dataesr/scientific-paragraphs-categorization huggingface dataset:
Extraction and Linking of Named Entities The ISSA pipeline relies on three tools to identify, disambiguate and link NEs from the articles (title, abstract and body) of the scientific archive: -DBpedia Spotlight  [15]  annotates text in eight different languages with DBpedia entities. Disambiguation is carried out by entity linking using a generative model with maximum likelihood. -Entity-fishing  [31]  identifies and disambiguates NEs against Wikidata. It relies on FastText word embeddings to generate candidates and ranks them with gradient tree boosting and features derived from relations and context. -Dictionary projection annotation performs in-domain NEs with pyclinrec  5 and disambiguation is performed with EigenThemes [9] using hyperbolic graph embeddings  [14]  computed from the corresponding domain thesauri.
For each article, the pipeline invokes each of the three tools and translates their respective outputs into an RDF representation. An additional post-processing step specifically identifies geographic entities by looking for GeoNames mappings in the corresponding Wikidata concepts. Like thematic descriptors, NEs are modelled in RDF as annotations, as exemplified in Listing 1. 1 (lines  [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]. The annotation points to the annotated article (property schema:about). The matched text fragment is described in the annotation target that points to the article part wherein the NE was recognized (title, abstract or body), and locates it with start and end offsets. The annotation body is the URI of the resource that the NE links to (Wikidata and Geonames in the example).
We have defined an RDF model that represents articles' metadata and content using well-adopted vocabularies: DCMI 7 , FRBR-aligned Bibliographic Ontology (FaBiO)  [29] , Bibliographic Ontology 8 , FOAF  [18]  and Schema. org [19]. A comprehensive description of the RDF representation together with examples are provided in the pipeline's Github repository. 9  Application to the Agritrop use case. In Agritrop, OAI-PMH is used to retrieve the common metadata as well as the abstract and thematic descriptors defined by the curators, that are mapped to RDF using the model described in Section 3. 1. Given that the text and abstract extracted from the PDF files by Grobid can be of poor quality, we provide a mechanism to coalesce title and abstract retrieved from the metadata with those extracted from full text.
Integrating all Building Blocks into a Comprehensive Pipeline Running the extractors. The pipeline's Github repository provides multiple scripts 10 that orchestrate and automate the processing steps from downloading articles to yielding the resulting RDF KG. To facilitate the deployment, thirdparty tools Grobid, Annif, Entity-fishing and DBpedia Spotlight are dockerized 6 https://www. openarchives. org/pmh/ 7 https://www. dublincore. org/specifications/dublin-core/dcmi-terms/ 8 http://bibliontology. com/specification. html 9 https://github. com/issa-project/issa-pipeline/blob/main/doc/ 10 https://github. com/issa-project/issa-pipeline/tree/main/pipeline using official Docker images. In addition, DBpedia Spotlight 11  and Entity-fishing are deployed using pre-trained English and French models.
Generation and publication of the KG. The translation into RDF of the outputs of each step is carried out using Morph-xR2RML,  12  an implementation of the xR2RML mapping language  [27]  for MongoDB databases. Thus, the next steps consist of importing the outputs into MongoDB, pre-processing them to filter out unneeded or invalid data, and apply the translation rules with Morph-xR2RML. Lastly, the produced RDF files are loaded into a dockerized Virtuoso OS server deployed using an official Docker image. An additional customizable RDF Turtle file  13  describes the generated RDF dataset using the DCAT  [22] , VOID  [8]  and SPARQL-SD  [33]  vocabularies. Incremental updates. After initial publication, periodic invocation of the pipeline can be scheduled to incrementally update the KG with new documents and retrain the Annif models.
Application to the Agritrop use case. In the case of Agritrop, the pipeline processed the 12,000 open-access articles in English and French. Annif and the NE extractors were deployed on a virtual machine with 12 CPU cores (2. 3GHz) and 32GB RAM, the processing took 11 hours. MongoDB and Morph-xR2RML were deployed on the same virtual machine. The upload in MongoDB of the documents produced by the NE and descriptor extractors, their pre-processing, the generation of RDF files and their loading into Virtuoso took 1h05m. Additional insights into the dataset generated for Agritrop are given in section 5. Pipeline Reusability. The pipeline can be customized to meet the needs of any scientific archive and community. The OAI-PMH protocol is very common among scientific archives, such that connecting to archives implementing it should be straightforward.
Augmented Visualization of Metadata Records The primary role of an open archive is to provide access to the bibliographic records of the resources it contains. The ISSA prototype meets this need by enabling users to access an enriched bibliographic view of each open access article in the database. Beyond merely presenting common article metadata, this service (exemplified in Fig. 2  for the case of Agritrop) visualizes the article abstract where extracted NEs are highlighted and point to the associated knowledge bases (Wikidata, DBpedia, GeoNames,. ). Thematic descriptors automatically extracted with text classification and linked to the considered thesaurus (e. g. AGROVOC) are also shown, along with a cartographic visualization of the places mentioned in the article, linked to GeoNames. Technically, the service consists of a React.
1. The mining process casts scientific publications as transactions and thematic descriptors as itemsets. Although the approach helps to reduce and focus the exploration of a dataset, researchers are still confronted with a large set of rules. Therefore, we leverage the potential of visualization to assist the exploration of these rules and thus the discovery of hidden knowledge in the database. In particular, we explore the data using ARViz 14  (Fig. 3 ), a generic tool designed to support the exploration of association rules via three complementary visualization techniques (i. e. a scatter plot, a chord diagram, and an association graph) providing the distribution of rules over the measures of interest and a focused exploration of (i) items, to find and/or describe the rules involving a particular item, and (ii) rules, to detect distinguishable association rules that are worth saving for knowledge acquisition.
5 Source Code, Dataset, Documentation Source Code Availability. From a technical perspective, ISSA consists of several software components integrated together. Third-party components such as Annif, Grobid, DBpedia Spotlight and Entity-fishing are obtained through their official Docker image distributions on DockerHub. 17 The components developed within the ISSA project are available on Github repositories, licensed under the open-source, free-software Apache 2. 0 license, and assigned a DOI that guarantees long-term availability. This information is summarized in Table  1. In particular, the processing pipeline's repository provides multiple scripts that orchestrate and automate the different steps from downloading the articles to running the triple store, together with documentation including deployment instructions, licensing and RDF modelling description.
Sustainability plan. In the short term, CIRAD wishes to dedicate efforts to the deployment of the ISSA pipeline and visualization tools for production use. This will be the opportunity to assess the quality of the deployment procedure and documentation, and improve them when necessary. Furthermore, a key motivation of the ISSA project is to provide a solution generic enough to be reused with various scientific archives. Therefore, we intend to provide support to communities showing interest in this solution and willing to experiment with it for their own needs. Depending on further funding opportunities, this may range from a best-effort support to more substantial collaborations. ISSA Agritrop Dataset. The dataset generated by the pipeline for the Agritrop archive is available as a downloadable, DOI-identified RDF dump, and through a Virtuoso OS triple store and SPARQL endpoint.
This information is summarized in Table  2  along with basic statistics. The RDF model underlying the dataset is provided in the Github repository. 18 At the time of writing, the URIs are not yet dereferenceable due to on-going security validation procedures required by CIRAD's administrators. By contrast, article content is ruled by various licenses that consequently also apply to the full text content extracted from the articles and stored in the ISSA dataset. The additional data produced by mining the articles (thematic descriptors, NEs) is published under the Open Data Commons Attribution License 1. 0 (ODC-By). 20.
Arviz and association rules mining Apache 2. 0 10. 5281/zenodo. 6511786 10. 5281/zenodo. 6511146 https://github. com/Wimmics/arviz https://github. com/Wimmics/association-rules-mining MGExplorer Apache 2. 0 10. 5281/zenodo. 6511782 https://github. com/Wimmics/ldviz Article visualization Apache 2. 0 10. 5281/zenodo. 6510031 10. 5281/zenodo. 6510029 https://github. com/issa-project/web-visualization https://github. com/issa-project/web-backend.
" Finally, the use of quality control (QC) flags is simplified by consolidating the three WOCE QC flag tables into a single flagging scheme, omitting flags that are either obsolete or rarely used. Standardized missing value indicators are also recommended. In addition, tools are presented to standardize calculations of derived oceanographic properties and parameters. Oceanographers often use two of the traditionally measured seawater carbon dioxide (CO 2 ) system parameters [namely, total dissolved inorganic carbon content (DIC), total alkalinity content (TA), pH, and carbon dioxide partial pressure (pCO 2 ) or fugacity (f CO 2 )] to compute the complete carbonate system using a program such as CO2SYS. CO2SYS was initially developed by  Lewis and Wallace (1998)  and it was later adapted for Microsoft Excel and MATLAB by  Pierrot et al.
(2006). The code was vectorized, refined, and optimized for computational speed by  van Heuven et al. (2011)  as a MATLAB program. These carbonate chemistry calculations, based on thermodynamic equilibria, are now available in a dozen public computer packages. Orr et al. (2015)  compared ten of them using common input data and the set of equilibrium constants then recommended for best practices. All packages calculate values that agreed within 0. 2 µatm for f CO 2 , 0. 0002 units for pH, and 0. 1 µmol kg -1 for carbonate ion content ([CO 3 2-]), in terms of surface zonal-mean values, although the overall uncertainties of such calculated quantities were much larger. Options for error propagation (included in the original CO2SYS) were added recently by  Orr et al. (2018)  to CO2SYS-Excel (Visual Basic), CO2SYS-MATLAB (MATLAB), seacarb  (R) , and mocsy (Fortran).
Some laboratories have begun to include carbonate ion content ([CO 3 2-]) as an additional measurable parameter of the seawater CO 2 system  (Byrne and Yao, 2008; Sharp and Byrne, 2019). In this study, we report upgraded CO2SYS programs (available in Excel, MATLAB/GNU Octave, and Python) and the R package seacarb that accept [CO 3 2-], as well as [HCO 3 -] (bicarbonate ion content), and [CO 2 * ] (the sum of dissolved carbon dioxide  [CO 2(aq)  ] and carbonic acid content [H 2 CO 3 ]) as input variables. These additions to the CO 2 system calculation programs now allow adjustment of measured [CO 3 2-] to in situ conditions, and calculation of other seawater CO 2 system parameters. By standardizing column headers, quality control flags, missing value indicators, and offering tools to standardize calculations of certain derived properties for the international chemical oceanographic community, this work will promote the use of chemical oceanographic data files in a uniform and consistent format.
COLUMN HEADER ABBREVIATION STANDARDS The updated column header abbreviations for discrete chemical oceanographic data (Table  1 ) are created in accordance with the following considerations: These abbreviations are discussed below in the order in which they appear Table  1. Their corresponding CCHDO Exchange format terms are listed in the second column of Table  1. The Global Ocean Data Analysis Product (GLODAP)  (Olsen et al. , 2020)  and Climate and Forecast (CF)  (Hassell et al. , 2017)  terms are listed in Supplementary Table  2. Expedition codes (EXPOCODE) uniquely identify specific voyages. These codes are composed of the four-character ship code of the International Council for the Exploration of the Sea (ICES) and the date of departure from port (Coordinated Universal Time, or UTC) using ISO-8601 format (YYYYMMDD) (Table  1 ).
For example, a research expedition onboard National Oceanic and Atmospheric Administration (NOAA) Ship Ronald H. Brown (ICES code: 33RO) leaving the port on August 27, 2015 (Coordinated Universal Time, or UTC) would have an EXPOCODE of 33RO20150827. In rare cases, if a ship leaves on a single day for multiple expeditions, the two-digit hour of departure from port (24-h format) can also be appended to the EXPOCODE with an extra "H" (hour) before the twodigit hour (e. g. , 33RO20150827H15). The ICES ship code can be accessed through https://vocab. ices. dk. If the utilized vessel does not have an ICES ship code, one can be obtained from either ICES or NOAA's National Centers for Environmental Information (NCEI). Cruise identification (Cruise_ID) is the particular ship cruise identifier or other alias for a cruise (Table  1 ).
It is recommended that each data row have a unique Sample_ID. This makes it easier to pinpoint a row when communicating with data providers and allows QC tools to generate statistics about what has been changed during the QC process. Sample_IDs can potentially be linked to persistent identifiers, e. g. , International Geo Sample Numbers (IGSNs)  (Plomp, 2020). All date and time information should be in UTC. Cruise reports should record all time information in UTC; this will avoid propagation of complex shifts in the ship's local time zone(s) into the databases. It is not necessary to report the local time (LT) as an additional column in a data file. However, it is a good practice to record the time zone(s) that the ship was in, and the time difference in hours between local time and UTC (e.
g. , local time from stations 1-40 was UTC -4, and for stations 41-95, UTC -3, etc. ) in cruise reports. This is particularly necessary for biological, physical, and biogeochemical parameters that are influenced by the diurnal cycle. Date in UTC should be reported as separate year, month, and day values (each in its own column, a total of three columns), instead of a combined date column, so as to reduce confusion caused by different date formats (e. g. , international vs. United States format). Yearday_UTC refers to the day number, including a fractional component, in an annual cycle, calculated using Eq. (  2 ): Yearday_UTC = datefun(Year_UTC, Month_UTC, Day_UTC) -datefun(Year_UTC, 1, 1) + Time_UTC + 1 (2) Where, "datefun" is the date function of a program (e. g. , in Excel, datefun would be "DATE").
These functions convert year, month, and day into an integer where 1 day is equal to 1. Two digits after the decimal point are recommended, providing a resolution in time of 14 min. For example, 18:00 on January 1 means a Yearday_UTC of 1. 75, and 06:00 on December 31 of a leap year means a Yearday_UTC of 366. 25. Note that Yearday_UTC starts with 1, instead of 0. Yearday_UTC is often incorrectly called Julian Day by oceanographers and meteorologists. Julian Day is the count starting from noon on January 1, 4713 BC (UTC), and starts with 0, instead of 1. For example, January 5, 2021 has a Yearday_UTC of 5, but a Julian Day of 2,459,220. Time in UTC should be recorded in ISO-8601 format (hh:mm:ss), and the user is requested to ensure that the numerical values associated with time in a program such as Excel are a fraction of 24 h, with a range of 0-1.
MISSING VALUE INDICATORS The WOCE manual recommends that "-9" be used in places where data are missing  (Joyce and Corry, 1994). However, the most commonly used missing value indicators have been "-999" and "-9999, " because "-9" is a viable number for some variables. On rare occasions, extremely large numbers are also used as missing value indicators. To be consistent, "-999" is recommended to indicate missing values for all chemical oceanographic data files. "Not a number" (NaN) can also be used for programs that handle them well (e. g. , MATLAB and IGOR).
Excel Tool for TEOS-10 Calculations A newly developed Excel program (GSW_Sys_v1. 0. xlsm) 1  uses the International Thermodynamic Equation of Seawater -2010 (TEOS-10) to calculate depth (unit: meter) [or pressure (unit: dbar), depending on the input], Absolute Salinity (S A , unit: g/kg), Conservative Temperature ( , unit: degree Celsius), potential temperature (θ, unit: degree Celsius), and potential density anomaly (σ θ , unit: kg m -3 ) from input of location (latitude and longitude), depth or pressure, practical salinity, and temperature. The name GSW derives from the Gibbs SeaWater (GSW) oceanographic toolbox that was developed by  McDougall and Barker (2011). The Excel program also calculates apparent oxygen utilization (AOU) and percent oxygen saturation using the expression in  Garcia and Gordon (1992).
This tool should be cited as  Pierrot et al. (2021). For more information about TEOS-10, refer to section "TEOS-10" of the Supplementary Material. fCO2_Calc Program to Calculate pCO 2 and fCO 2 From xCO 2 A newly developed fCO2_Calc program 2  is also presented to standardize the calculation of partial pressure of carbon dioxide (pCO 2 ) and fugacity of carbon dioxide (f CO 2 ) from molecular ratio (mole fraction) of carbon dioxide in dry air (xCO 2 ) measurements. f CO 2 at the temperature of equilibration is calculated according to  Wanninkhof and Thoning (1993) ;  Dickson et al. (2007) , and  Pierrot et al. (2009). Water vapor.
It is likewise recommended that PyCO2SYS should be installed with pip as described in the documentation (see link in Table  4 ). PyCO2SYS has been described in detail by  Humphreys et al. (2022). In the new Excel version of CO2SYS (Table  4 ;  Pierrot et al. , 2021) , the pair of seawater CO 2 variables (columns J through O: DIC, TA, pCO 2 , f CO 2 , pH, and [CO 3 2-]) that will be used for the calculation can be indicated by clicking their corresponding header rows (Row #3), which will highlight the selected cells. The latest version of the R package seacarb  (Gattuso et al. , 2021)  includes other functions useful for ocean acidification research, e. g. , the ability to calculate pH from spectrophotometric measurements of absorbance ratios. "ScarFace" is a Shiny web application that has been developed to facilitate the use of seacarb via a userfriendly interface rather than with a command-line interface  (Raitzsch and Gattuso, 2020).
BEST PRACTICES APPROACH The creation of this methodology document was motivated by feedback from users of NOAA/NCEI's Ocean Carbon and Acidification Data System (OCADS) 3 in terms of the WOCE Exchange format. It benefited from OCADS' commitment to funnel the scientific expertise of the research community to the data management community. The key to the success of this truly community effort was the assemblage of the group of experts who knew this topic well, and who would also be users of this document. The group was composed of (a) some of the most visionary researchers in this field, (b) experts on the measurement of these oceanographic parameters, (c) experts on oceanographic data quality control and product development, (d) experts on the related calculation tools, (e) experts on the new TEOS-10 system, and (f) experts on oceanographic data management.
ACKNOWLEDGMENTS The column headers for discrete chemical oceanographic data are a refinement and update to the Exchange format of the  World Ocean Circulation Experiment  ( WOCE) Hydrographic Program  with updates and refinements by the  Climate and Ocean-Variability, Predictability, and Change (CLIVAR)  and the  Carbon Hydrographic Data Office (CCHDO) of the Scripps Institution of Oceanography. We thank  Dwight Gledhill  ( NOAA Ocean Acidification Program ,  Silver Spring, MD ,  United States ) and  Kim Yates  ( St. Petersburg Coastal  and  Marine Science Center ,  St. Petersburg ,  FL, United States ) for reading a previous version of the manuscript and offering comments that helped improve the manuscript significantly, and  James Orr  ( Laboratoire des Sciences du Climat et l'Environnement, France ) for key contributions to the R package seacarb and for contributing to the discussion about the best equations to estimate boronsalinity ratio.
FUNDING Funding for L-QJ and AK was from  NOAA Ocean Acidification Program  (OAP, Project ID:  21047 ) and  NOAA National Centers for Environmental Information (NCEI)  through  NOAA  grant  NA19NES4320002  [ Cooperative Institute for Satellite Earth System Studies (CISESS )] at the  University of Maryland/ESSIC. BT was in part supported by the  Australia's Integrated Marine Observing System (IMOS) , enabled through the  National Collaborative Research Infrastructure Strategy (NCRIS). AD was supported in part by the  United States National Science Foundation. AV and FP were supported by  BOCATS2  Project ( PID2019-104279GB-C21/AEI/10. 13039/501100011033 ) funded by the  Spanish Research Agency  and contributing to WATER:iOS  CSIC  interdisciplinary thematic platform. MH was partly funded by the  European Union 's  Horizon 2020 Research and Innovation Program  under grant agreement N • 821001 (SO-CHIC).
Observation Data IXPE observations of 4U 1626-67 were taken starting on 2022 March 24 beginning at 01:51 UTC and ending on 2022 March 27 at 05:39 UTC. Nearly simultaneous observations with other X-ray telescopes along with the IXPE observations are summarized in Table  1. Operational constraints limited the ability of NICER and Chandra to observe 4U 1626-67 simultaneously with IXPE. Given that 4U 1626-67 had not been previously observed to exhibit large variability in the X-rays on approximate timescales of days, we did not anticipate this offset in time to introduce any large systematic errors in our joint spectral analysis. Light-curve analysis for each of these observations also shows no evidence for significant variability in 4U 1626-67's emission. Original content from this work may be used under the terms of the Creative Commons Attribution 4.
2022). The output of this pipeline processing is an event file for each of the three IXPE DUs that contains, in addition to the typical information of spatially resolved X-ray astronomy, event-specific angles useful for determining the linear polarization of the radiation. Analysis of the processed IXPE event lists was carried out with several independent analysis tools, most notably ixpeobssim, which is a software suite designed specifically to operate with both simulated and real IXPE data, and  Baldini et al. (2022)  describe these algorithms in detail. In particular, we made use of the canned routines to do data filtering (xpselect), generate polarization cubes (xpbin with the PCUBE algorithm), and extract polarization parameters (xpbinview). In this work, we make use of version 25. 6.