dataesr/scientific-paragraphs-categorization huggingface dataset:
Looking ahead, future detailed analysis of the EHT and MWL observations on 2017 April 11 holds great promise for understanding the underlying mechanisms that drive Sgr A * 's flares and other variability. We thank an anonymous referee for their insight and helpful suggestions that have improved this paper. The Event Horizon Telescope Collaboration thanks the following organizations and programs: the Academia Sinica; the Academy of  Finland (projects 274477, 284495, 312496, 315721) ; the Agencia Nacional de Investigación y Desarrollo (ANID), Chile via NCN19_058 (TITANs) and Fondecyt 1221421, the Alexander von Humboldt Stiftung; an Alfred P. Sloan Research Fellowship; Allegro, the European ALMA Regional Centre node in the Netherlands, the NL astronomy research network NOVA and the astronomy institutes of the University of Amsterdam, Leiden University and Radboud University; the ALMA North America Development Fund; the Black Hole Initiative, which is funded by grants from the John MCIU through the "Center of Excellence Severo Ochoa" award for the Instituto de Astrofísica de Andalucía (SEV-2017-0709); the Spinoza Prize SPI 78-409; the South African Research Chairs Initiative, through the South African Radio Astronomy Observatory (SARAO, grant ID 77948), which is a facility of the National Research Foundation (NRF), an agency of the Department of Science and Innovation (DSI) of South Africa; the Toray Science Foundation; Swedish Research Council (VR); the US Department of Energy (USDOE) through the Los Alamos National Laboratory (operated by Triad National Security, LLC, for the National Nuclear Security Administration of the USDOE (Contract 89233218CNA000001); and the YCAA Prize Postdoctoral Fellowship.
043. 513 ) and grant  OCENW. KLEIN. 113 ; the  Dutch National Supercomputers, Cartesius and Snellius (NWO  Grant  2021. 013 ); the  EACOA  Fellowship awarded by the  East Asia Core Observatories Association , which consists of the  Academia Sinica Institute of Astronomy and Astrophysics , the  National Astronomical Observatory of Japan, Center for Astronomical Mega-Science, Chinese Academy of Sciences , and the  Korea Astronomy and Space Science Institute ; the  European Research Council (ERC)   Synergy Grant  " BlackHoleCam: Imaging the Event Horizon of Black Holes " (grant  610058 ); the  European Union   Horizon 2020 research and innovation programme  under grant agreements  RadioNet  ( No 730562 ) and  M2FINDERS (No 101018682 ); the  Generalitat Valenciana postdoctoral  grant  APOSTD/2018/177  and  GenT Program  (project  CIDEGENT/2018/021 ); MICINN Research Project  PID2019-108995GB-C22 ; the  European Research Council  for advanced grant " JETSET: Launching, propagation and emission of relativistic jets from binary mergers and across mass scales " (Grant No.
884631 ); the  Institute for Advanced Study ; the  Istituto Nazionale di Fisica Nucleare (INFN) sezione di Napoli , iniziative specifiche TEONGRAV; the  International Max Planck Research School for Astronomy and Astrophysics at the Universities of Bonn and Cologne ;  DFG research grant  " Jet physics on horizon scales and beyond " (Grant No. FR 4069/2-1 ); Joint Princeton/ Flatiron and Joint Columbia/Flatiron Postdoctoral Fellowships, research at the  Flatiron Institute  is supported by the  Simons Foundation ; the  Japan Ministry of Education, Culture, Sports, Science and Technology (MEXT ; grant  JPMXP1020200109 ); the  Japanese Government  (Monbukagakusho: MEXT) Scholarship; the  Japan Society for the Promotion of Science (JSPS)   Grant-in-Aid for JSPS Research Fellowship  ( JP17J08829 ); the  Joint Institute for Computational Fundamental Science, Japan ; the  Key Research Program of Frontier Sciences, Chinese Academy of Sciences  (CAS, grants QYZDJ-SSW-SLH057, We thank the staff at the participating observatories, correlation centers, and institutions for their enthusiastic support.
2017YFA0402700 ) of China and  Natural Science Foundation of China  grant  11873028. Additional funding support for the JCMT is provided by the  Science and Technologies Facility Council (UK)  and participating universities in the UK and  Canada. The LMT is a project operated by the  Instituto Nacional de Astrófisica, Óptica, y Electrónica (Mexico)  and the  University of Massachusetts at Amherst (USA). The IRAM 30-m telescope on Pico Veleta, Spain is operated by IRAM and supported by  CNRS  ( Centre National de la Recherche Scientifique, France ),  MPG  ( Max-Planck-Gesellschaft, Germany)  and  IGN (Instituto Geográfico Nacional, Spain). The SMT is operated by the  Arizona Radio Observatory , a part of the  Steward Observatory of the University of Arizona , with financial support of operations from the  State of Arizona  and financial support for instrumentation development from the  NSF.
5 Å  [12, 13, 17]. We refer to the two faces as the 3 and the 5 -face; for a definition of 3 -and 5 -nucleobase faces, see the Method Section. The lp. π distance has also to be >2. 0 Å to exclude rare "in-plane" contacts. Issues related to alternate conformations in database searches are described in  [66]. Such conformations (with occupancies < 1. 0) will be ignored in the following. Note that for generic lp. π contacts, the two nucleotides do not need to be consecutive or to belong to the same strand and the nucleobases do not need to be stacked.
These outliers may correspond to rare but real conformations of a given motif or to a locally deficient model based on poorly interpreted experimental data as detailed in Section 3. 4. When outliers are identified, it is advised to verify if the model agrees with experimental data and with current knowledge to decide if the explored structural fragment should be categorized as a new conformation, excluded from the dataset, or corrected. While correcting the structures seems the best option, choosing this process depends on the quality of the available electron density maps. When experimental data are of poor quality, it is not advised to attempt such a correction, and discarding the structure seems a better choice. However, when one is interested in using a structure for initiating MD simulations, it is advised to check the structures and eventually correct any visible flaws  [78].
Methods WebFR3D search tool: WebFR3D is the online implementation of FR3D, a generalpurpose RNA motif search tool  [4, 5]. WebFR3D makes it possible to search for RNA motifs of one to over a dozen nucleotides in different search modes. In a geometric search, the user specifies a list of RNA nucleotides from a 3D structure from the Protein Data Bank and a set of structures to search, and WebFR3D is guaranteed to find all matches up to a userspecified tolerance called the discrepancy cutoff. Geometric searches can be augmented with symbolic constraints to require certain base identities, pairwise interactions, or chain continuity constraints. Some motifs can be described entirely by symbolic constraints, and WebFR3D makes it possible to search for those based on the constraints alone. WebFR3D has pre-computed annotations of RNA base pairs, base stacking, base-backbone, and other interactions for all RNA-containing 3D structures in PDB.
For statistical surveys, one can avoid the redundancy inherent in the 3D structure database by searching a representative set of 3D structures, at a chosen resolution threshold  [67]. Attribution of a nucleobase 3 and 5 -face: For each standard RNA base, the BGSU RNA pipeline  [4, 5]  calculates the geometric center of the heavy (non-hydrogen) atoms of the base, weighting each heavy atom equally. From this point, the normal vector to the base is oriented to point toward the 3 -direction in a regular RNA helix. We refer to that nucleobase face as the 3 -face; the opposite face becomes the 5 -face. This convention was introduced earlier and is also used to annotate base-base stacking by FR3D  [4]. lp. π search: For each nucleotide pair, the relative locations of the base of the first nucleotide and the backbone oxygen atoms of the second are computed in the following way.
QSPR and graph machine models QSPR regressions are very popular to quantitatively correlate molecular descriptors to physicochemical properties. Such descriptors fall into five classes: topological, geometrical, thermodynamic, electronic or constitutional  [102]. QSPR models are regression and classification, that are based on the simple idea that similar structural features are likely to modify physicochemical properties in a similar way  [102]. QSPR methods can take the form of multilinear regression (MLR), Partial Least Squares (PLS) or artificial neural networks (ANN)  [89, [109] [110] [111] [112] [113] [114]. Such models require to build an extensive dataset of molecules, which gathers for each structure a set of well-chosen molecular descriptors and the values of the property of interest.
Such database is also known as learning database, because it allows carrying out the required first learning phase. This step consists in learning the structure-property relationships from the examples of the dataset in order to then be able to generalize, i. e. to predict the physicochemical properties of unknown molecules. The accuracy of the estimation strongly depends on the quality of the learning step. Examples should be carefully gathered and the selected values cautiously extracted from the literature: two independent and consistent bibliographic references are required to ensure the reliability and the learning dataset  [89]. Based on neural networks, an alternative method to conventional QSPR models has been proposed by Goulon et al. i. e. the "graph machines" approach  [115]. It differs from the QSPR regressions by the input parameters that are required.
The initial connectivity of the atoms concerned by the deletion of the edge is however preserved Fig. 14  illustrates the construction of the graphs of 3-buten-1-ol (left) and 2,3-dimethylphenol (right) starting from their chemical structures. As graphs should be acyclic, one edge of 2,3-dimethylphenol is removed. In this case, the degrees of atoms involved in the removed edge remains unchanged (dotted red line in Fig. 14 ). In the specific case of graph machines, the database gathers the graph of each compound and the physicochemical value associated. Goussard et al. compared the efficiency of four modelling approaches, namely, group contributions, corresponding-states principle, neural networks, and graph machines, for the estimation of the surface tension of 269 pure liquid compounds from their molecular structure.
This study focuses on liquids containing only carbon, oxygen, hydrogen, Fig. 13. Overview of the different steps involved in the COSMO-RS approach to predict some relevant physicochemical properties of potential or existing emollients. or silicon atoms since our purpose is to predict the surface tension of cosmetic oils. The graph machine approach provides the best results, estimating the surface tensions of 23 cosmetic oils, such as hemisqualane, isopropyl myristate, or decamethylcyclopentasiloxane (D5), with accuracy better than 1 mN⋅m -1  [89]. These authors adopted the same strategy to investigate the viscosity of liquids at 25 • C by comparing several modelling tools. Models were trained from a database of viscosities of 300 molecules covering a wide diversity of chemical structures, namely, alkanes, ethers, esters, ketones, carbonates, acids, alcohols, silanes, and siloxanes, as well as different chemical backbone, i.
Acknowledgements The authors would like to thank  Dr. Pieter Van der Weeën  and  Mrs. Aurélie Fievez  for fruitful discussions and the  Company Oleon  for its financial support through a  PhD grant. Chevreul Institute  ( FR 2638 ),  Ministère de l'Enseignement Supérieur et de la Recherche ,  Région Hauts de France  and  FEDER  are also acknowledged for supporting and funding partially this work.
, 2020) , states that in the absence of errors two facing HFR sites should provide the same estimates of radial velocities (differing only in the sign) at the midpoint of the baseline that joins them, since the range and the angular distribution are similar. This self-consistency test presents some benefits like the nonexistence of horizontal-scale or depth mismatch, as the two involved sites are operating in the same frequency, providing two current datasets with, in principle, identical origin and nature. In terms of Lagrangian assessment, it is worth mentioning that the Tracking Oil Spill and Coastal Awareness (TOSCA) project experience  (Bellomo et al. , 2015)  constituted one of the first coordinated initiatives at the Mediterranean level to test the precision of a core of 12 HFRs and identify a set of good practices for pollution mitigation.
, 18, 761-795, 2022 https://doi. org/10. 5194/os-18-761-2022 manufacturer or antenna design and setup, and they include the following: site requirements for optimal HFR performance; typical authorizations needed for installing and operating an HFR station; list of basic accessories for ensuring protection of the equipment, remote management, and reliable data transfer; items to be evaluated for estimation of deployment and operating costs; key elements for a correct setup of HFR systems and suggestions for monitoring their performances; scalable data management encompassing a common protocol for data processing and the standardization of a single HFR interoperable data format with a unified list of metadata descriptors; and unified procedures for quality assurance-quality control (QA-QC) of HFR data in real time.
Data flow: from providers to distribution via the EU HFR Node In 2014, EuroGOOS launched the HFR task team (Fig. 2 ) with the aim of promoting the implementation of an operational HFR network in Europe based on coordinated data management and integration of basic products into the major platforms for marine data distribution technology  (Corgnati et al. , 2021). In 2015, a pilot action coordinated by EMODnet Physics began to develop a strategy for assembling HFR metadata and data products within Europe in a uniform way to make them easily accessible and more interoperable (Fig. 2 ). The European Union (EU) project JERICO-NEXT (https://www. jerico-ri. eu/previous-project/ jerico-next/, last access: 31 March 2022), launched in 2015, aimed to provide procedures and methodologies to enable HFR data to comply with the international standards regarding their quality and metadata, with the overall goal of integrating the European coastal observatories.
In parallel, the SeaDataCloud EU project, launched in 2016, contributed to the integration and long-term preservation of historical time series from HFR into the SeaDataNet infrastructure (Fig. 2 ) by defining standard interoperable data and Common Data Index (CDI) metadata formats as well as quality control (QC) standard procedures for historical data. In 2016 as well, the CMEMS Service Evolution Call supported the INCREASE project, which set the basis for the integration of existing European HFR operational systems into the CMEMS-INSTAC. More recently, the EU projects JERICO-S3 and EuroSea have been continuing these efforts to further expand the standardization and interoperability of HFR data in order to promote the distribution of high-quality HFR data and improve their impact in scientific, operational, and societal applications.
The results of these integrated efforts are significant and allowed the harmonization of system requirements as well as design, data quality, and standardization of HFR data access and tools  (Mantovani et al. , 2020). Thanks to these achievements, the inclusion of HFR data into CMEMS-INSTAC, EMODnet Physics, and SDC Data Access was decided to ensure the improved management of several related key issues such as marine safety, marine resources, coastal and marine environment, and weather, climate, and seasonal forecast. The EU HFR Node (Fig. 7 ) was established in 2018 by AZTI, CNR-ISMAR, and SOCIB under the coordination of the EuroGOOS HFR task team  (Rubio et al. , 2017)  as the focal point and operational asset in Europe for HFR data management and dissemination (https://thredds. hfrnode.
eu, last access: 31 March 2022) by promoting networking between EU infrastructures, marine data portals, and the global HFR network. The EU HFR Node has been fully operational since December 2018 to distribute tools and support for standardization to HFR providers as well as standardized near-real-time (NRT) and delayed-mode HFR radial and total current data to CMEMS-INSTAC, EMODnet Physics, and SDC Data Access. Within the European framework, the EU HFR Node is currently managing data from 16 systems (https://thredds. hfrnode. eu, last access: 31 March 2022). In particular, 5 of these 16 systems (31 %) are deployed in the Mediterranean coastline and belong to the MONGOOS network: HFR-Gibraltar, HFR-Ibiza, HFR-DeltaEbro, HFR-TirLig, and HFR-NAdr (Fig. 1 ). Furthermore, the EU HFR Node integrates and delivers US HFR network data to the aforementioned data portals.
In particular, the EU HFR Node implements the operational chain, which encompasses data acquisition and harvesting, harmonization, formatting, QC, validation and assessment, NRT data delivery, and historical data distribution with different reprocessing levels. The core of this service consists of the continuous development of the data model and the processing standards through discussion with operators, providers, distributors, and international experts. Based on this, the EU HFR Node maintains and updates manuals, procedure guidelines, and software tools, and it pushes them towards the HFR operators, providers, and managers via repositories and training workshops. In particular, the software tools for processing native HFR data for QC and converting them to the standard format for distribution are continuously made available to HFR operators via public GitHub repositories and releases with a DOI assigned (https://doi.
org/10. 5281/zenodo. 2639555). The data processing and distribution service is founded on a simple and very effective rule (that applies only to HFR observations): if the data provider can set up the data flow according to the defined standards, the node only checks and distributes the datasets. If the data center cannot set up the data flow, the EU HFR Node directly harvests the raw data from the provider, harmonizes, quality controls, and formats https://doi. org/10. 5194/os-18-761-2022 Ocean Sci. , 18, 761-795, 2022.
The European common data and metadata model for real-time high-frequency radar surface current data An appropriate file description (i. e. , "comprehensive metadata"), complying with accepted standards, is crucial for enforcing data discovery and access. The detailed metadata description is a prerequisite for the fully operational implementation, providing an inventory of the continuously available data for operational models. It is also necessary for providing a detailed overview of marine monitoring programs relevant for the Marine Strategy Framework Directive (MSFD) implementation. In the framework of the aforementioned initiatives and projects, in particular within the JERICO-NEXT and IN-CREASE projects, a model for HFR-derived data and metadata was defined and later implemented to be the official European standard for HFR real-time data in order to ensure efficient and automated HFR data discovery and interoperability with tools and services across distributed and heterogeneous Earth science data systems.
The European HFR data format and metadata model have been defined and implemented according to the standards of the Open Geospatial Consortium (OGC) for access and deliv-ery of geospatial data and are compliant with (i) the Climate and Forecast Metadata Convention CF-1. 6, (ii) the Unidata NetCDF Attribute Convention for Data Discovery (ACDD), (iii) the OceanSITES convention, (iv) the CMEMS-INSTAC and supplemental digital content (SDC) requirements, (v) the recommendations given by the Radiowave Operators Working Group (US ROWG), and (vi) the INSPIRE (Infrastructure for spatial information in Europe) directive. The model specifies the file format, the global attribute scheme, the dimensions, the coordinates, the data and QC variables as well as their syntax, the QC procedures, and the flagging policy.
Variable short names from SeaDataNet (SDN) P09 controlled vocabulary are recommended. CF-1. 6 standard_names are required when available. The European common data and metadata model for real-time HFR surface current data are comprehensively de-Ocean Sci. , 18, 761-795, 2022 https://doi. org/10. 5194/os-18-761-2022 scribed in the JERICO-NEXT Deliverable D5. 14  (Corgnati et al. , 2018). In order to fulfill the specific requirements of CMEMS-INSTAC, EMODnet Physics, and SDC Data Access that have been operationally distributing NRT and historical HFR data since 2019, the standard data and metadata model were declined for those specific applications: the manual for the standard data and metadata model adopted in CMEMS-INSTAC and EMODnet Physics is described in  Carval et al. (2020) , and the one for SDC Data Access is described in  Corgnati et al.
Quality control procedures The European common data and metadata model for realtime HFR data require a battery of QC tests in order to ensure the delivery of high-quality data and to describe in a quantitative way the accuracy of the physical information as well as to detect occasional non-realistic current vectors or artifacts (defined as spikes, spurious values, or unreliable data), generally detected at the outer edges of the HFR domain and flagged in accordance with a predefined protocol. A battery of QC tests is consistently applied to HFR data as defined by the EuroGOOS Data Management, Exchange and Quality Work Group (DATAMEQ) recommendations on real-time QC and building on the Quality Assurance/Quality Control of Real-Time Oceanographic Data (QARTOD) manual produced by the US Integrated Ocean Observing System (IOOS).
These mandatory QC tests have been selected in strict collaboration with most of the European HFR operators and data providers. While they are meant as a minimum set of QC needed for data distribution, any further QC processing of HFR data is strongly encouraged. These standard sets of tests, which are manufacturerindependent, have been defined for both radial and total velocity data. The battery of mandatory QC tests and the flagging scheme are thoroughly described in  Corgnati et al. (2018). Each QC test results in a flag related to each data vector: the flag is contained in the specific test variable. These variables can be matrices with the same dimensions of the evaluated data variable, containing, for each cell, the flag related to the vector lying in that cell in the case that the QC test evaluates each cell of the gridded data.
Or they can be scalars in the case that the QC test assesses an overall property of the data file. An overall QC variable reports the quality flags related to the results of all the QC tests: it is categorized as a "good data" flag if and only if all QC tests are successfully passed by the data. The mandatory QC tests for HFR radial velocity data are syntax, over water, variance threshold, velocity threshold, median filter, temporal derivative, average radial bearing, and radial count. The mandatory QC tests for HFR total velocity data are data density threshold, GDOP threshold, variance threshold, velocity threshold, and temporal derivative. However, the main drawback lies with the potential removal of accurate data when the discriminating algorithm is based on tight thresholds. Therefore, HFR operators will need to select, and keep updated, the most suitable thresholds for some of these tests.