dataesr/scientific-paragraphs-categorization huggingface dataset:
The French National Immunization Program was updated in 2013 for vaccination against diphtheria, tetanus, pertussis, and poliomyelitis. Our previous findings on the evolution of age-specific booster vaccination coverage rates (VCRs) up to 2017 suggested suboptimal vaccination coverages due to the pre-2013 recommendation-residual vaccination practices. In the current analysis, we evaluated all age-specific booster VCR and distribution of age at vaccination visits in 2018. In this retrospective observational cohort study, the cumulative booster VCRs were updated at all vaccination visits up to 2018 among the people who were eligible for a booster vaccination, using a 1/97th random sample of French national healthcare reimbursement databases. The cumulative booster VCR for individuals from all age groups increased from 2017 to 2018, except for 85-years-old vaccination visit.
Study design This retrospective observational cohort study was conducted using a 1/97th random representative sample of the French national healthcare reimbursement databases (the General Sample of Beneficiaries [Échantillon Généraliste des Bénéficiaires {EGB}])  [5]. The reference system of the EGB population is updated on a quarterly basis from the health insurance administrative databases. The findings of the data analyzed from January 01, 2013 to December 31, 2017 have been recently published  [2]. These results were updated with data up to December 31, 2018. The study was performed according to the Good Epidemiology Practices  [6].
Such noncompliance with the vaccination schedule may decrease the population immunity, particularly in 6-yearold children with a significant gap between the last dose at 11 months and a possible late booster at 8 years of age. Vaccinations outside common practice could be due to a continued application of the old vaccination schedule, lack of communication on the updated vaccine schedule, vaccinations made fortuitously, and as a part of the cocooning strategy. Our study had some limitations. The data collected from EGB provided information about the number of participants who retrieved the vaccine from the pharmacy. However, it did not confirm that the vaccines were administered. This bias could have led to overestimation of VCRs. Conversely, there was possibility of underestimation of VCRs, since health care consumption of participants is not recorded by some health centres.
Over the past 60 years, scientists have strived to understand the past, present and future of the Antarctic Ice Sheet. 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 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 survey and gridded datasets accessible under the 'Findable, Accessible, Interoperable and Reusable' (FAIR) data principles. With the goals to make the gridding process reproducible and to allow scientists to re-use the data freely for their own analysis, we introduce the new SCAR Bedmap Data Portal (bedmap. scar. org, last access: 18 October 2022) created to provide unprecedented open access to these important datasets, through a user-friendly webmap 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.
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 had not been held centrally nor 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 Bedmap 1  (Lythe et al, 2001) , Bedmap2  (Fretwell et al. , 2013)  and Bedmachine Antarctica  (Morlighem et al. , 2020) , have had to first find data, gain permissions, download, clean and standardize hundreds of datasets from survey campaigns of many different sources, before finally constructing the grids. Theses 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, and the urgency in predicting future ice loss (e. g. Mouginot et al. , 2014; Golledge et al. , 2015; DeConto & Pollard 2016; Gardner et al.
, 2018; Seroussi et al. , 2020; Levermann et al. , 2020) , it is essential 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 standardisation of the data following the Findable, Accessible, Interoperable and Reusable (FAIR) data principles  (Wilkinson, 2016)  and the use of consistent data formats and attributes, as agreed by the international community through the Bedmap project. Additionally, we introduce the SCAR Bedmap Data Portal (bedmap. scar.
org, last access: 18 October 2022) which offers the ability to search individual datasets within one stand-alone map-based platform and increases 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 standardised, 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. A follow-up publication to this paper will introduce the new gridded products from Bedmap3.
1980 -1990: First compilation efforts to map Antarctica By 1983, around 50% of the Antarctic ice sheet had at least some airborne RES survey measurements (i. e. within a 50 to 100 km square grid cell)  (Drewry et al. , 1982)   the SPRI Glaciology and Geophysical Folio Series  (Drewry, 1983)  rapidly became a reference for bedrock surface and ice thickness for Antarctica. The grid contours of bed elevation were drawn from ice thickness data collected on sparse surface traverses and by airborne surveys over the entire continent, using state-of-the-art digital mapping techniques, although in many areas survey lines were separated by hundreds of km. (Lythe et al. , 2000).
g. continental ice-edge, grounding line, ice-shelf extent, etc. ) 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 and surface elevation grids were provided at a uniform 1-km spacing, but still with a native interpolation resolution of 5-km in order 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 efforts such as BedMachine Antarctica have used a combined modelling-observation approach, including a mass conservation method, to generate an 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 to produce an updated version of the Bedmap gridded products and to improve the accessibility of the underlying survey datasets of Antarctic ice thickness and bed topography (see Figure 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. In total, Bedmap3 contains 77 million points and thus includes twice the 185 number of new data points available to Bedmap2 (Figure  1d , Table  1  and S2 ).
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 comes 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 standardised for Bedmap1 consists 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.
Such an 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 data density on gridding, the statistically-summarised shapefile/geopackage point dataset (centred on a continent-wide 500 m x 500 m grid) reports the average values of the fullresolution survey data plus information on their distribution (Table  3 ). These summary statistics enable assesment 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 to the mean values of ice thickness, bed 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 on the quality of the data. Data has been directly compiled from the data providers, with only minimal quality checks: all nonvalue 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 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 degrees range 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.
At times, bed elevation was higher than surface elevation, likely due to issues with the semi-automatic picker used to extract the surface and bed reflector or a lack of distinctive reflectors in areas of shallow ice. To prevent this affecting the gridded product, we converted these values to -9999 for both the surface and the bed. Finally, we also conducted routine checks on the ice thickness data by comparing the given ice thickness value with the inferred ice thickness calculated from subtracting surface with bed. If these did not match, we placed -9999 on the ice thickness values. File naming conventions were also used throughout to easily identify a specific dataset as follows: DataProvider_Year_CampaignName_TypeofData_BM3. The type of data used was separated into three categories: Airborne Radar (AIR), Ground-based Radar (GRN), and Seismic (SEI) data.
The "BM3" abbreviation at the end identifies the datasets was part of the Bedmap3 compilation to differentiate from the Bedmap 1 and 2 (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 amount of data providers and radar systems used in the last 60 years (see Appendix tables). This uncertainty is often calculated as the RMS error of bed elevation values at crossover points across a survey area (e. g. Fremand et al. , 2022). This error typically amounts to tens of meters and is constrained by changing bed characteristics, the radar system used and/or the processing of the data, as well as 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 meters.
The metadata compiled by Bedmap for each survey provides 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 elevations values for the entire Bedmap dataset, we provide standard deviation, 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 1990s onwards. The accuracy of each bed elevation or ice thickness values can vary from sub-meter accuracy for modern GPS measurements  (Fremand et al. , 2022)  to several kilometres for data compiled as part of the Bedmap1 dataset  (Schroeder et al.
Publishing the Bedmap source data The October 2022) has been developed by the Mapping And Geographic Information Centre (MAGIC) team from the British Antarctic Survey. The publishing procedure described here is expected to be used for all upcoming versions of Bedmap with regular updates allowing new source data to be easily accessible to the community. Below, we discuss the release of the datasets centered around the FAIR data principles (Section 4. 1), and present the data portal infrastructure and its functionalities (Section 4. 2).
FAIR data publishing The source data for each version of Bedmap has been published as two separate Digital Object Identifier (DOI) datasets, the first dataset contains all the standardised CSV files described in Section 3. 1, the second dataset contains all the lines and point shapefiles as discussed in Section 3. 2. The derived, gridded Bedmap products are also published as separate DOI datasets. Previously available through ftp services, Bedmap2 products are now citable and properly stored for long-term preservation. ). We believe these to be particularly beneficial for assisting users in accessing the data and reproducing their own gridded products independently of the Bedmap project.
The SCAR Bedmap data portal The newly-developed SCAR Bedmap Data Portal (bedmap. scar. org, last access: 18 October 2022) provides a common endpoint for interacting with the Bedmap source data and products. The webmap architecture is based upon the well-used SCAR Antarctic Digital Databse webmap (https://www. add. scar. org/). The Data Portal is divided into five layer-menus: "Base layers", "Topographic information", "BEDMAP1", "BEDMAP2", and "BEDMAP3". The first menu contains the gridded products: ice bed, surface and thickness grids for Antarctica, currently contains Bedmap2 grids; these will be updated with new Bedamp3 grids as they become available. The second menu contains general topographic information such as coastline and Ice-Land surface contours taken from the Antarctic Digital Database (https://www.
Conclusion We have presented here the release of the source survey data on ice thickness, bed and surface elevation data used in Bedmap gridded products, including the upcoming Bedmap3. Altogether, this data release represents over 77 million data points collected as part of 270 campaigns since the 1950s. In addition to the previous Bedmap 1 and 2 datasets, we have here gathered new ice thickness data from 75 surveys, adding 50 million additional data points to those previous compilations. We have developed a standardised CSV format in order to ensure interoperability between the different datasets, which we have checked following a specific quality control procedure and summarised on a 500mx500m grid to provide key statistics at the scale needed for the Bedmap3 gridded products. The data have been published following the FAIR data principles.
In particular, we have provided extensive metadata with commonly-used keywords and have developed a data portal that provides a user-friendly interface to interact with and download the data. By providing and displaying both the source data and grids, the data portal allows any user to investigate the uncertainty of the gridding in specific areas and analyse differences between measurements and gridded interpolations. We believe that this data release will considerably benefit the glaciology and broader Earth science community, particularly in emerging fields such as machine learning and geostatiscs which can now make use of this wealth of fully standardised data, as well as reproduce and create new compilation grids at different scales indenpendently from the Bedmap grids. We believe that the standardisation and free availability of previously-unpublished datasets presented here will lead to improved assessment of fundamental properties of the Antarctic Ice Sheet and predictions of its future contribution to sea level rise, as well as significantly increasing the life cycle of these important data.
Data Availability All the data included in this manuscript are freely available from the UK Polar Data Centre (https://data. bas. ac. uk, last access: 19 October 2022) and the SCAR Bedmap Data Portal (bedmap. scar. org, last access: 19 October 2022). Direct link to the metadata and data can be found in Table  4. The data can be downloaded directly from the Ramadda interface by clicking on the 'GET DATA' link from the metadata page or using wget commands as documented in the following instructions: https://antarctica. github. io/PDC_GeophysicsBook/BEDMAP/Downloading_the_Bedmap_data. html (last access: 20 November 2022). When using this data, please also cite the DOI citation provided in the source CSV metadata if this exists.
Code Availability The user guide for the data portal and the Jupyter Notebook tutorials designed for reading the standardised CSV ice bed, elevation and thickness data or create the shapefiles are accessible on the Jupyter Book interface under the BEDMAP3 section Fretwell liaised with all the data providers. A. Frémand wrote the initial manuscript with input from P. Fretwell, J. Bodart and H. Pritchard. P. Fretwell designed and populated the web map. Elena Field helped with the design of the web map.
Acknowledgements We would like to dedicate this paper to the many scientists who have collected geophysical field data in harsh and extreme conditions over the Antarctic ice sheets over the last sixty years. Their commitment, dedication and drive has populated this dataset and has advanced Polar science beyond measure. Funding for the  British Antarctic Survey  staff has come from  Natural Environment Research Council core  funds. Funding for the data collection has come from many grants, institutions and projects. These are individually cited where appropriate in the metadata of the datasets.
https://doi. org/10. 5194/essd-2022-355 Preprint. Discussion started: 22 November 2022 c Author(s) 2022. CC BY 4. 0 License. archived on the BAS GitHub repository and provided via an interactive web interface using Jupyter Book (https://antarctica. github. io/PDC_GeophysicsBook, last accessed: 29 July 2022). In addition, a specific python package allows to read and plot the specific CSV formatted data (https://github. com/paul-breen/xcsv, Paul Breen, 2022, last access: 29 July 2022.
( https://antarctica. github. io/PDC_GeophysicsBook, last accessed: 29 July 2022) or via the BAS GitHub repository (https://github. com/antarctica/PDC_GeophysicsBook, last accessed: 29 July 2022). A specific library called xcsv (https://github. com/paul-breen/xcsv, Paul Breen, 2022, last access: 29 July 2022) allows to read and plot data in the extended CSV format as described in Section 3. 2. 8. Author contributionsAlice Frémand. , Peter Fretwell, and Julien Bodart co-led this data release. A. Frémand and J. Bodart standardised the data. The Jupyter Notebook was developed by A. Frémand. P. Fretwell and H. Pritchard initiated the collaboration and P.
Bedmap source data are available via the UK Polar Data Centre (PDC, https://www. bas. ac. uk/data/uk-pdc/, last accessed: 29 July 2022), a trusted repository whose purpose is to manage polar datasets. Part of the Environment Data Services (EDS) of the Natural Environment Reasearch Council (NERC) and certified by the CoreTrustSeal (https://www. coretrustseal. org/, last access: 29 July 2022), PDC applies best data management practices and requirements to facilitate reuse of the datasets stored on its data catalogue (https://data. bas. ac. uk/, last access: 29 July 2022). To increase the discoverability of the datasets, a specific data portal -the SCAR BEDMAP data Portal (bedmap. scar. org, last access: 18.
MS survey scans were followed by DIA scans of dynamic window widths with an overlap of 0. 5 Th. DIA spectra were recorded at a resolution of 17,500 at 200 m/z using an automatic gain control target of 3 Â 10 6 , a maximum injection time of 55 ms and a first fixed mass of 200 m/z. Normalised collision energy was set to 25% with a default charge state set at 3. Data for both MS scan and MS/MS DIA scan events were acquired in profile mode.
Mass spectrometry data analysis Raw mass spectrometry data was processed using Spectronaut (Biognosys) version 15. 0. 210615. 50606 with the DirectDIA option selected. The following parameters were chosen: cleavage rules were set to Trypsin/P, maximum peptide length 52 amino acids, minimum peptide length 7 amino acids, maximum missed cleavages 2. Carbamidomethylation of cysteine was set as a fixed modification while the following variable modifications were selected: oxidation of methionine, deamidation of asparagine and glutamine and acetylation of the protein N-terminus. The FDR threshold for both precursor and protein was set at 1%. Profiling and imputation were disabled. Quant 2. 0 was selected. DirectDIA data were searched against a mouse database from Uniprot release 2020 06. This database consisted of all manually annotated mouse SwissProt entries along with mouse TrEMBL entries with protein level evidence and a manually annotated homologue within the human SwissProt database.