dataesr/scientific-paragraphs-categorization huggingface dataset:
S12 ). Data was obtained from In Loco (mapabrasileirodacovid. inloco. com. br), a company that provides geolocation services for a broad range of mobile applications and covers ~20% of the mobile devices in the country. Anonymized cell-phone shows a similar pattern to air travel data, but shows travel from Manaus to other municipalities in the Amazonas states being even more important than with flights. Numbers of recorded state-level movements from and to Amazonas state, as well as city-level movements from and to Manaus municipality, are available as Data S5 and Data S6, respectively.
Logistic Function Fitting to P. 1 Genome Fraction We fitted a logistic function to the time-varying fraction of sequenced genomes belonging to P. 1, binned according to the week sampling had occurred in. The form of the logistic function is as follows: 𝑓𝑓(𝑡𝑡) = 𝐿𝐿 1 + 𝑒𝑒 -𝑘𝑘(𝑡𝑡-𝑡𝑡2) where  = maximum value of the logistic function,  = logistic growth rate,  = time since P. 1's emergence and 2 is the (inferred) time at which half of the genomes in sequenced cases belong to P. 1. Model fitting was carried out using a Bayesian framework, written in the probabilistic programming language STAN and implemented in the statistical software R (Version 4. 0. 2) using the package rStan (Version 2. 19. 03). Two chains of 1000 iterations each were run, with the 1st 500 samples from each discarded as burn-in and the remaining 500 (for each chain, 1000 samples total) retained for inference.
e. the rate at which natural immunity elicited by prior SARS-CoV-2 infection declines). The model is fitted to two sources of data. Mortality data from the SIVEP-Gripe (Sistema de Informação de Vigilância Epidemiológica da Gripe ) SARI (severe acute respiratory infections) hospitalisation database  (37) , including both class 4 and 5 death records (corresponding to confirmed and suspected COVID-19 deaths), consistent with earlier analyses  (8)  and corrected for known delays in mortality reporting using a Gaussian process nowcasting based framework  (38, 39). In addition to COVID-19 mortality data, we also integrate genomic data from the sequenced samples, fitting the model to the fraction of sequenced genomes each week that belong to P. 1 described in table  S1. Model fitting was carried out using a Bayesian framework, written in the probabilistic programming language STAN and implemented in the statistical software R (Version 4.
0. 2) using the package rStan (Version 2. 19. 03). Hamiltonian Monte Carlo with 3 chains of 1000 iterations each were run, with half the samples discarded as burnin and the remaining retained for inference. In every instance chains mixing was satisfactory, with traditional rhat statistics (for assessing convergence) less than 1. 02. All code used for inference and plotting is available at https://github. com/CADDE-CENTRE.
Data sharing and code availability Preliminary genome sequences generated from samples obtained from laboratory A were shared on GISAID on 12 January 2021. Findings were shared with representatives from the World Health Organization, Pan American Health Organization, Secretary of Health Amazonas, and FioCruz Manaus on 11 January 2021. Preliminary report describing first P. 1 genomes from Manaus was shared on 12 January 2021 (86). Epidemiological data and epidemiological model code, together with BEAST XML files, tree files, log files are archived at https://github. com/CADDE-CENTRE. GISAID IDs for the SARS-CoV-2 Manaus sequence data can be found in Data S2. All consensus sequences generated by this study can be found at https://github. com/CADDE-CENTRE.
32,0. 1) ∈ [0,100], based on results from  (67) , adjusted for the age structure of the population of Manaus and: 𝑖𝑖𝑓𝑓𝑖𝑖 2 ~ 𝑅𝑅𝑅𝑅 • 𝑖𝑖𝑓𝑓𝑖𝑖 1 , 𝑅𝑅𝑅𝑅 ~ 𝐿𝐿𝑁𝑁𝑔𝑔𝑛𝑛𝑁𝑁𝑖𝑖𝑁𝑁𝑁𝑁𝑁𝑁(0,0. 5) ∈ [0, ∞], where  denotes relative risk. We note that other work has shown that the degree of transmission advantage exhibited by the B. 1. 1. 7 can vary over time as a function of control interventions and behavior  (68). The infection-to-death distribution  is composed of infection-to-onset and onset-todeath contributions as in previous work  (69) , with adaptations to take into account the most likely onset-to-death distribution in Amazonas state based on hospitalization distributions obtained by ref. (70). The observation model uses two sources of data. In the first likelihood, the expected deaths   are modelled as negative-binomially distributed: 𝐷𝐷 𝑡𝑡 ∼ NegativeBinomial �𝑑𝑑 𝑡𝑡 , 𝑑𝑑 𝑡𝑡 + 𝑑𝑑 𝑡𝑡 2 𝜙𝜙 � with mortality data   and dispersion prior: 𝜙𝜙 ∼ Normal + (0,5) The deaths data source is based on class 4 and 5 (confirmed and probable) SARI COVID-19 deaths  (71, 72) , from the SIVEP-Gripe database for Manaus city, that have been amended with Gaussian Process nowcasting to correct for known delays between deaths occurring and being recorded in the dataset  (38, 39).
Acknowledgments General: We thank  Lucy Matkin  ( University of Oxford ) for logistic support and  Claudio Sachi  ( Instituto Adolfo Lutz ) for agreeing with the use of unpublished sequence data available in GISAID before publication. We thank the administrators of the GISAID database for supporting rapid and transparent sharing of genomic data during the COVID-19 pandemic. A full list acknowledging the authors publishing data used in this study can be found in Data S3.
figs. S3-S5). Date phylogenetic subtyping and PANGO lineage classification using the latest pangolin version (v. 2. 2. 1, 6 February 2021) (15) was conducted for dataset B and dataset C (of sample collection, age, sex, RT-qPCR CT values, lineage assignment and sequencing statistics for 184 sequences generated in this study from Manaus can be found in Data S2.
Introduction The Antarctic Ice Sheet and Southern Ocean are major thermal reservoirs in the earth system that significantly influence climate change  (Kennicutt et al. , 2015 (Kennicutt et al. , , 2019)). As the amount and availability of scientific data in this region grows, and the needs of more interdisciplinary research are realized  (Kennicutt et al. , 2014) , researchers and students increasingly require an easy-to-use, unified platform to import, display, and analyse geospatial datasets for work involving this region. Educators and logistics operators also need a convenient platform with low entry barrier to get a complete picture of these remote areas as well as develop practical tools for their work. Many such users develop their own dedicated analysis environments for individual purposes, bringing together basic data such as terrain models, satellite imagery, and scientific data from different disciplines.
Unfortunately, this entails significant start-up cost and duplicated effort. Basic Antarctic datasets, such as the Antarctic Digital Database (ADD, https://www. add. scar. org/), and the Southern Ocean Observation System Map (SOOSmap, http://www. soosmap. aq/) exist as online portals. One can download satellite data for Antarctica from the earliest such data (decades ago) through to the present (e. g. , NASA's World View, https://worldview. earthdata. nasa. gov/, Polar View, https ://www. polarview. aq/antarctic, and the US Geological Survey's EarthExplorer, https://earthexplorer. usgs. gov/). In addition, the Antarctic Environmental Portal (https://www. environments. aq/) provides an important link between Antarctic science and Antarctic policy, particularly in environmental management. However, being online portals, all these data sources are not immediately helpful during remote field work or a scientific cruise.
One exception is Antarctic Mapping Tools  (Greene et al. , 2017) , which provides Antarctic data that can be operated offline. However, it is limited mostly to physical science data and its analysis functions require proprietary Matlab computing software. Thus, instead of having to gather Antarctic scientific data from numerous online data portals in many file formats and geographic projections, a user-friendly system would provide pre-compiled basic Antarctic datasets in multiple disciplines and load consistently both in the office and in the field. For this purpose, the base software platform should be low cost and multiple-platform operational with little or no license restrictions to maximize benefits for the rapidly growing Antarctic research community. In 2011, we began developing a GIS data package that can operate with modest computer capabilities so that the system is useful at remote field camps and for a wide range of users.
We selected Quantum GIS (renamed to QGIS in 2013, https://qgis. org/) for the software platform, and named the data package "Quantarctica" (hereafter QA). Version 1 of QA was released in 2012. For version 2 released in 2014, we improved the glaciology and geophysics data coverage and data visualization. The Scientific Committee on Antarctic Research (SCAR), a thematic organization of the International Science Council, selected QA version 2 as a SCAR map product in 2014 (https://scar. org/resources/maps/). To respond to the Antarctic scientific community's increased interest in QA, Fig. 1. Sample screenshot of Quantarctica. A QGIS interface displays both raster and vector data layers. Expandable tree options (left panel) show colour bars and legends for each data layer. The main panel at right shows the detailed basemap together with year-round and summer-only research stations, and ocean surface salinity in the summer at the scale of 1:5,054,222 (see the bottom status bar).
Selection of data To select data used for basemaps, miscellaneous base layers, satellite data, and terrain models, we considered Antarctic map products widely available as best suited for the purpose in the combined terms of coverage, completeness, and detail (Sections 3. 1-3. 4). To select the scientific data, we first surveyed the relevant scientific community in 2016 to identify community priorities for included datasets. This input was considered by one or two editors for each discipline, and then the recommended datasets were further screened by the core development team at the Norwegian Polar Institute. QA3 includes 265 data layers, of which 164 layers represent scientific datasets (Tables  1  and 2 ). These selections will be re-visited when QA version 4 is developed in the future.
Mapping projection The Antarctic Polar Stereographic projection EPSG:3031 was chosen because it is both Antarctic centric and the most commonly used projection for Antarctic geographical data already. Some datasets are originally in other projections. QGIS can handle multiple data layers projected to different systems, nonetheless, on-the-fly reprojection hampers fast rendering and demands CPU work. Therefore, all data in QA are projected to the single projection, EPSG:3031.
File formats, compression, and data re-sampling Individual datasets are originally in various file formats. For consistency, we convert all vector data to ESRI shapefile format, and raster data to GeoTIFF format. Most raster files are compressed with lossless LZW and Deflate algorithms. These file formats are commonly used in the GIS community and are supported in most other GIS systems. However, including high-resolution imagery in a lossless format largely decreases QA's portability, so we converted them to the JPEG format with associated georeferenced files or to the JPEG2000 format, with a compression level that provides a hardly noticeable quality reduction (Section 3. 3). These formats are less common in the GIS community but still supported widely. In this way, datasets in QA can be used in other platforms as well.
Cartography The presentation of data layers is carefully adjusted to be intuitive, readable and visually pleasant at all map scales. The scientific datasets are presented such as to stand out when rendered on the top of basemaps and satellite imagery. The presentation is also made so that datasets often viewed together are easily distinguishable from one another for colour-deficient users. If the predefined cartography is not fully suited for a specific purpose, QA is fully customizable, so users can change the appearance. Many data layers, particularly the detailed basemaps, use scale-dependent features so that these layers are rendered to an appropriate level of details at a given scale (Fig. 1 ). Most data layers constituting detailed basemaps are stored at high, medium, and low resolutions for high-performance rendering and for best cartographic expression.
Meta data Metadata includes a brief description of the dataset for general use. Also included are original data location, citation, and the editor responsible for the data layer. We urge all users to cite this original data source, not QA, when they use the dataset in their work. In QA version 3. 2, all metadata are stored as a part of the QGIS project file (. QGS file format), and in each data folder associated with individual data layers in the QMD file format. The abstract section of the metadata (brief description of the data, citation, and handling editor) is stored as plain text (TXT) files for quick reference. The latter is useful when users are interested in using specific data layers on different platforms such as ArcGIS and non-GIS platforms such as computing software R (https: //www.
Results QA3 includes datasets categorized as (1) simple and detailed basemaps, (2) miscellaneous base layers, (3) satellite imagery, (4) terrain models, and (5) a range of scientific data (Table  1 ). Users can display and  rearrange the order of data layers that lay on top of basemaps, satellite imagery, and terrain models at a user-chosen continuously adjustable map scale (Fig. 1 ). Scientific data are categorized into nine disciplines (Table  2 ). All data layers are stored with a folder-tree structure so that individual data files and associated metadata can be easily found. Table  3  lists all data included in QA3. The total size of QA3's data files is 7. 53 GB, with the satellite imagery and terrain models constituting about 4. 13 GB (Table  1 ). Our distribution package with QGIS version 3.
g. , multibeam or single beam; Fig. 2c ;  Arndt et al. , 2013). Finally, a global ETOPO1 relief model applies to latitudes north of 60 o S  (NOAA National Geophysical Data Center, 2009). Elevation references differ between the terrain models: CryoSat-2 and RAMP2 refer to the WGS84 ellipsoid, whereas the others refer to the mean sea level using different geoid models. Similarly, each terrain model has different features with different strengths and weaknesses, so users can choose the dataset that best matches their needs. All models are displayed as rasters, derived hillshades and elevation contours. Contour intervals and hillshade parameters can be customized in QGIS to better view highly variable terrains such as continental shelf breaks, abyssal plains, steep mountainous regions near the coast, and virtually flat and featureless inland ice sheet over a range of spatial scales.
Atmosphere data Most atmospheric data provided in QA are outputs from RACMO2, a regional atmospheric climate model  (van Wessem et al. , 2014a (van Wessem et al. , , 2014b)). These include near surface data, such as 2-m-high temperature and 10-m-high wind speeds over the ice sheet and ocean (Fig. 2a ), as well as surface mass balance over the ice sheet and ice shelves, all annual values averaged between 1971 and 2011. The 27 km resolution RACMO2 raster datasets are originally projected in a rotated polar coordinate system which cannot easily be converted to EPSG:3031. However, each model cell has EPSG:4326 coordinate values as well, which we used to build up EPSG:3031 rasters at 35 km resolution, the best achievable without creating void data cells anywhere in between. Atmospheric data also include a dataset of satellite-observed wind scour zones over the ice sheet (Fig.
Biology data Biology datasets for the Southern Ocean include summer chlorophyll-a density near the ocean surface (Fig. 2b ;  Johnson et al. , 2013; Johnson et al. , 2017) , 20 pelagic regions based on seawater temperature and sea-ice distribution  (Raymond, 2014) , and 29 benthic regions  (Douglass et al. , 2014). On top of these raster or polygon data layers, users can plot locations of available vertical profiles of temperature and salinity collected by the Marine Mammals Exploring the Ocean Pole to Pole project (MEOP, Fig. 2b ;  Treasure et al. , 2017) , as well as population densities of Antarctic krill and salps surveyed in 1926-2016 by 10 countries (KRILLBASE;  Atkinson et al. , 2017). For the Antarctic coast, data include 204 areas classified as important bird areas (IBA;  Harris et al.
Environmental management data This discipline focuses on specially designated areas for environmental protection purposes. Included are the Antarctic Specially Protected Areas (ASPAs;  Terauds, 2016) , and Antarctic Specially Managed Areas (ASMAs; https://www. ats. aq/devph/en/apa-database/search#ap a-results) adopted by the Parties to the Antarctic Treaty under the provisions of Annex V to the Protocol on Environmental Protection to the Antarctic Treaty. Also included are the Convention for the Conservation of Antarctic Marine Living Resources' (CCAMLR's) Marine Protected Areas (MPAs), Small-Scale Management Units (SSMUs), and Small-Scale Research Units (SSRUs), all available from https://gis. ccamlr. org/h ome/ccamlrgis, as well as 16 Antarctic Conservation Biogeographic Regions (ACBRs) that cover most ice-free areas in Antarctica  (Terauds et al.
Geology data Geological data layers include ADD's high-resolution rock outcrop (Burton-  Johnson et al. , 2016) , undersea geomorphic features  (Post et al. , 2014) , a schematic geological map  (Tingey, 1991) , tectonic plate boundaries (Fig. 2c ;  Bird, 2003) , and epicentres of earthquakes since 1900 (Fig. 2c , https://earthquake. usgs. gov/earthquakes/search/). This discipline has three more layers. The first layer has attributes of the data source at each location of multibeam seafloor survey data included in IBCSO's bathymetry terrain model  (Arndt et al. , 2013). The second layer is locations of rock and sediment samples available from the Byrd Polar and Climate Research Center's Rock Repository at Ohio State University (https://research. bpcrc. osu. edu/rr/). Users can learn about the sample by clicking a feature and can request a physical sample.
Geophysics data Geophysics data include two gravity-field and one magnetic-field datasets. The first gravity-field one gives 10-km-resolution free-air and Bouguer gravity anomalies. It was developed by the International Association of Geodesy's (IAG) subcommission 2. 4f Gravity and Geoid in Antarctica (AntGG) using ground-based, airborne, and shipborne data (Fig. 3a ;  Scheinert et al. , 2016). Above the anomalies, a semi-transparent layer gives the accuracy estimates. As the transparency is assigned in terms of accuracy, anomalies are less visible at lower accuracy. The second one is a satellite-based EIGEN-6C4 gravity field (geoid) model available for 60 o S poleward  (Förste et al. , 2014) , which has lower-resolution, yet greater geographical coverage than the AntGG compilation (Fig. 3a ).
Finally, the magnetic-field dataset is a satellite-measured magnetic field with 5-km resolution for ~59-90 o S from SCAR's Antarctic Digital Magnetic Anomaly Project (ADMAP;  Golynsky et al. , 2013; Golynsky et al. , 2001). Also included are magnetic pole locations since 1590 and present-day magnetic declination contours, which may be used in fieldwork  (Chulliat et al. , 2014). For sub-surface geothermal flux, the geophysics discipline includes one dataset inferred from seismic velocity  (An et al. , 2015) , which adds to the two datasets in glaciology's ALBMAP compilation (Section 3. 10).
Glaciology and ice-core data Glaciological and ice-core data have the largest data volume in QA3. Data are provided on grounding lines  (Bindschadler et al. , 2011) , hydrostatic lines  (Bindschadler et al. , 2011) , two sets of major drainage boundaries (http://icesat4. gsfc. nasa. gov/cryo_data/ant_grn_draina ge_systems. php, and  Rignot et al. , 2013) , and an inventory of coastal ice rises and rumples  (Matsuoka et al. , 2015) , all of which give the basic form of the Antarctic Ice Sheet. For the grounding and hydrostatic lines, colours indicate the level of confidence in their locations. Looking to the past, paleo ice-sheet extents are shown at four epochs since the Last Glacial Maximum, together with three levels of confidence  (Bentley et al. , 2014). A 450-m-resolution surface ice-flow field derived from satellite data is shown in a raster and also as flow vectors (Fig.
Other data layers are modelled surface firn density (with depths at which the firn density reaches about 60% and   (Treasure et al. , 2017)  and satellite-mapped Emperor penguin colony locations with estimated populations given by the circle sizes  (Fretwell et al. , 2012)  superimposed on chlorophyll-a summer density  (Johnson et al. , 2017 ) and detailed basemap. One of 685 MEOP tracks is selected (red) so that one can use QGIS's identify features tool to see attributes of this track (e. g. , data collection time, file name for the full dataset, seal platform used for the data collection). (c) Epicentres of earthquakes with colour indicating magnitudes are shown clustered along the tectonic plate boundaries. Thin black curves mark locations of multibeam bathymetry data used for the IBCSO's bathymetry compilation  (Arndt et al.
, 2013). (d) Subglacial lakes outlined  (Smith et al. , 2009)  and shown with different colours for different lake properties  (Carter et al. , 2007) , superimposed on modelled subglacial water flux (blue; Le  Brocq et al. , 2013)  and outcrops (brown) using the detailed basemap. Orange-pink shading at bottom right over the ice shelf is modelled magnitude of buttressing given to the upstream ice  (Furst et al. , 2016). 90% of the pure-ice density)  (Ligtenberg et al. , 2011) , satellite-observed, 5-km resolution surface melt flux averaged between 1999 and 2009  (Trusel et al. , 2013) , and satellite-observed blue ice areas  (Hui et al. , 2014). ALBMAP is a compilation of glaciological datasets (Le Brocq et al. , 2010) that include satellite-observed and modelled surface mass balance  (Arthern et al.
, 2006; van den Broeke et al. , 2006) , modelled surface temperature  (Comiso, 2000)  and firn thickness (van den Broeke, 2008), two geothermal flux datasets inferred each from global seismic models  (Shapiro and Ritzwoller, 2004 ) and satellite-measured magnetic fields  (Fox-Maule et al. , 2005) , and newly-generated ice and bed topography datasets. These individual datasets originally have different spatial resolutions, extents, and geographic projections. ALBMAP modifies these original datasets and provides them with 5-km-resolution to be used as boundary conditions for ice-flow modelling. We include ALB-MAP in QA as a grouped set of layers. For this specific case, the airborne-measured high-resolution AntGG free-air gravity anomaly  (Scheinert et al. , 2016)  is shown together with the satellite-measured low-resolution EIGEN-6C4 gravity disturbance  (Förste et al.
For this case, the first choice of the three routes from the site F23 did not work, so the traverse team returned to the waypoint F23 and made the second choice through F23_3, which was successful. RADARSAT-2 Data and Products copyrighted by  MacDonald, Dettwiler and Associates Ltd (2013). All Rights Reserved. RADARSAT is an official mark of the Canadian Space Agency. QA3 has an ice-core database that includes the location, depth, and reported literature of 241 ice cores around Antarctica, which were reported by ITASE IceReader (http://www. icereader. org/icereader/lis tData. jsp), Climate Change Institute Antarctic Ice Core Data (http://cc i. icecoredata. org/Antarctica. html) and WAIS Divide Ice Core Project  (Fudge et al. , 2013). Also included is a dataset of surface mass balance measured at 3236 sites, together with metadata for each site showing methods, and the year of ice-core retrieval  (Favier et al.