a validated and approved language, and researchers mainly use blurring or false coloring to depict uncertainty in their datasets.
Potentials and Current Limitations Today, there is a great attempt to increase the BIM capabilities for more comprehensive, sophisticated, and query-able characteristics  [32]. However, there are no applications that cover the characteristics of historic states yet. Besides, post-data processing for attribute enhancement and the integration of different datasets is yet unresolved. The Industry Foundation Classes (IFC), a standard data format developed by international Alliance for Interoperability, aims to cover both geometries and semantics  [33]. Recent attempts for post-data processing and data management have been presented, both as open and commercial software solutions. Examples include the Building Information Modelserver  [34] , based on the IFC format, the Open BIM movement, initiated by Graphisoft, Tekla, and buildingSMART  [35]  for the enrichment of attribute data related to the building elements, zones, or complete buildings  [36].
Nevertheless, the enhanced attribute management for historic building chronology is not yet an integral part of them. As such, the integration of external attribute related data is currently not fully covered. In addition, historic buildings necessitate an integrated representation of the different physical states. This requires a multi-layered modeling approach in which we would be able select a temporal state and represent it in a 3D environment. Moreover, it is possible to create and access semantically-rich objects in a GIS environment, as proposed by Reference  [33] , particularly in the case of the semantic 3D city modeling process and urban conservation project assessment  [37]. In this context, attributes can be easily joined to geometries and a relational database can be built in GIS.
Conceptual Modeling and Design Considering the peculiarities of historic buildings, the information management systems helps to separate segments of information and relink them together in a structured way which allows new information to be derived, or to aid its link to 3D data  [43, 44]. This aid would come in the form of the semantic enrichment of 3D models, which is also a potential solution for archiving historic building's chronological information. The criteria for its production process needs to include: (i) the definition of mutual and hierarchical relationships, (ii) attribute management, (iii) 3D editing functionalities, (iv) spatial and multi-criteria query-able characteristics, (v) representation of datasets in 3D, and (vi) the representation of different restitution states. Regarding the defined criteria above, it is necessary to go beyond the geometric data which is composed of either 3D or 2D geometries.
Thus, the spaces are identifiable as different hollow volumes that one can interpret by following a cognitive process, as they are not physically solid geometries (Figure  4 ). Within the lighted area of the above-mentioned identification process, the conceptual modeling is carried out for the case study, in order to perform its systematic decomposition. In this process, the relationships among the different building parts, along with its hierarchies, are defined. Figure  5  represents the conceptual model prepared for the case study. A standard graphical notation and vocabulary are used, thanks to the Unified Modeling Language (UML)  [46]. UML is a language which helps with the representation of conceptual schemas graphically. The conceptual schema is independent of any specific architecture (for example, software, database system, and so forth).
After completing the conceptual schema, an entity-relationship (ER) model was manually derived for the successive database implementation (in this case, for PostgreSQL/PostGIS). For the creation of the PostgreSQL database schema (Figure  6 ), all the required database objects (for example, schemas, tables, constraints, data types, and so forth) were created. The reason for adopting the PostgreSQL relies on an advanced open-source database that works with the relational database management concept  [47]  and also on the availability of its spatial extension of PostGIS. This allows the use of special data types for geometric objects and enables their storage, along with their manipulation and querying. The capability is not limited to only 2D primitives, but also allows for 3D geometries in the form, for example, of polyhedral surfaces and triangulated irregular networks (TIN).
When visualizing in 3D, one starts to find out where there is a pre-existing gap of knowledge. Eventually, the manual methodology for the geometric modeling is derived through the use of computer-aided architectural design (CAAD) methods, as is adopted for the Kurşunlu Khan. Through this, it thus aims to represent a LoD in which a vital understanding of the building will be offered without exhaustive details. A deductive approach is considered for the logical segmentation of the Kurşunlu Khan, with correspondence to the classes and relations as defined in the conceptual data model. However, an inductive approach was applied to the building, as assigning different datasets included textual, numeric, vector, raster data types to objects and classes (Figure  8 ). Consequently, the creation of the segmented 3D model has been a process of reverse engineering.
The model is drawn from the smallest segments upwards, in a bottom-up approach, thus, allowing for the assignment of thematic datasets of building chronology to the objects of the 3D model. Autodesk 3DS Max is used as the primary 3D modeling tool (Figure  9 ). In the overall process, the aim was to produce 3D geometries that represented the different architectural elements. Accordingly, closed polygons and solid features are used. Essentially, 3D modeling was started with the extrusion process of vertical components (for example, walls) using a 2D floor plan created in AutoCAD. As such, it is not a straight-forward process and considerable effort must be paid to the following issues: (i) the segmentation decision for overlapping or joining edges, (ii) the differences in height information on a single object, (iii) openings that are in non-rectilinear forms need to be subtracted.
In 3D modeling, the goal is to characterize each building element with a specific name and function. This is in order to place it conceptually into the hierarchical tree diagram. In this case study, a naming convention was applied as a viable solution for the assignment of each corresponding object. This was done by means of the standardized name coding, due to the scarce capabilities in Autodesk 3DS Max to assign attributes to the geometries. Hence, during the use of other software, one may have to adopt other strategies in order to have the geometry classified. It is possible to regenerate a segmented 3D model for any restitution state. The building elements which remain original (unchanged), from one state to another, are preserved in the 3D model. Thus, the original building elements remain unchanged, while the modified ones are removed.
Semantic Enrichment Once the 3D objects representing the different architectural elements have been created, imported into the PostgreSQL database, and further grouped to generate the corresponding higher-hierarchy objects, (for example, spaces) they are enriched with attributes using the following criteria. Firstly, in the "building elements" table, the element name (arch, vault, wall, and so forth) and the element type (structural, architectural or finishing) are included. Those are followed by the attributes belonging to the construction technique and material usage (cut-stone, brickwork, stonework, and double color broad-stone with brick courses, and so forth), along with information regarding the element size (width, length, height), which are also indicated. It should be noted that these are not the exact measurements of the elements, but of the bounding boxes defining each of them.
Data Access and Visualization Options Data visualization enables decision makers to see analytics presented visually so they can grasp difficult concepts or identify new patterns. With an interactive visualization, one can take the concept a step further, by using technology to drill down into charts and graphs for more detail, interactively changing what data you see and how it is processed. In this work/approach/proposed solution, it is possible to export data to ArcGIS and visualize them in 3D, using certain software such as ArcScene. Through this method, it is possible to do a comparison of different restitution states and their reliability levels and gather a critical interpretation. In Figure  10 , the consecutive restitution states and their reliability levels are visualized in ArcGIS.