dataesr/scientific-paragraphs-categorization huggingface dataset:
Research Data As previously argued, the W3C WADM supports a wide range of different types of sources, while the ontology supports the encoding of the annotations of the different types of reading experience (emerging from the sampling of sources). About the different types of research activities, we rely on the development of different tools, making use of the ontology, designed for supporting specific tasks. In this regard, in READ-IT we consider the following types of tasks. -Crowdsourcing of sources of reading experience, including metadata and licensing through a webtool (as showcased at the SHARP 2019 conference  26  ). The new collected sources will be in the scope of the ontology only when annotated -Manual Annotation of sources by researchers, scholars, students and volunteers. In this regard, a prototype tool for text annotation is currently being tested.
Summarising, the W3C WADM provides the information and references to assess the source (target) and the agent responsible for the annotation (body) but does not provide a structure to report an evaluation as part of the annotation model. -Can we include anonymised data from reader focus groups? (interviews) The anonymised or pseudo-anonymised transcripts retain the relation between subjects and content. Therefore, transcripts of group meetings can be encoded using the concepts of Person and Reader. For instance, a group including two people each reporting about their readings can be encoded as in Fig. 17   -Can we represent the historical and social context of sources relevant for their correct interpretation and study (diaries)? -In case the evidence of reading is meant for someone, e. g.
The READ-IT model then enables generative research through the formulation of new, reasoned hypotheses on the experience of reading. The approach to modelling of the READ-IT ontology supports researchers by establishing a common framework of enquiry. For example, by defining reading agent and reading resource (consisting of medium and content) as two fundamental elements of reading, the model enables Humanities researchers to compare findings and hypotheses even if they are based on data that spans significant temporal and linguistic diversity. The modelling process also highlights questions that are still open for debate, for example those that pertain to the state of mind of the reader, helping to focus current and future research. With the READ-IT ontology, modelling transcends the encoding of the consolidated domain knowledge of a specific discipline and enables the convergence of knowledge from different disciplinary perspectives.
The ontology provides a common framework that can be applied beyond the isolated case studies that constitute the norm in Humanities research, to analyse issues that are still under debate and allow the definition of a common object of study. From a technical perspective, the READ-IT project and the development of its technological ecosystem required rethinking the role of the ontology. In READ-IT, the ontology facilitates the creation of new data sets, addressing limitations of data models used in previous projects. Furthermore, the ontology is used as a reference for the design and development of tools for supporting research activities, such as crowdsourcing reading experience and annotation of sources. The fulfilment of these roles emerged as a precondition for the assessment of the value of the ontology in relation to data and within the project framework.
Challenge of Legacy A second challenge concerns the conversion of legacy data from UK-RED (Reading Experience Database). In contrast to what was expected, the data collected during the approximately ten years of UK-RED related to reading experience could not be used to validate the ontology. Indeed, the UK-RED data concern concepts and relations almost exclusively related to the reused ontologies (CIDOC-CRM and FOAF). Thus, rather than providing a validation set for READ-IT ontology, UK-RED data actually provides a challenge in terms of preserving the value of a legacy project within a new theoretical and technological framework that is yet to be addressed. 27 https://readit-project. eu/consortium/.
Acknowledgments This work was partially supported by  Reading Europe -Advanced Data Investigation Tool  ( READ-IT ), a project funded by the  JPI Cultural Heritage  under the  European Union Horizon 2020 Research and Innovation programme  (grant agreement No  699523 ). This research work has been partially funded by the  Agence Nationale de la Recherche  ( ANR-17-JPCH-0001-01 ). This research work has been partially funded by (a) the  Research, Development and Innovation Program  from the  Universidad Politéctinca de Madrid (UPM)  ( Programa Propio de I+D+i de la UPM ) and (b) the project entitled "System for Evaluating and  Adapting Learning Materials  to the Easy-to-Read Methodology", approved by the  XIX UPM Call for Actions  to contribute to the achievement of the Sustainable Development Goals.
e. , larger ligands or alternative binding modes) as it do not account for the inherent flexibility of proteins in solution  (Kneller et al. , 2020). Here we present DINC-COVID, a webserver that accounts for the flexibility of both ligands and proteins in determining binding modes of small molecules and peptides interacting with SARS-CoV-2 proteins (Fig. 1 ). DINC-COVID is based on DINC  (Devaurs et al. , 2019) , a parallelized meta-docking approach that was shown to outperform conventional docking tools on several challenging datasets, especially for large and flexible ligands (e. g. , peptidomimetics and peptides). DINC-COVID users have the option to choose among three distinct ensembles for each SARS-CoV-2 target protein, including data from experimental structures (crystal) or from molecular dynamics with distinct force fields (charmm or gromos).
Generating representative ensembles of protein conformations is a challenging and computationally intensive task, thus each ensemble was tailored through robust data analysis and sampling methodology, reflecting different degrees of flexibility and features of the targeted binding site. By making DINC-COVID available as a webserver with pre-computed ensembles, we aim to facilitate and speed-up the use of ensemble docking in the search of efficient SARS-CoV-2 inhibitors (Fig. 1 ). The parallelized meta-docking approach DINC is used to sample alternative binding modes using all the receptor conformations in the ensemble. 4. Scoring and Ranking: All generated binding modes are rescored and ranked using three scoring functions (i. e. , Vina, Vinardo and AutoDock4). 5. Output complexes: The top scoring complexes are returned to the user, reflecting the flexibility of both the ligand and receptor.
Acknowledgements & Funding We thank the  Center for Research Computing (CRC)  at  Rice University  for supporting our use of ORION VM Pool. Use of CRC resources is supported by the Data Analysis and Visualization Cyberinfrastructure funded by  NSF  ( OCI-0959097 ) and by  Rice University. We also thank the  Centro Nacional de Supercomputação  ( CESUP/UFRGS, Brazil ), whose resources were used to perform our MD simulations. This work was funded in part by the  National Science Foundation IIBR:Informatics:RAPID program  ( 2033262 ), the  Cancer Prevention & Research Institute of Texas  through a fellowship from the  Gulf Cost Consortia on the Computational Cancer Biology Training Program  ( RP170593 ), and from the  National Library of Medicine. This work has also been supported in part by the  National Council for Scientific and Technological Development (Brazil ,  437373/2018-5 ), and by  Rice University  funds.
On the other hand,  Krolewski et al. (2019)  found no evidence for alignment in projection between galaxy spins, measured from Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) kinematics and filaments from the SDSS Main Galaxy Sample. There is currently no clear consensus on these various observations. However, these differences are not surprising, given that the signal is expected to be weak and various selection criteria need to be carefully examined. In this work, we measure the alignment between filaments identified in the SDSS Main Galaxy Sample and galaxy spins mea-sured from MaNGA kinematics in 3D. This is the first measurement of the spin alignment in 3D with the use of IFU spectroscopy kinematics. The outline of this paper is as follows. In Section 2, we briefly describe the data and methods used in this work.
Any incomplete data release of MaNGA should therefore be unbiased with respect to IFU sizes and hence a reasonable representation of the final sample scheduled to be complete in 2020. The sizes of the IFU are matched to each galaxy in the Primary sample to give a minimum coverage of 1. 5 effective radii (  )  (Law et al. 2015). The Secondary sample covers up-to a minimum of 2. 5  and the Colour-Enhanced supplement up-to 1. 5. All together these three sub-samples produce a galaxy population that is unbiased towards morphology, inclination and colour and provides a near flat distribution in stellar mass,  *. A full description of the MaNGA observing strategy is given in  Law et al. (2015) ;  Yan et al. (2016b). The raw data cubes are calibrated  (Yan et al. 2016a ) and reduced by the Data Reduction Pipeline (DRP;  Law et al.
Classifications in MaNGA are taken from a combination of GZ2 and GZ4 data releases 2. Identification of S0s relies on morphological classifications based on the the Deep Learning (DL) scheme introduced in Domínguez Sánchez et al. (  2018 ); here we give a quick outline. The algorithm is trained on two visually-based morphological catalogues; citizen science project GZ2 and expert classifications of  Nair & Abraham (2010). Morphology is defined for each MaNGA galaxy using SDSS photometry. The DL algorithm estimates a morphological T-type (e. g. see; Nair & Abraham 2010, for more information) which is first used to separate each galaxy into ETGs and LTGs. To select lenticulars, all ETGs are assigned a probability of being S0 ( 0 ) based on the presence of disk structure and dominance of the bulge.
ACKNOWLEDGEMENTS The authors thank the anonymous referee for comments that helped improve the clarity of the paper. We thank  Stéphane Rouberol  for the smooth running of the HORIZON Cluster, where some of the postprocessing was carried out. We thank  Thierry Sousbie  for provision of the D code (ascl. net/1302. 015 ). KK thanks  Christophe Pichon  for his exceptional support, enthusiasm and exacting attention to details that have been a great source of inspiration. KK also warmly thanks  Jounghun Lee  for enlightening discussions and helpful feedback. Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U. S. Department of Energy Office of Science , and the Participating Institutions. SDSS-IV acknowledges support and resources from the  Center for High Performance Computing at the University of Utah.
The authors verified their system design via a simulation approach and only considered scenarios with up to two trains. In our verification approach we prove the distributed signalling system mathematically and hence guarantee its safety for any number of trains. In the study by  Morley [Mor96]  the author formally proved a distributed protocol, which is used in the real-world railway signalling systems to reserve a route, which is jointly controlled by adjacent signalling systems. Even though the distributed signalling concepts of our works are different, the effects of message delays to the safety were considered in both works. Our developed distributed signalling protocol is also based on serialisability and similar to ones defined from transactions processing [EGLT76,  BSR80, GR92]  in centralised and distributed database systems.
While it is established that posterior fusion and the use of osteotomies allow reconstruction of the physiological sagittal profile in these patients  [18] , there are only very limited data regarding the effect of VBT on sagittal parameters  [10]. In particular, the role of VBT on the overall sagittal alignment has not yet been investigated. Furthermore, despite offering promising advantages in comparison to fusion, as it does not limit spine mobility  [19] , lumbar tethering is not performed by many surgeons as an anterior instrumentation may potentially have a kyphotic effect on the lumbar spine. To the best of our knowledge, there is no published data regarding the role of lumbar VBT in respect to sagittal parameters. The authors of the present study retrospectively analyzed and compared the sagittal profile of patients undergoing VBT before surgery and at the 2-year follow-up.
Demographic and perioperative data Of the 86 considered patients, 72 (84%) were female and 14 (16%) were male. The mean age at surgery was 13. 2 ± 2. 4 years. Thirty-six patients were Risser 0 (42%), seven were Risser 1 (8%), four were Risser 2 (5%), 19 were Risser 3 (22%) and 20 were Risser 4 (23%). Fifty-four patients underwent unilateral VBT (45 thoracic and nine lumbar instrumentations) and 32 underwent bilateral VBT. On average, 8. 5 ± 1. 8 levels were instrumented. The mean estimated blood loss was 318. 3 ± 163. 4 ml (Estimated blood volume 7%, range 2. 5-25%), with only one patient receiving a cell salvage transfusion. The mean length of surgery was 258. 3 ± 67. 8 min; the mean inpatient stay was 8. 0 ± 1. 3 days. Six postoperative complications were observed: five recurrent pleural effusion after bilateral VBT and one case of psoas irritation through the head of the L4 screw.
Background: In the era of big data, the intensive care unit (ICU) is likely to benefit from real-time computer analysis and modeling based on close patient monitoring and electronic health record data. The Medical Information Mart for Intensive Care (MIMIC) is the first open access database in the ICU domain. Many studies have shown that common data models (CDMs) improve database searching by allowing code, tools, and experience to be shared. The Observational Medical Outcomes Partnership (OMOP) CDM is spreading all over the world.
Methods: We transformed MIMIC (version 1. 4. 21) into OMOP format (version 5. 3. 3. 1) through semantic and structural mapping. The structural mapping aimed at moving the MIMIC data into the right place in OMOP, with some data transformations. The mapping was divided into 3 phases: conception, implementation, and evaluation. The conceptual mapping aimed at aligning the MIMIC local terminologies to OMOP's standard ones. It consisted of 3 phases: integration, alignment, and evaluation. A documented, tested, versioned, exemplified, and open repository was set up to support the transformation and improvement of the MIMIC community's source code. The resulting data set was evaluated over a 48-hour datathon. Results: With an investment of 2 people for 500 hours, 64% of the data items of the 26 MIMIC tables were standardized into the OMOP CDM and 78% of the source concepts mapped to reference terminologies.
Indeed, based on important medical information, expectations are to improve clinical outcomes and practices, enable personalized medicine and guide early warning systems, and also easily enroll a large, multicenter cohort, while minimizing costs  [6, 7]. The Medical Information Mart for Intensive Care (MIMIC)-III is a high-granularity data set of over 60,000 intensive care stays and 46,000 unique patients from 2 successive ICU systems at the Beth Israel Deaconess Medical Center in Boston, admitted from 2001 to 2012  [8]. It is the first ICU database available for free, and it has been intensively used in research, resulting in more than 300 international publications. However, its monocentric nature makes it difficult to generalize findings to other ICUs. For Kahn et al  [9] , "Database modelling is the process of determining how data are to be stored in a database.
" It specifies data types, constraints, relationships, and metadata definitions and provides a standardized way to represent resources/data and their relationships. Some studies have shown that using a common data model (CDM) by standardizing the structure (data model) and concepts (terminological model) of the database allows larger-scale multicenter research and exploitation of rare diseases or rare events and catalyzes research by sharing practices, source code, and tools  [10, 11]. However, some studies have shown that the results are not fully reproducible from one CDM to another  [12]  or from one center to another  [13]. Some approaches argue that keeping the local conceptual model  [14]  and the local structural model  [15]  leads to better results. On the one hand, keeping MIMIC in its specific form will not solve the limitation for multicenter research, but on the other hand, a fully standardized form would introduce other disadvantages, such as loss of data and lower computational performances.
The ideal solution is probably in between to allow local or standardized analysis, depending on the research question. The Observational Medical Outcomes Partnership (OMOP) CDM is a data model originally designed for multicenter research related to adverse drug events, which has been now extended to medical, laboratory, and genomic cases. OMOP provides structural and conceptual models relying on reference terminologies, such as Systematized Nomenclature of Medicine (SNOMED) for diagnostics, RxNORM for drugs, and Logical Observation Identifiers Names and Codes (LOINC) for laboratory results. Several examples of databases transformed into OMOP have been published  [16] [17] [18] , and OMOP stores more than half a billion patient records from around the world  [19, 20]. The OMOP conceptual model is based on a closure table pattern  [21]  capable of ingesting any simple, hierarchical, and also graph terminologies, such as SNOMED.
In addition to local terminologies, OMOP defines and maintains a set of standard terminologies to be mapped unidirectionally (local to standard) by implementers. Although OMOP has proven its reliability  [22] , the concept mapping process is known to have an impact on results  [23]  and the application of the same protocol on different data sources leads to different results  [13]. This shows the importance of keeping local terminologies so that local analysis is still possible. Previous preliminary work has been done on the translation of MIMIC into OMOP  [24]. This work remains to be refined and updated for proper evaluation. When comparing different CDMs  [10, 25] , OMOP obtained the best results for completeness; integrity; flexibility; simplicity of integration; implementability for a wider coverage of the structural and conceptual model; a more systematic analysis, thanks to an analytical library and to visualization tools; and easier access to data through SQL queries.
In terms of a conceptual approach, OMOP offers a broader set of standard concepts. In terms of a structural CDM, it is rigorous in how data should be loaded into specific tables, while other CDMs, such i2b2, are flexible with a general table that solves all data domains. This rigorous approach is necessary for standardization. Previous work has been performed to load MIMIC-III into i2b2  [26] ; however, the work could not be finalized due to the tricky concept mapping to standard terminologies tasks. OMOP has the advantage of not making the terminology-mapping step mandatory by keeping the local codes accessible to analysts. Compared to the Fast Healthcare Interoperability Resources (FHIR)  [27] , OMOP performs better as a conceptual CDM because the FHIR resources currently do not specify the terminology to be used for most of the attributes.
The OMOP relational model can be materialized in csv format and stored in any relational database, while the FHIR uses json files and needs some processing and higher skills to exploit. Among the above models, OMOP is the best candidate to overcome the MIMIC limitations mentioned earlier. Our paper was guided by the 2 following objectives:  (1)  transforming MIMIC into OMOP in terms of the time needed, skills required, and quality of the result and (2) evaluating the resulting data set to support efficient, shareable, and real-time analysis.
Data The majority of source code was implemented in PostgreSQL 9. 6. 9 (Postgres) because it is the primary support for the MIMIC database. It also allows the community to reproduce our work on limited resources without licensing costs and benefit from recent Postgres improvements in the data processing area. Some elaborated data transformations have been implemented as Postgres functions. OMOP CDM version 5. 3. 3. 1 (OMOP) tables were created from the provided scripts, with some changes documented in our scripts. OMOP defines 15 standardized CLINICAL data tables, 3 HEALTH system data tables, 2 HEALTH ECONOMICS data tables, 5 tables for DERIVED elements, and 12 tables for standardized VOCABULARY. The VOCABULARY tables were loaded from concepts downloaded from Athena [28], and the clinical and derived tables were loaded from MIMIC.
Structural Mapping The structural mapping aimed at moving the MIMIC data to the right place in OMOP, with some data transformations. It was organized into 3 phases: conception, implementation, and evaluation. The conception phase consisted of looping over each MIMIC table and choosing an equivalent location in OMOP for each column. In general, both projects were appropriately documented, but in several cases, we needed some clarification from MIMIC contributors on the dedicated MIMIC git repository  [29]  or from the OMOP community forum  [30]. Some trickier choices have been discussed in the MIMIC-OMOP git repository  [31]  and can be tracked in the commit logs. The implementation was done through an extract-transform-load (ETL) process that consisted of Postgres scripts to extract information from the source or concept mapping tables and then transform it and load it into an OMOP target table.
The scripts were managed sequentially through a main program. As a last resort, some modifications to the structural model of OMOP were made. A dedicated script recaps all of them and contains columns name modifications, new columns, column type modifications, or database indexing modifications. In particular, each source table has been given a unique global sequence incremented from 0, which serves as the primary key and links to the OMOP target tables. As a result, every record was uniquely identified, allowing us to chain the information with OMOP, while simplifying the maintenance of primary/foreign keys. Although evaluating a structural model is difficult  [32] , several papers have attempted to assess the quality of the CDM  [9, 25]. The criteria developed by Khan et al  [9] , which refer to the Moody and Shanks metrics  [32] , were adapted to assess the quality of the data transformation (Table  1 ).
To compare overall statistics, some SQL queries were set up to compare MIMIC and MIMIC-OMOP, and we provided basic characterizations of the populations. All tables were covered and tested through simple counts, aggregate counts, or distribution checks. We estimated the loss of information during the ETL process by measuring the percentage of both columns and rows lost in the process, as other previous studies have done  [17]. It is important to note that we chose not to keep irrelevant information: for example, some rows are known to be invalid in MIMIC or some information is redundant. Each ETL script was tested using pgTAP, a unit testing framework for Postgres. Each unit test script checked whether a particular OMOP target table was correctly loaded. Integrity constraints (primary keys, foreign keys, nonnull columns) were included to apply integrity checks at ETL run time.