dataesr/scientific-paragraphs-categorization huggingface dataset:
The latest global biogeographic scheme for marine ostracods was in the 1980s based on expert knowledge without quantitative data or analyses  (Whatley, 1986; Titterton and Whatley, 1988). A much-needed updated global ostracod biogeography scheme would be greatly facilitated by global database developments  (Yasuhara et al. , 2019). PBDB is the only occurrence database dedicated to integrating and curating deep-time fossil data. It is a resource for quantitatively estimating Phanerozoic-through ostracod diversity. Indeed, ostracods have a fair number of occurrences throughout the Ordovician to Present in PBDB (Fig. 6 ). Sepkoski (2000)  was the first to utilize PBDB for estimating Crustacea s. l. diversity, recognizing ostracods as the single dominant component of crustacean fossil records throughout the Phanerozoic.
Non-marine ostracod database applications The combination of species occurrence records with geographical coordinates in non-marine distributional databases has high potential value as a research tool, for example in biogeographic studies of endemism and cosmopolitanism. This rich area of research holds many unanswered questions for which temporal and spatial biogeographic data are critical. Such datasets facilitate hypothesis-testing and can reveal interesting ecological as well as taxonomic insights. Calibrations of the temperature ranges of species, for use in Quaternary paleoclimatic reconstructions, are achieved by matching climate data to geographical occurrences in NODE, NANODe and DOAD (see, e. g. ,  Horne et al. , 2012; Marchegiano et al. , 2020). Calibrations can be improved, moreover, by combining data from geographically separated regions in order to obtain more complete coverage of the climatic distributions of species.
Conclusions In the rising trend of database research, it has become clear that occurrence and measurement data have more impact in science if they are digitalized, tabulated, and integrated. Even though they are far from comprehensive, ostracod data in many databases have demonstrated its unique potential in advancing our understanding of large-scale biodiversity and evolution in the future. They cover a wide range of ecosystems and are an important representative of Crustacea s. l. almost throughout the Phanerozoic in the fossil records. Therefore, we strongly recommend ostracodologists publish georeferenced data with suitable databases after they published their studies on taxonomy, ecology, biogeography, and evolution. Contributing ostracod data to databases can not only strongly impact paleontological and biodiversity research, but also add value to personal efforts and public funds.
Consistent and accurate taxonomy is of key importance to large-scale biodiversity research, while global databases are integrating an increasing number of datasets. Aphia's well-developed taxonomic system is the taxonomic backbone of many distributional databases. PBDB has an algorithm-powered taxonomic classification dynamically linked to its own occurrence data. Collaborating through Aphia and PBDB can be an effective way for ostracodologists to achieve the consensus opinions and directly improve the taxonomic quality of mainstream databases in the future.
Acknowledgements We would like to thank  Robin J. Smith  for the data of the East Asian Non-marine Ostracod Database;  Renate Matzke-Karasz  for the updated information on Kempf Database Ostracoda;  Laura Gemery  and  Thomas Cronin  for the updated information on Arctic Ostracode Database; two anonymous reviewers and two editors,  Richard Jordan  and  Gene Hunt , for valuable comments. The NODE database was supported by  Human Capital & Mobility Programme, European Union  funding [grant number  ERBCHRXCT930253 ];  Marie Curie Research and Training Network, European Union  funding [grant number  FP6-512492 ];  Contingency Fund award, European Union  funding. NANODe and AJS were supported by the  U. S. Geological Survey ;  Kent State University ;  National Science Foundation. AJS's work in Neotoma database was supported by  National Science Foundation  [grant numbers  0947459 , 1550721, 1948297];  NSF EarthCube program  [grant number  1540994 ].
HHMH and MY are partly supported by  Research Grants Council, Hong Kong Special Administrative Region, China  [grant numbers  HKU 17300821 ,  HKU 17300720 ,  HKU 17302518 ,  HKU 17311316 ]; the  Seed Funding Programme for Basic Research , the  University of Hong Kong  [grant numbers 202011159122, 201811159076, 201711159057, 201611159053];  Faculty of Science RAE Improvement Fund , the  University of Hong Kong ;  SKLMP Seed Collaborative Research Fund  (grant number  SKLMP/SCRF/0031 ),  City University of Hong Kong ; Seed Funding of the  HKU-TCL Joint Research Centre for Artificial Intelligence. HHMH was supported by  Ecology and Biodiversity Division Fund , the  University of Hong Kong ;  Peter Buck Postdoc Fellowship ,  Smithsonian Institution. SNB's work on the World Ostracoda Database was/is funded by a  Lifewatch Mini Grant  and  UESC [Projeto Biodiversidade de Ostracoda Marinho do Sul da Bahia ].
, 2008; Martens et al. , 2013) Arctic Ostracode Database (AOD) Occurrence-based (paleo) biodiversity database √ Modern to the Quaternary NOAA's NCEI, OBIS (Cronin et al. , 1995; Gemery et al. , 2017; Cronin et al. , 2021) Ocean Biogeographic Information System (OBIS) Occurrence-based data harvester/ recombiner √ Modern to the Quaternary GBIF, PANGAEA, ANTABIF (OBIS, 2021) PANGAEA Data archive/repository √ Modern to the Quaternary OBIS, GBIF (PANGAEA®, 2021) Antarctic Biodiversity Information Facility (ANTABIF) Data archive/repository √ Modern/Recent Antarctic Biodiversity Portal, OBIS (biodiversity. aq, 2021) Atlas of Atlantic Planktonic Ostracods Outputs generated from an occurrence-based (paleo) biodiversity database √ Modern/Recent Natural History Museum London, OBIS (Angel et al. , 2008) An Atlas of Southern Ocean planktonic ostracods Outputs generated from an occurrence-based (paleo) biodiversity database √ Modern/Recent OBIS (Blachowiak-Samolyk and Angel, 2008) Non-marine Ostracod Distribution in Europe (NODE) Occurrence-based (paleo) biodiversity database √ Modern to the Quaternary OMEGA (Horne et al.
, 2011) North American Non-marine Ostracode Database (NANODe) Occurrence-based (paleo) biodiversity database √ Modern/Recent Neotoma Database, OMEGA, NACODe (Smith et al. , 2015) Delorme Ostracode Autecological Database (DOAD) Occurrence-based (paleo) biodiversity database √ Modern/Recent Neotoma Database, OMEGA, NACODe (Curry et al. , 2012) East Asia Non-marine Ostracod Database (EANODe) Occurrence-based (paleo) biodiversity database √ Modern/Recent OMEGA Unpublished compilation Ostracod Metadatabase of Environmental and Geographical Attributes Occurrence-based (paleo) biodiversity database √ Modern/Recent Freshwater Information Platform, BioFresh, GBIF (Horne et al. , 2011) (OMEGA) Neotoma Database Occurrence-based (paleo) biodiversity database √ Modern to Pliocene Earth Life Consortium, NCEI (Williams et al.
, 2018) North American Combined Ostracode Database (NACODe) Occurrence-based (paleo) biodiversity database √ Modern/Recent - (Curry et al. , 2012) Paleobiology Database (PBDB) Occurrence-based (paleo) biodiversity database √ √ Phanerozoic Earth Life Consortium, ePANDDA API, GBIF (Peters and McClennen, 2016) Global Biodiversity Information Facility (GBIF) Data harvester/recombiner √ √ Living and fossil OBIS, Paleobiology Database, Biofresh, etc. (GBIF, 2021).
An exhibition "Ecce Homo" dedicated to the figure of Metropolitan Andrey was prepared for the opening of the Sheptytsky Centre, which can now be viewed in a multimedia format. 84 The new location takes into account all the latest requirements for book storage and comfortable workplaces for readers. In particular, a book security system has been introduced thanks to RFID-tags (Radio-frequency identification) and special gates at the entrance. These labels allow the library not only to control unauthorised removal of books from the premises, but also to conduct inventory processes of the collection. Plans for the future include the introduction of self-service stations, for which the presence of RFID-tags is also important. The Library of UCU follows the trends in the development of university libraries in the world and explores the possibilities of attracting ebooks and databases.
Electronic databases, in particular theological ones, such as the ATLA Religion Database, are being actively tested. The quarantine associated with the COVID-19 pandemic has been a major challenge for the Library of UCU, but has also led to positive library progress in establishing remote access to electronic resources via VPN. The Library of UCU is continuing to develop the collection, while also taking into account the emergence of new faculties and curricula at the university. Despite the innovations, the Library of UCU duly appreciates and continues to develop its unique treasure, a theological collection of literature, carefully collected and organised. It is one of the most complete collections of theological literature in Ukraine in various different languages and continues to be a magnet not only for our students and teachers of the Philosophy and Theology Faculty of UCU, but also for external readers.
Cryo-EM data acquisition The purified spike proteins (0. 7mg/ml of the trimer) and each antibody were mixed with a molar ratio of 1:9, and incubated at 4°C for 30 min. The mixture (3 μl) was applied onto a freshly glowdischarged holey carbon grid, coated with a single or two layers of graphene oxide, then blotted for 3 s at 4°C in 100% humidity and plunge-frozen in liquid ethane using the Vitrobot Mark IV system. Cryo-EM imaging of the complex was performed on a 300 kV Titan Krios G4 transmission electron microscope equipped with a BioQuantum K3 direct electron detector in the electron counting mode. The imaging was performed at a nominal magnification of ×105,000, corresponding to a calibrated pixel size of 0. 829 Å/pixel. Each micrographic movie was recorded for a total of 15. 5 electrons per pixel per second for 2.
Image processing Image processing was mainly performed with the RELION-3. 1. 2 package  (Zivanov et al. , 2018). All of the dose-fractionated movies were subjected to beam-induced motion correction using MotionCor2  (Zheng et al. , 2017) , and the contrast transfer function (CTF) parameters were estimated using CTFFIND4  (Rohou and Grigorieff, 2015). Particles were picked using crYOLO  (Wagner et al. , 2019)  from the micrographs and subjected to several rounds of 2D and 3D classifications to select good particles, and subsequently subjected to 3D refinement and Bayesian polishing. In the last cycle, particles were refined with a specific mask (imposing C3 symmetry on some complexes), yielding an overall map with a global resolution of 3. 6 to 2. 4 Å. To further improve the density of Fab moieties, the particles were subjected to focused 3D classification using a soft mask encompassing the RBD and variable domains of Fabs.
Model building and validation The trimeric structures of the spike protein with each Fab were manually fitted and constructed based on a structure (PDB ID: 6VXX) as an initial model, using Coot  (Emsley et al. , 2010). The template model of the variable domain was automatically built by the SWISS-MODEL server  (Waterhouse et al. , 2018) , based on selected sequences with higher homology, and was manually fit in Coot  (Emsley et al. , 2010). In the spike moiety, several prominent densities were identified near Asparagine residues, which corresponded to N-glycosylation. Glucosamines (NAG) were built depending on each density size. The model of the entire structure was iteratively refined using phenix. real_space_refine of the Phenix package  (Liebschner et al. , 2019) , with secondary structure restrains.
) Referring to the GlyConnect database  (Mariethoz et al. , 2018) , possible glycan forms were listed (Supplementary Figure  2A ). The traceable glycan model was reasonably placed near the Asn59 along the electron density, and fucose is identified, which is linked α-1,6 to the reducing terminal of β-N-acetylglucosamine, as shown in the Supplementary Figure  2BC.
Acknowledgments We thank  Yukari Kaneda ,  Mami Yamada ,  Mayumi Yonemochi ,  Mariko Ikeda ,  Mio Inoue ,  Naoko Kitagawa , and  Hideaki Ishida  for helping with the experiments, and  Ikuo Kawamoto ,  Takahiro Nakagawa ,  Hideaki Tsuchiya , and  Iori Itagaki  for animal care. We also thank  Dr. Takehiro Suzuki  ( Biomolecular Characterization Unit, RIKEN CSRS ) for glycosylation analysis by mass spectrometry. The cryo-EM experiments were performed at the cryo-EM facility of the  RIKEN Center for Biosystems Dynamics Research Yokohama. This study was supported by the  Collaborative Research Resources, School of Medicine, Keio University , and was funded by a research grant for COVID-19 in  Keio University School of Medicine  ( Donner Research Project Grant ); by  AMED  under Grant Numbers JP20fk0108283,  JP21ym0126022 , and  JP21fk0108468 ; by the  RIKEN President 's discretionary funds; and by the  Mitsubishi Foundation.
) and establishes contextual and contrastive modeling. It is, therefore, well suited to the challenges of discourse analysis that I have highlighted. For this paper, I used the Iramuteq software, which offers a set of analysis procedures for the description of a textual corpus (Iramuteq is downloadable at http://www. iramuteq. org, (accessed on 20 January 2022) and comes with extensive documentation and case studies. More precisely, Iramuteq is an interface for R, which can be used to make multidimensional analyses of texts and questionnaires). One of its principal methods is Alceste. This allows a user to segment a corpus into "context units", to make comparisons and groupings of the segmented corpus according to the lexemes contained within it, and then to seek "stable distributions"  [11].
To complete this, and to go more specifically to the context which interests us, it is also worth taking into account the work of  [15] , which shows that, regardless of the challenge to the notion of linear scientific progress, there is no doubt that scientific knowledge is growing fast; however, whilst "the process of accumulation taking place in scientific disciplines is widely studied. the way in which information accumulates and knowledge grows in nonscientific areas is less known". In her book Science Communication Online: Engaging Experts and Publics on the Internet,  [16]  looks "specifically to microblogs, Wikipedia, and an online database of radiation contamination readings as emerging forms of science communication online". She expands the work of  [13]  by putting forward the notion of "parascientific genres".
Media Articles about Didier Raoult Using the Iramuteq software, I applied the Alceste method-presented in Section 2and obtained the dendrogram presented in Figure  1 : These classes were, therefore, derived from a statistical classification which brought together terms that were close to each other. Looking closely at the terms grouped in the various classes and reading the text segments that led to these groupings, we chose to focus on three particular classes (which correspond to clusters of terms that refer to actual topics, rather than communicational or contextual aspects): -Class 1 (14. 9% of text segments), which contains texts about the efficacy, effects, and results of hydroxychloroquine, the drug advocated by Prof. Raoult; -Class 2 (13. 4% of text segments), which discusses the scientific validation (in journals such as The Lancet) of the study and whether or not the drug should be authorized for treating patients; -Class 5 (19.
e. calculation after the individual retrievals). We show that this approach has strong theoretical similarities to applying the spectra of the different sensors together in a single retrieval procedure but with the substantial advantage of being applicable to data generated with different individual retrieval processors, of being very time efficient, and of directly benefiting from the high quality and most recent improvements of the individual retrieval processors. We demonstrate the method exemplarily for atmospheric methane (CH 4 ). We perform a theoretical evaluation and show that the a posteriori combination method yields a totalcolumn-averaged CH 4 product (XCH 4 ) that conserves the good sensitivity of the corresponding TROPOMI product while merging it with the high-quality upper tropospherelower stratosphere (UTLS) CH 4 partial-column information of the corresponding IASI product.
As a consequence, the combined product offers additional sensitivity for the tropospheric CH 4 partial column, which is not provided by the individual TROPOMI nor the individual IASI product. The theoretically predicted synergetic effect is verified by comparisons to CH 4 reference data obtained from collocated XCH 4 measurements at 14 globally distributed TCCON (Total Carbon Column Observing Network) stations, CH 4 profile measurements made by 36 individual AirCore soundings, and tropospheric CH 4 data derived from continuous groundbased in situ observations made at two nearby Global Atmospheric Watch (GAW) mountain stations. The comparisons clearly demonstrate that the combined product can reliably detect the actual variations of atmospheric XCH 4 , CH 4 in the UTLS, and CH 4 in the troposphere.
continuous collaborative retrieval developments are required. While this is certainly possible, it might not be the most efficient way, in particular considering the steadily increasing number of available satellite data products (level 2 retrieval products). The optimal synergetic exploitation of the level 2 retrieval products that are already available would be much less computationally expensive than running dedicated multispectral retrievals. Such synergetic combination of level 2 products is the topic of this paper. There are already several examples of a level 2 product fusion discussed in literature (the following list is not intended to be complete):  Worden et al. (2015)  combine the thermal and near-infrared level 2 products of methane (CH 4 ) of TES and GOSAT (Greenhouse gas mOnitoring SATellite), respectively, by performing approximative calculations and with a focus on monthly mean data.
Here, we present a method for fusing the available level 2 CH 4 profile product of IASI and the XCH 4 (total-columnaveraged methane) product of TROPOMI (Tropospheric Monitoring Instrument) by means of a Kalman filter approach. Our objective is a data product with improved vertical profile information (determine tropospheric CH 4 independently from CH 4 at higher altitudes, which is not possible by IASI or TROPOMI data alone) by synergetically exploiting the different vertical sensitivities of the two products. The method allows for a computationally very efficient generation of global daily maps of the combined data product and only needs the individually retrieved states, averaging kernels, and noise covariances provided by the respective remote-sensing experts in the context of their standard retrieval work.
The proposed method can be used flexibly for combining measurement information of different satellite sensors and is in particular interesting for combining a profile product with a total-column product. The method has strong theoretical similarities to a dedicated combined optimal estimation retrieval that uses the combined IASI and TROPOMI spectra as input (synergetic use of level 1 data). The reliable and global detection of tropospheric CH 4 independently from CH 4 at higher altitudes can lead to an improved understanding of the CH 4 cycle. Respective data allow for a more direct investigation of the CH 4 boundary layer source and sink signals than total-column-averaged mixing ratios (XCH 4 ) provided globally for instance by GOSAT (e. g. Parker et al. , 2020)  or TROPOMI  (Lorente et al.
, 2021a). This is because XCH 4 signals are strongly affected by vertical shifts of the tropopause altitude; i. e. their use for investigating CH 4 absorption and release at the ground depends on the correct consideration of the tropopause altitude by model simulations  (Pandey et al. , 2016). This paper is organized as follows. Section 2 briefly presents the used IASI and TROPOMI products (generated by two individual retrievals). Section 3 presents the equations for the optimal a posteriori combination of the two independent retrieval outputs (level 2 product combination) and performs a theoretical evaluation of the individual and combined products. Section 4 validates the total-column and tropospheric and UTLS (upper troposphere-lower stratosphere) partial-column products obtained by the individual IASI and TROPOMI retrievals and by the a posteriori combination by an inter-comparison to reference data from the Total Carbon Column Observing Network (TCCON), AirCore, and Global Atmospheric Watch (GAW).
Section 5 discusses the global consistency of the products and shows global maps. Section 6 resumes the results of our study and briefly discusses upcoming possibilities. Furthermore, in Appendix A we give a brief overview on retrieval theory, and in Appendix B we discuss the theory of our a posteriori combination method and show that the method has strong similarities to performing a full multispectral optimal estimation retrieval. Appendix C introduces the operator for transferring logarithmic-scale differentials into linear-scale differentials. Appendix D presents the operators used for converting vertical profile data into total-and partial-column data. Appendix E examines the dislocation error, i. e. to what extent the temporal and spatial dislocation of the IASI and TROPOMI observations (the two sensors are on two different satellites with different orbits) impacts the combined data product.
Satellite data In this section we briefly present the satellite data products that are used for the combination procedure. These are the XCH 4 data obtained from the analysis of the near-infrared and shortwave infrared (SWIR and NIR) spectra measured by TROPOMI and the CH 4 profiles derived from IASI thermal nadir (TIR) spectra. In addition, we explain the criteria used for collocating the two satellite observations.
MUSICA IASI CH 4 profiles As the IASI CH 4 data product, we use the data generated by the retrieval processor MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water, a European Research Council project between 2011 and 2016). The MUSICA IASI data full retrieval product encompasses trace gas profiles of H 2 O, the HDO/H 2 O ratio, N 2 O, CH 4 , and HNO 3. The data have been validated in several previous studies  (Schneider et al. , 2016; Borger et al. , 2018; García et al. , 2018) , and it has been shown that the CH 4 product can detect the CH 4 signals originating in the upper troposphere-lower stratosphere particularly well. MUSICA IASI data using processor versions 3. 2. 1 and 3. 3. 0/1 are currently available for the 2014 to 2021 period and are presented in  Schneider et al.
(2022). This MUSICA IASI data set is best suited for a posteriori data reusage (e. g. Diekmann et al. , 2021)  because in addition to the retrieved trace gas profiles, it contains full information on retrieval settings (a priori states and constraints) and on averaging kernel and error covariance matrices. In order to ensure the highest MUSICA IASI data quality, here we require the flag variable musica_fit_quality_flag to be set to 3 (the spectral fit of the MUSICA IASI retrieval has a good quality, and the spectral residuals are close to the instrumental noise level). Furthermore, we only use MUSICA IASI data for which the flag variable eumetsat_cloud_summary_flag is set to 1, which guarantees that the IASI instrumental field of view is cloudfree. A particularity of the MUSICA IASI processor is that the trace gas inversions are performed on a logarithmic scale.