dataesr/scientific-paragraphs-categorization huggingface dataset:
Oceanography data QA3 includes oceanography data to 40 o S, so that the Subantarctic Ocean Front and even most of the Subtropical Ocean Front are included. Most oceanography data layers are taken from the World Ocean Atlas 2013 (WOA), including temperature  (Locarnini et al. , 2013) , salinity  (Zweng et al. , 2013) , and concentrations of oxygen  (Garcia et al. , 2014a) , silicate, phosphate, and nitrate  (Garcia et al. , 2014b)  at the surface and three depths  (50, 200, and 500 m) , both in the summer and winter (Fig. 1 ). All WOA data layers are gridded every 25 km. Other oceanography datasets include five ocean fronts (Southern Antarctic Circumpolar Current Front, Southern Boundary of the Antarctic Circumpolar Current, Polar Front, Subantarctic Front, and Subtropical Front), and a grid of mean surface current speed with the 16-km resolution that is obtained using observation-data assimilation techniques  (Mazloff et al.
Social sciences data Data here include human footprints in Antarctica since the early stage of expeditions. Routes and tracks of six historic Antarctic expeditions are presented, including the first circumnavigation of Antarctica by Fabian Gottlieb von Bellingshausen in 1819-1821, tracks to the South Pole by Roald Amundsen and Robert Falcon Scott in early 1910s, and the first trans-Antarctic flight by Lincoln Ellsworth in 1935  (Dater, 1975). Historic stations  (Headland, 2009)  as well as historic sites and monuments included in ADD are presented. These include 103 stations on the continent and 36 more on surrounding islands developed by 24 nations between the first International Polar Year 1882-1883 and its fourth one in 2007-2009. Human activities in Antarctica have increased dramatically in recent decades through research, expeditions, and tourism.
User applications Quantarctica is a multidisciplinary knowledge base for Antarctica, the Southern Ocean, and sub-Antarctic islands. It has given researchers, students, educators, and logistics operators a single, easy-to-use platform to view, analyse, and synthesize Antarctic datasets  (Perkel, 2018). QA3 is distributed with the CC-BY4. 0 license, so users can develop their own GIS environment using QA as its basis, and visually present their own data with QA as the graphical basis in many scientific publications over various disciplines, such as Quaternary science  (Andersen et al. , 2020) , oceanography  (Schiaparelli and Aliani, 2019) , behavioural ecology  (Schiaparelli and Aliani, 2019) , and microorganisms  (Hirose et al. , 2020). Cautions are needed, however, because some datasets have different licenses, terms of use, and attribution requirements, which are documented in the metadata.
QA can be freely distributed and thus it is hard to know the actual number of users, but we know of users from all SCAR member countries, including those currently in the early stages of developing full-scale Antarctic programs. The SCAR Expert Group on Antarctic Biodiversity Informatics has developed a R package quan-tarcticR (https://github. com/SCAR/quantarcticR) that provides access to QA datasets for R users, without needing QGIS to be installed. With the success of QA, a similar GIS data package is being developed for the Greenland Ice Sheet (QGreenland, https://qgreenland. org/). Many users import non-QA datasets to develop their own GIS workspaces based on QA. QGIS provides numerous tools and plugins to help analyse QA datasets. The identify features tool provides detailed information on selected vector data points (e.
g. , red profile in Fig. 2b ) or cell values of selected raster layers. The attribute tables list information that can be sorted or filtered by field properties. Some commonly used analytical tools include polygonization of raster/vector datasets, contour extractions, terrain (slope and aspect) analysis, and raster calculation using multiple scientific datasets and terrain models, as well as interpolation, smoothing, and merging of multiple raster datasets (e. g. , continental data in QA and user's own data in a smaller region). The profile tool displays the transect of raster values along custom line segments (Fig. 3a ). QGIS also allows 3D viewing (Fig. 3b ). Selected datasets can be draped over three-dimensional representations of a terrain model with various angles and vertical exaggerations.
QGIS has many other tools and plugins. Due to its modest computational requirements, QA can be easily used on laptops during Antarctic fieldwork. Before deploying to the field, QA's data layers, satellite imagery and basemaps can be used for the first-order safety assessments. In addition, users can import recent satellite imagery and analyse them on the QA workspace to define hazardous crevasse areas and design traverse routes (Fig. 3c ). QGIS also works to plot the user's current location using a handheld GPS receiver. This capability increases safety and speed during field traverses. During fieldwork, researchers can gain a deeper understanding of a region by displaying QA's included package of scientific data and satellite imagery.
Quantarctica-friendly datasets The number of data layers and the data volume in the QA package are limited to those with a large spatial coverage and useful for multidisciplinary users, so that users can easily browse data outside of their own disciplines. However, individual users working in a specific region or specific discipline need more detailed data. To improve user experience and promote data sharing, we encourage data providers to follow our recommendations and mandatory guidelines (ftp://ftp. quantarctica. npolar. no/Quantarctica3/Making%20Quantarctica-friendly%20datasets. pdf). Those datasets that meet these criteria, called Quantarcticafriendly datasets, are hosted by the data providers, and download links and short descriptions are listed at QA's project web site (https: //www.
npolar. no/quantarctica/). Mandatory guidelines are file formats (ESRI shapefile for vector and GeoTIFF, or if needed JPEG2000 or JPEG, for raster), layer style file (. QML format), metadata file (. QMD format and TXT file with only the meta data abstract for convenience and clarity), and projection (EPSG:3031). Also, we recommend the following: (1) specific layer styles and labelling to visualize the data compatible with QA's datasets, (2) image pyramids of large raster data for faster rendering, and (3) raster data compression, preferably using lossless LZW or Deflate compression, but also if needed using lossy compression such as JPEG2000 for large-volume data. Datasets currently listed as Quantarctica-friendly datasets include region-specific bed topography data in the Weddell Sea  (Jeofry et al.
QGIS functionality in polar regions In our development of QA, we contributed to the development of the following three functions of QGIS that benefit QA users as well as QGIS users in the Arctic. (1) Print Layout can now display the north arrow pointing to true north (not to the grid north) in polar coordinate systems (https://www. qgis. org/en/site/forusers/visualchangelog218/index. html#feature-true-north-arrows). This is particularly useful in maps of small regions. (2) QGIS can now render, select, and edit on-the-fly projected vector data when on-the-fly reprojection is enabled for EPSG:3031 and other polar stereographic projections (https://issues. qgis. org/issues/7596#change-64937). This problem occurred when users loaded their own data not projected to EPSG:3031 to QA's workplace as a test before re-projecting their data to EPSG:3031.
Outlook The scientific disciplines included in QA are not comprehensive. For example, data for upper atmosphere research and astronomy are absent in the current version, as are Antarctic Ice Sheet mass balance estimates and trends. As new datasets with higher precision and resolution become available, adding new datasets and replacing obsolete datasets are necessary to provide a state-of-the-art knowledge base to the entire Antarctic and Southern Ocean community. We seek to maintain QA as a balanced product in terms of portability, coverage, and user friendliness.
Conclusions Quantarctica (https://www. npolar. no/quantarctica) is an integrated mapping environment for Antarctica, the Southern Ocean, and sub-Antarctic islands, freely available with the CC-BY4. 0 license with DOI: https://doi. org/10. 21334/npolar. 2018. 8516e961. Quantarctica works with QGIS software version 3. 16 on multiple platforms without an internet connection. It is composed of 265 data layers in simple and detailed basemaps, satellite imagery, terrain models, and scientific data in nine disciplines of atmosphere, biology, environment management, geology, geophysics, glaciology and ice cores, oceanography, sea ice and social science, giving the total data size of 7. 53 GB. We will maintain Quantarctica by adding new datasets and replacing obsolete datasets to provide a state-of-the-art knowledge base in a balanced form in terms of portability, coverage, and user friendliness.
Acknowledgements Development of Quantarctica version 3 was funded by the  Norwegian Ministry of Foreign Affairs  (grant number:  QZA-15/0332 ). Quantarctica became highly visible and widely used in the Antarctic community, thanks to SCAR's endorsement of QA as a SCAR product and promotions made by  Association of Polar Early Career Scientists (APECS) , and  SCAR 's scientific research programs, action groups and expert groups, as well as SCADM and SCAGI. The  Norwegian Environment Agency, Tasmanian Partnership for Advanced Computing, Arctic  and Antarctic Data Archive System at the  National Institute of Polar Research, National Center for Polar and Ocean Research , and Polar Geospatial Center provide distributed global download capabilities. The RADARSAT-2 image used in Fig. 3c provided by  NSC/KSAT  under the  Norwegian-Canadian RADARSAT agreement.
quantarctica/),  India's National Center for Polar and Ocean Research  (ftp://ftp. ncaor. gov. in/), and the  Polar Geospatial Center at the University of Minnesota  (ftp://ftp. data. pgc. umn. edu/gis/packages/quantarctica/). All download sites and Quantarctica-friendly datasets (see Section 4. 2) are listed at  Quantarctica 's project web page at the  Norwegian Polar Institute : https://www. npolar. no/quantarctica.
Data Collection An invitation to participate in this study was sent on November 13, 2018, to co-authors of the E30-2018 study (2) through the European Centre for Disease Prevention and Control (ECDC) Epidemic Intelligence Information System Vaccine-Preventable Diseases platform (https://www. ecdc. europa. eu/en/publications-data/ epidemic-intelligence-information-system-epis), and to members of the European Non-Polio Enterovirus Network (ENPEN; https://www. escv. eu/enpen). We requested pseudonymized data from 2016-2018 with sample identifi er, sampling date, specimen type, and sequence in FASTA be sent to ECDC secure fi le transfer protocol server by January 7, 2019. We also collected optional data, such as patient age, clinical presentation, whether they were hospitalized, and infection outcome.
Sequence Data Collection We requested that the FASTA sequence data contain the VP1 gene and collected 1,784 records (Appendix Figure  1 ). Sequences were obtained from enteroviruspositive samples by using 5′ UTR PCR  (23)  and typed within the VP1 gene by using Sanger sequencing  (2, 24). We excluded sequences with indicators of poor sequence quality, such as >2 ambiguous or undefi ned bases, in-frame stop codons, identical to reference E30 strains; sequences of the wrong type, such as E3; or sequences shorter than 200 basepairs or spanning a non-VP1 region. In total, we had 1,407 study sequences that comprised 2 nonoverlapping regions, 1,262 sequences from region 1 (nt positions 2543-2902, according to the prototype E30 strain Bastianni, GenBank accession no. AF311938) and 145 sequences from region 2 (nt positions 2916-3428).
Next-Generation Sequencing Stool suspensions and CSF samples were processed to remove as much nonviral material as possible by using centrifugation, filtration, and endonuclease treatment. RNA was extracted by using the MagNAPure 96 (Roche Diagnostics, https://www. roche. com) automated extraction kit or QIAGEN filters and eluted in 50 µL of elution buffer (Appendix). Complementary DNA (cDNA) and double stranded DNA (dsDNA) were generated and purified (Appendix). For tagmentation and library preparation, the Nextera XT DNA Library Preparation Kit (Ilumina, https://www. illumina. com) was used according to the manufacturer's instructions. Runs were performed on the Nextseq (Ilumina). Raw data were processed by using Jovian (D. Schmitz et al. , unpub. data, https://github. com/ DennisSchmitz/Jovian) (Appendix).
Nucleotide Sequences and Phylogenetic Analysis We conducted VP1 phylogenetic reconstruction with the 1,407 study sequences and 324 sequences extracted from Genbank. We selected region 1 for clade analysis because it is more commonly used for enterovirus typing  (24). We performed analysis of region 2 sequences based on sequence clustering, in which both region 1 and 2 were available, such as fulllength sequences or sequences spanning the entire VP1 gene. Sequencing of the 540 nt 3Dpol gene, positions 5825-6364, also was provided for 12 samples with region 1 sequences (Appendix Figure  1 ). Sanger sequencing indicated that samples did not display double infection and that VP1 and 3Dpol were from 1 virus. Complete genomes (≈7. 3 kb) were generated for 48 sequences by using NGS. To compare 3Dpol groupings within EV species B, we downloaded all sequences available from GenBank as of October 18, 2019, that were >70% complete between positions 5825-6364 with <6 ambiguous base positions and <6 undetermined bases and without stop codons.
We aligned the downloaded sequences with complete genomes or 3Dpol sequences from our study. We aligned data by using sequence editor SSE version 1. 3 (http://www. virus-evolution. org). We generated maximum-likelihood and neighbor-joining trees for VP1 and 3Dpol regions by using MEGA version 7 (https://www. megasoftware. net) with the optimal model (general time reversible plus invariant sites plus gamma distribution for rates over sites) and 100 bootstraps  (25). We analyzed the species B dataset with neighbor-joining and maximum composite likelihood distances.
Nextstrain VP1 Phylodynamic Analysis The dataset used for Nextstrain phylodynamic analysis comprised 1,285 sequences; 1,215 study sequences (region 1) and 70 complete VP1 sequences extracted from GenBank (Appendix Figure  1 ). We excluded sequences shorter than 250 bp, sequences from samples collected before 1958, and sequences deemed as outliers during phylogenetic reconstruction. We deemed these outliers recombinants with possible recombination breakpoints within the sequence fragment used made phylogenetic reconstruction impossible. We aligned sequences by using MAFFT  (26). We inferred a phylogenetic tree by using IQ-TREE  (27)  and generated time-resolved trees by using TreeTime  (28)  by estimating the mutation rate. When available, we attached to sequences data on country, sample type, E30 clade, age groups, and clinical data, such as whether patients were hospitalized and their symptoms.
Nextstrain VP1:3Dpol Tanglegram Phylodynamics We used a dataset of 110 sequences to conduct 3Dpol analysis, including 48 complete genome sequences and 12 3Dpol sequences generated in this study and 50 sequences extracted from GenBank (Appendix Figure  1 ). We aligned 3Dpol sequences to the E30 reference sequence (GenBank accession no. MK238483) and inferred phylogenetic and time-resolved trees as we did for VP1, but we used a fixed clock rate of 4 × 10 -3 substitutions/site/year during the time-resolved tree reconstruction. We provided the resulting Nextstrain tanglegram build for viewing (https:// nextstrain. org/community/enterovirus-phylo/echo30-2019/3D:community/enterovirus-phylo/echo30-2019/ vp1) and the codes for both VP1 and 3Dpol analyses (https://github. com/enterovirus-phylo/echo30-2019).
Genbank and ENA Accession Numbers We deposited VP1 and complete genome sequences in GenBank under accession nos. KC309427-37, KY986976-7033, MK251835-6, MK372854-80, MK507733-7, MK814991-6288, and MK895104-9 and 3Dpol sequences under accession nos. MN395293-303. We deposited NGS fastq reads in European Nucleotide Archive database under accession nos. SAM17101211-58.
Fever, either as sole symptom or in combination with other signs and symptoms, was recorded in only 52 (7. 1%) cases. Unfortunately, not all records were filled in completely, and clinical data were absent for some samples. Other signs and symptoms mentioned were gastrointestinal symptoms in 6 cases, respiratory symptoms in 6, rash in 2, other neurologic symptoms in 4, or other unspecified in 53 cases; 8 cases had no symptoms. We created an interactive representation of age and clinical features of sequences from E30 cases, which we made available on Nextstrain (https://nextstrain. org/community/ enterovirus-phylo/echo30-2019/vp1). Hospitalization status was available for only 17. 6% (n = 228) of cases, only 5 of which had no hospitalization. The low fraction of hospitalization reported limited further analysis.
Recombination Analysis We used 110 sequences containing both VP1 and 3Dpol region and complete genome sequences to analyze recombination events between VP1 and the 3′ distal end of the E30 genome (Appendix Figure  1 ). The E30 3Dpol sequences formed a series of separate clusters interspersed with those of other species B types, indicative of many within-species recombination events during their diversification (Figure  4 ). We took the entire published sequence dataset and used a nucleotide sequence distance threshold of 8%, based on pairwise sequence comparisons, which divided sequences into distinct groups (Appendix Figure  3 ), comparable to those derived from a previous analysis of E30 RFs  (17). Accordingly, species B could be divided into ≈442 RFs, an indication of the frequency and complexity of recombination events occurring during the evolution of this species.
Discussion A large upsurge of E30 infections was reported in several countries in Europe during 2018  (2). We  conducted a comprehensive molecular characterization of E30 by using VP1, 3Dpol, and whole genome sequences. Our molecular characterization enabled an analysis of the recombination events occurring during E30 diversification in Europe, which can be conducted only when dealing with a single infection. Our study used a large EV sequence dataset collected worldwide, comprising 1,329 E30 sequences collected from 22 countries in Europe during 2016-2018 and was made possible due to the large-scale collaboration between countries through ENPEN and ECDC. The data clearly demonstrate that analysis based on phylogenetic clade assignment shows differential dominance of many different clades.
Unfortunately, G5 could not be inferred due to possible recombination events within the fragment. Complete reconstruction of E30 temporal events with geographic spread was hampered by the inevitably uneven sampling and testing in different years by the different contributing countries. This study underpins the strength of the ENPEN consortium, which brings together virologists, public health experts, infectious disease doctors, and scientists across Europe to enable rapid detection and early warning through standardized surveillance. Previous studies using Nextstrain with 2 EV-D68 datasets have shown the value of combining demographic and phylogenetic analysis, both as retrospective  (33)  and real-time analysis  (34). The E30 dataset and the 2 EV-D68 datasets  (33, 34)    time tracking of viruses over time and across countries.
Acknowledgments We thank all clinicians and technical staff participating in the European enterovirus/poliovirus surveillance programs in all participating laboratories. We also thank the following for echovirus diagnostics and sequencing:  Sanela Numanovic  ( Norwegian Institute of Public Health Oslo, Norway );  Maria Evangelidou  ( Hellenic Pasteur Institute, Athens, Greece );  Maria Takacs  ( National Public Health Center, Budapest, Hungary );  Elenor Hauzenberger  and  Anna-Lena Hansen  ( PHAS, Sweden );  Elena Pariani  and  Sandro Binda  ( University of Milan, Italy );  Darja Duh ,  Nika Volmajer  and  Katja Soršak  (National laboratory of health, environment and food, Centre for medical microbiology, Slovenia). The study was supported by the  Ministry of Health, Welfare and Sport , the  Netherlands  as part of the  EV surveillance program  of the  National Institute for Public Health  and the Environment; the  European Union   Horizon 2020 research and innovation program  ( COMPARE grant  no.
Native Aortic Root Model ITK-SNAP 3. 6 software (www. itksnap. org accessed on 20 February 2021) was utilized to identify the main anatomical features of the patient's aortic root from preoperative DICOM images and generate three-dimensional reconstructions. Following the segmentation procedure, an in-house developed Matlab code was used (v. R2018b, Mathworks Inc. , Natick, MA, USA) allowing the generation of a suitable mesh of the aortic wall, assuming a constant thickness of 2. 5 mm (for simplicity). Then, Rhinoceros 5. 0 commands (McNeel & associates, Seattle, WA, USA), were utilized to construct a definitive 3D CAD (Computer Aided Design) geometry of the aortic root. Volumetric reconstruction of the aortic root was exported to Abaqus for discretization using C3D4 tetrahedral elements.
Prosthetic Model Accurate geometrical models of the 26-mm CoreValve and Edwards Sapien 3 were obtained from high-resolution micro-CT images of the actual devices after their expansion. The CAD models of the stent frames were built using Rhinoceros 5. 0 and Matlab. Abaqus/Explicit was used to reproduce the crimping phases of both the devices into their catheters by gradually reducing the initial catheter diameters (42 mm and 27 mm) to the final ones, as follows: 6 mm and 4. 7 mm, for CoreValve and Sapien, respectively. Pericardial tissue leaflets were excluded because their effect on mechanical stents performance and interactions with the aortic root wall were negligible. With the aim to model the mechanics of a self-expandable valve, the gradual removal of the rigid catheter was performed post-compression, with an upwards sliding movement to enable stent reopening.
This regulator-supplied information was incorporated into MetFrag, an in silico identification tool replete with "post-relaunch" features used here. This study's unique regulatory context posed challenges in data quality and volume that were directly addressed with the prescreening, quality control, and identification workflow developed. Results: One confirmed and 21 tentative identifications were achieved, suggesting the presence of compounds as diverse as manufacturing reagents, adhesives, pesticides, and pharmaceuticals in the samples. More importantly, an in-depth interpretation of the results in the context of environmental regulation and actionable next steps are discussed. The prescreening and quality control workflow is openly accessible within the R package Shinyscreen, and adaptable to any (retrospective) analysis requiring automated quality control of mass spectra and non-target identification, with potential applications in environmental and metabolomics analyses.