dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, using our AI-based methodology and 191 Monkeypox sequences available in the GISAID repository, along with 20,603 sequences from other viruses from the NCBI dataset, we obtained 4 different primer sets with 100% in-silico specificity and 100% sensitivity using sequence EPI_ISL_13053218 as reference. The results are reported in supplementary table 5. Although future primer sets delivered by our automated methodology will still require laboratory testing to be validated, our methodology can enable the timely, rapid, and low-cost operations needed for the design of new primer sets to accurately diagnose new emerging SARS-CoV-2 variants and emerging viral infectious diseases.
Methods The methodology to create a primer set is summarized in Fig. 3. First, we generate a dataset containing the target viral sequence labeled as "1" and other viral sequences labeled as "0". In the case of the main lineage we will have a dataset with SARS-CoV-2 sequences labeled as "1" and other viruses labeled as "0". For the VOCs we create the dataset with the target variant as "1" and a set of different lineages of SARS-CoV-2 lineages as "0". Then, using evolutionary algorithms we find a candidate forward primer, by finding a suitable 21-bps sequence that can distinguish the target variant/virus from the rest. Next, assisted by Primer3Plus  28  , we verify that the forward primer is viable and create the reverse primer. In the case of SARS-CoV-2, we validate the generated primers in silico in 2,107,300 SARS-CoV-2 sequences from the GISAID repository.
The GeneFinder COVID-19 PLUS RealAmp Kit detects regions of the N, E, and RdRp genes and an internal control. The AI designed primers bind to the ORF3a gene obtaining a 179 bp amplicon, this primers set are identified as UtrechtU-ORF3a primers. For commercial Kits, RT-qPCR is performed following the manufacturer's recommendations, while for UtrechtU-ORF3a primers SuperScript III Platinum One-Step qRT-PCR Kit, forward (5′-TAG CAC TCT CCA AGG GTG TTC-3′), reverse (3′-GCA AAG CCA AAG CCT CAT TA-5′) and TaqMan probe (5′-FAM CCT TGA AGC CCC TTT TCT CT NFQ-3′) are used. All the RT-qPCR tests are developed in QuantStudio 5 Real-Time PCR System (Applied Biosystems). Finally, to evaluate PCR efficiency we run a duplicate 10-fold dilution series of viral RNA for each assay using Twist Synthetic SARS-CoV-2 RNA Control 1.
Create primer Set We downloaded 10,712 SARS-CoV-2 sequences from the GISAID repository on December 23, 2020. After removing repeated sequences, we obtained a total of 2104 sequences labeled as B. 1. 1. 7 and 6819 sequences from other variants, for a total of 8923 samples. Next, we select 1000 sequences for training the algorithm, where 605 are B. 1. 1. 7 and 395 are other SARS-CoV-2 variants. Then, we assigned label 1 to B. 1. 1. 7 variant samples, and the rest are assigned label 0, and we ran the EA algorithm 10 times, Fig. 6  (left). The results are reported in supplementary table 2. Again, we simulated the candidate primers using Primer3Plus and EPI_ISL_601443 as the reference sequence for B. 1. 1. 7. From the results, we selected the forward primer 5′-CAT GCT ATC TCT GGG ACC AAT-3′, because the sequence has more than 1 single nucleotide mutation, and therefore has a higher probability of being a successful specific primer.
In silico validation Then, we simulated the candidate forward primers using Primer3Plus 28 using the accession EPI_ISL_6590782 as reference for B. 1. 1. 519 (Omicron) variant. Although sequence GAC CCA CTT ATG GTG TTG GTC resulted in a warning for High end self complementarity, it presented 3 characteristics mutations of the B. 1. 1. 529 (Omicron) variant  47  : Q498R (A23055G), N501Y (A23063T) and Y505H (T23075C), position 23,054 to 23,075 in the reference accession NC_045512. 2  48. A single-nucleotide mutation may not be enough to work as a specific primer for detecting SARS-CoV-2 variants. Thus, to solve the High end self complementarity, we increased the size of the primer by adding a base pair at the end (GAC CCA CTT ATG GTG TTG GTCA ), which resulted in an acceptable primer candidate with a T m of 62.
Migration (α) Migration (β) C 7. 2 × 10 -10 8. 1 × 10 -6 n r 8 59=10 n α -1 -4=5 n f Edd -3 -7=5 n M 1 1 6=5 the MBH due to the emission of GWs. The presence of an accretion disk can further enhance the decay of the compact object's orbit. In this work we employ the FASTEMRIWAVEFORM (FEW) waveform model package  [24] [25] [26] [27]  and modify the trajectory evolution to take into account the presence of environmental effects. We model the evolution of Kerr circular-equatorial orbits at adiabatic order [69] by interpolating the Teukolsky fluxes _ L GW using the TEUKOLSKY package in the Black Hole Perturbation Toolkit  [70]. We model the disk-induced effects by writing the rate of change of angular momentum as _ L ¼ _ L GW þ _ L disk , with the environmental contribution from Eq. (2. 2). This implementation provides a fast and accurate adiabatic trajectory that can be fed into a waveform-generation formalism through the augmented analytic kludges  [71].
ACKNOWLEDGMENTS The authors wish to thank  V. Cardoso ,  F. Duque ,  Z. Haiman ,  S. Hughes ,  J. Krolik , and  N. Stone  for very useful discussions. Special thanks to  A. Derdzinski  for insightful discussions and feedback on our results. A. A. is supported by  NSF  Grants No. PHY-1912550 , No. AST-2006538 , No. PHY-090003 , and No. PHY-20043 , and  NASA  Grants No. 17-ATP17-0225 , No. 19-ATP19-0051 , and No. 20-LPS20-0011. S. B. acknowledges support form the  French space agency CNES  in the framework of LISA. E. B. acknowledges financial support provided under the European Union's  H2020   ERC Consolidator Grant  " GRavity from Astrophysical to Microscopic Scales " Grant Agreement No. GRAMS-815673 , and from the  MISTI Global Seed Funds MIT-Friuli Venezia-Giulia. This work was supported by the  EU   Horizon 2020 Research and Innovation Programme  under the  Marie Sklodowska-Curie Grant  Agreement No.
A cosmological example of this subtle rearranging of portions of likelihood into prior can be found in many codes which implement nested sampling such as cobaya  [10] , cosmomc  [11, 12] , MontePython  [13]  or cosmosis  [14]  in the manner in which they consider Gaussian priors. Codes can either make the choice to incorporate this as part of the prior (typically as an inverse-errorfunction-based unit hypercube transformation), or as a density term added to the rest of the log-likelihood calculations. This rearranging affects the Kullback-Leibler divergence. The difference can be found using a posterior average: D π {P } = D π {P } + ln π π P (9) If the final term in Equation (  9 ) is negative, repartitioning provides a mechanism for reducing the time to convergence by transferring a reasonable "guess": the proposal distribution, from likelihood to prior, reducing the Kullback-Leibler divergence between the prior π and the posterior P = P.
Abstract. One of the key components of this research has been the mapping of Antarctic bed topography and ice thickness parameters that are crucial for modelling ice flow and hence for predicting future ice loss and the ensuing sea level rise. Supported by the Scientific Committee on Antarctic Research (SCAR), the Bedmap3 Action Group aims not only to produce new gridded maps of ice thickness and bed topography for the international scientific community, but also to standardize and make available all the geophysical survey data points used in producing the Bedmap gridded products. Here, we document the survey data used in the latest iteration, Bedmap3, incorporating and adding to all of the datasets previously used for Bedmap1 and Bedmap2, including ice bed, surface and thickness point data from all Antarctic geophysical campaigns since the 1950s.
More specifically, we describe the processes used to standardize and make these and future surveys and gridded datasets accessible under the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles. With the goals of making the gridding process reproducible and allowing scientists to re-use the data freely for their own analysis, we introduce the new SCAR Bedmap Data Portal (https://bedmap. scar. org, last access: 1 March 2023) created to provide unprecedented open access to these important datasets through a web-map interface. We believe that this data release will be a valuable asset to Antarctic research and will greatly extend the life cycle of the data held within it. Data are available from the UK Polar Data Centre: https://data. bas. ac. uk (last access: 5 May 2023 ).
Introduction Detailed and extensive information on ice thickness and bed topography is needed to reconstruct the geological and geomorphic history of Antarctica and to model ice flow in order to predict the ice sheet's future contribution to sea level rise  (Fretwell et al. , 2013; DeConto and Pollard, 2016; Scambos et al. , 2017; The IMBIE team, 2018; Rignot et al. , 2019; Morlighem et al. , 2020; DeConto et al. , 2021; Fox-Kemper et al. , 2021). This information has primarily been gathered using ground-based or airborne radio-echo sounding (RES) and seismic surveys conducted by over 50 institutions under multiple national programmes across Antarctica over the last 60 years. However, up until now, these survey datasets have not been held centrally or been standardized, thus limiting their accessibility to the wider Antarctic community.
Consequently, previous attempts to map the ice sheet on the continental scale, such as Bedmap1  (Lythe and Vaughan, 2001) , Bedmap2  (Fretwell et al. , 2013)  and Bedmachine Antarctica  (Morlighem et al. , 2020) , have had to first find data, gain permissions, and download, clean and standardize hundreds of datasets from survey campaigns of many different sources before finally constructing the grids. These constraints have led to only a limited number of gridded products being made, often years apart and with a long lag after the surveys have been completed. Given the rapidity of change affecting large parts of the Antarctic Peninsula and threatening the stability of the West Antarctic Ice Sheet, together with the urgency in predicting future ice loss (e. g. Mouginot et al. , 2014; Golledge et al.
, 2015; DeConto and Pollard, 2016; Gardner et al. , 2018; Seroussi et al. , 2020; Levermann et al. , 2020) , it is essential, beyond the legal imperative stated in Section III 1c of the Antarctic Treaty, for these data to be freely available to the international community. Supported by the Scientific Committee of Antarctic Research (SCAR) Bedmap3 Action Group, this paper presents the release of all of the underlying ice bed, surface, and thickness survey data points that have been used in the previous and upcoming versions of Bedmap gridded products (Bedmap1, Bedmap2, and Bedmap3). We discuss the standardization of the data following the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles  (Wilkinson et al. , 2016)  and the use of consistent data formats and attributes, as agreed to by the international community through the Bedmap project.
Additionally, we introduce the SCAR Bedmap Data Portal (https://bedmap. scar. org, last access: 1 March 2023), which offers the ability to search individual datasets within one stand-alone map-based platform and in-creases the discoverability and accessibility of the data. Our aim is to make the gridding process as reproducible as possible by making the source survey data fully standardized, openly available, and easily accessible through one portal. It is expected that the data presented in this paper will facilitate the creation of a range of new gridded products at different spatial resolutions, enable the application of emerging techniques such as machine-learning and geostatistical techniques to fill gaps between direct measurements, and provide a common data-sharing baseline for future geophysical surveying of Antarctica.
1990-2020: the Bedmap era In the mid-1990s, advances in radar data acquisition and development of modern global navigation satellite systems (GNSSs) led to substantial improvements in the coverage and accuracy of the data collected. Until then the positioning was often inferred using the "unaided inertial navigation" technique which often had substantial positioning errors  (Schroeder et al. , 2020). In 1996, the first BEDMAP consortium group (here termed Bedmap1) was set up under the joint sponsorship of the European Ice Sheet Modelling Initiative (EISMINT) and SCAR. It led to the publication of the first Bedmap products: a printed map published in 2000  (Lythe and Vaughan, 2000)  and its associate digital version in 2001  (Lythe and Vaughan, 2001). For more than a decade, Bedmap1 played a crucial role in providing large-scale boundary conditions of the Antarctic Ice Sheet for observational and modelling applications (e.
g. continental ice edge, grounding line, ice shelf extent) useful for ice sheet modelling. This compilation included 25 million measurements, an order of magnitude more than were used in Bedmap1. This time, the ice thickness, bed elevation, and surface elevation grids were provided at a uniform 1 km spacing but still with a native interpolation resolution of 5 km to satisfy the data providers' conditions for use  (Fretwell et al. , 2013). Since 2012, new RES datasets have been collected across Antarctica, with a particular focus on the "poles of ignorance" identified in Bedmap2  (Pritchard, 2014) , thus filling known data gaps in key areas of East Antarctica (see Sect. 3. 1). In addition, new hybrid compilation ef-forts such as BedMachine Antarctica have used a combined modelling-observation approach, including a mass conservation method, to generate improved bed topography and ice thickness in data-deficient areas of the Antarctic coastline  (Morlighem et al.
2020-present: general approach for Bedmap3 In 2020, the SCAR Bedmap3 Action Group was tasked with producing an updated version of the Bedmap gridded products and with improving the accessibility of the underlying survey datasets of Antarctic ice thickness and bed topography (see Fig. 1a-c ) through standardization and dissemination of the data via the new SCAR Bedmap Data Portal. This will serve as a common endpoint to discover and interact with all underlying Bedmap data. The Bedmap3 gridded products will be constructed using a similar process to Bedmap2 but will offer a significant improvement in survey data coverage along with a newly updated grounding line, updated altimetry-derived surface topography, and updated ice extent and bathymetry. Each iteration of Bedmap contains large survey data additions that have increased the accuracy of the gridded products.
Ice thickness, surface and bed elevation data The primary source data consist of survey point measurements of ice thickness, bed elevation and surface elevation, which principally come from airborne radar surveys and seismic soundings and to a smaller extent from ground-based radar surveys. We present here the data compiled within each version of Bedmap. Bedmap1 source data (1950s-1990s) often lack the campaign metadata available for more modern datasets, and so we present these as a single dataset. In total, the data standardized for Bedmap1 consist of almost 2 million points from 127 individual campaigns (Table  1 ). While the data coverage is substantial, especially over West Antarctica and the Antarctic Peninsula (Fig. 1a ), the distance between individual flight lines and soundings is much larger than those of the Bedmap2 and Bedmap3 data.
Standardization Due to the large number of data providers and the lack of common protocols, the data received as part of Bedmap data calls came in various forms, including text, comma-separated value (CSV), ASCII or Excel files. To ensure long-term accessibility, all submitted data files were standardized based As part of the standardization, a specific header and structure consisting of identical variable names in a strict order for all the ice thickness data were developed in order to simplify access, particularly for programming purposes. The format consists of (i) an extended header section, (ii) a header row composed of the column name following the CF convention and units in parentheses, and finally (iii) the data using a comma as the separator. The extended header consists of general information regarding each campaign, such as the year, the name of the main investigator, and funding and processing details as shown in Table  3.
Summarized point data In addition to providing standardized CSV data (see Sect. 3. 1), we also provide the data as shapefile and geopackage lines and statistically summarized points. Lines were calculated automatically from the point data and split each time a gap of more than 5 km between two data points was found. For Bedmap1, due to the sparsity of points, it was not possible to convert the data to shapefile or geopackage lines; thus, only the Bedmap1 shapefile points are provided as part of this data release. Please also note that the Bedmap1 data are not split per campaign as per Bedmap2 and Bedmap3 and are only provided as a single geopackage or as shapefile point files. The spatial distribution of the full-resolution survey point data is heterogeneous with, for example, dense, metre-scale sampling along modern flight lines that are often separated across-track by kilometres to hundreds of kilometres, and this heterogeneity varies between campaigns and data providers.
Uneven data distribution can cause gridding algorithms to be overly weighted to those areas with the highest sampling frequency, to the detriment of adjacent areas with valid data but sparser sampling. To reduce the impact of along_track_distance Metres (m) Distance in the along-track direction data density on gridding, the statistically summarized shapefile/geopackage point dataset (centred on a continent-wide 500 m × 500 m grid) reports the average values of the fullresolution survey data plus information on their distribution (Table  4 ). These summary statistics enable assessment of the confidence in the averaged data values and the variability of the measurements within each cell (e. g. bed roughness). Figure  2  gives an insight into the mean values of ice thickness, bed elevation, and surface elevation as well as the number of points per cells used for the calculation.
Quality control and limitations The purpose of this data release is to include all possible data collected over the last 60 years without discriminating the quality of the data. Data have been directly compiled from the data providers, with only minimal quality checks: all non-value data were converted to -9999, including any negative ice thickness values and any points with clear outliers. We checked the minimum and maximum values of each field to ensure the data are in a reasonable range and calculated the mean and standard deviation on each dataset to identify potential issues. For example, if longitude-latitude values did not fall within the expected -180 to 180 • or -50 to -90 • ranges respectively, the entire row was removed. When no ice thickness values were provided but surface and bed elevation values existed, we simply calculated ice thickness by subtracting the surface value from the bed value.
A. C. Frémand et al. : Antarctic Bedmap data File-naming conventions were also used throughout to easily identify a specific dataset as follows: Dat-aProvider_Year_CampaignName_TypeofData_BM3. The type of data used were separated into three categories: airborne radar (AIR), ground-based radar (GRN), and seismic (SEI) data. The "BM3" abbreviation at the end identifies the datasets as part of the Bedmap3 compilation to differentiate them from the Bedmap1 and Bedmap2 (BM1 and BM2) compilations. For instance, the file named "NASA_2019_ICEBRIDGE_AIR_BM3. csv" refers to the ICEBRIDGE airborne campaign led by NASA in 2019. Providing an overall uncertainty value for all the bed elevations compiled by Bedmap is challenging due to the number of data providers and radar systems used in the last 60 years (see the Appendix tables).
This uncertainty is often calculated as the root-mean-square error (RMSE) of bed elevation values at crossover points across a survey area (e. g. Fremand et al. , 2022a). This error typically amounts to tens of metres and is constrained by changing bed characteristics, the radar system used, the processing of the data, and the value used for the propagation of radar waves through ice which is used to convert the radar two-way travel time to depth in metres. The metadata compiled by Bedmap for each survey provide information on whether any firn correction has been applied to the elevation values and on the value used for speed of electromagnetic waves through the ice. In order to address the uncertainty in elevation values for the entire Bedmap dataset, we provide standard deviation, interquartile range, and standard error statistical parameters which are key to determining the variability of values in each 500 m × 500 m cell.
Together, these parameters are used to assess the level of confidence in the data, where low values reflect a stronger fidelity in the data. We also note that the spatial accuracy of datasets included in Bedmap2 and Bedmap3 is significantly higher than for Bedmap1 due to the use of high-resolution GPS data, which have allowed for much better accuracy in the location of the measurements for all surveys acquired from the 1990s onwards. The accuracy of each bed elevation or ice thickness value can vary from sub-metre accuracy for modern GPS measurements  (Fremand et al. , 2022a)  to several kilometres for data compiled as part of the Bedmap1 dataset  (Schroeder et al. , 2019). As this spatial uncertainty directly impacts the position of the elevation values and therefore their accuracy, the elevation uncertainty statistical parameters can be used to indirectly assess the confidence in the spatial accuracy.
Publishing the Bedmap source data The Bedmap source data are available via the UK Polar Data Centre (PDC, https://www. bas. ac. uk/data/uk-pdc/, last access: 1 March 2023), a trusted repository whose purpose is to manage polar datasets. Part of the Environment Data Services (EDS) of the Natural Environment Research Council (NERC) and certified by the CoreTrustSeal (https://www. coretrustseal. org/, last access: 1 March 2023), the PDC applies best data management practices and requirements to facilitate re-use of the datasets stored in its data catalogue (https://data. bas. ac. uk/, last access: 1 March 2023). To increase the discoverability of the datasets, a specific data portal -the SCAR BEDMAP Data Portal (https://bedmap. scar. org, last access: 1 March 2023) -has been developed by the Mapping And Geographic Information Centre (MAGIC) team from the British Antarctic Survey (BAS).
FAIR data publishing The source data for each version of Bedmap have been published as two separate digital object identifier (DOI) datasets: the first dataset contains all the standardized CSV files described in Sect. 3. 1, and the second dataset contains all the lines and point shapefiles as discussed in Sect. 3. 2. The derived gridded Bedmap products are also published as separate DOI datasets. Previously available through ftp services, Bedmap1 and Bedmap2 products are now citable and properly stored for long-term preservation. Table  5  presents the different links to the data for the different versions of Bedmap. To make the data findable, ISO 19115/19139 metadata are provided for each dataset. Each metadata record provides general information about the dataset and is registered and indexed accordingly in the UK PDC data catalogue A DOI is provided for every Bedmap dataset (Table  5 ), making them retrievable and citable.