dataesr/scientific-paragraphs-categorization huggingface dataset:
In contrast, more than 600 and 300 signals have been reported for BMI and WHR, respectively 6,7. These individual associations from GWAS, however, do not explain the shared co-morbidity between obesity and pancreatic cancer. Nevertheless, genomic loci identi ed in GWAS could be implemented in methods such as polygenic scores (PGS)  8  and Mendelian randomization (MR)  9. PGS can be used to de ne the shared genetic component between epidemiologically related phenotypes, while MR uses genetic variants as instruments to assess causality in relationships between phenotypes. In the present study, the impact of total and abdominal adiposity on pancreatic cancer risk was examined through PGS analyses, using publicly available GWAS of obesity traits data and information about pancreatic cancer within UK biobank.
UK Biobank The UK Biobank (UKBB) resource (www. ukbiobank. ac. uk) was used to de ne adiposity and cancer phenotypes for this study. We used the BMI data collected at the time of recruitment (UKBB eld 21001). WHR data was computed by dividing waist circumference (UKBB eld 48) by hip circumference (UKBB eld 49) measured at baseline. BMI and WHR data were available for 457,270 individuals. For pancreatic cancer, we used a combination of hospital admissions data, International Classi cation of Disease (ICD) codes and self-report data. Individuals with an ICD-10 code C25 and who self-reported to have a pancreatic cancer diagnosis (code 1026) were set as cases, while individuals with no cancer diagnosis were set as controls. In total, there were 1,425 cases and 457,465 controls for pancreatic cancer.
Ukbb Gwas We performed single phenotype GWAS in UKBB using the BOLT-LMM software  10. BOLT-LMM applies a linear mixed model while age, sex, genotyping array and six principal components (PCs) were used as covariates for pancreatic cancer and BMI. BMI was an extra covariate in WHR GWAS to obtain WHRadjBMI analyses. The statistical threshold for genome-wide signi cant SNPs used was P < 5×10 - 8.
Polygenic scores To construct BMI and WHRadjBMI PGS, we used risk increasing alleles at 567 and 274 SNPs respectively. The SNP list was obtained from recent large scale GWAS me-ta-analyses by GIANT consortium 6,7. However, as the target data for PGS analysis was the UKBB, which was part of the GIANT meta-analyses, we used weights from the study which did not include UKBB in the meta-analyses  12, 13  (Supplementary Fig. 1 ). We used the PLINK software  14  to generate the PGS. We used sex, age, genotyping array and six PCs as covariates in the regression model. As a sensitivity analyses, we ran a regression model with T2D as an extra covariate.
Mendelian Randomization To assess causality between the two adiposity measures and pancreatic cancer, we performed bidirectional MR using the TwoSampleMR R package  15. We obtained the genetic instrument for BMI (566 SNPs) and WHRadjBMI (278 SNPs) from the GIANT consortium 6,7. The genetic instruments for pancreatic cancer (16 SNPs) were obtained from Klein et al  5. The causal effect estimate was derived from the inverse-variance weighted (IVW) method  16. The MR-Egger  17  , simple mode 18 , weighted mode  19  and weighted median  19  tests were used as sensitivity analyses. We excluded palindromic SNPs from the exposure-outcome pairs and matched alleles between summary statistics as part of the TwoSampleMR pipeline. Outliers were removed after inspection of scatter plots and leave-one-out results.
Discussion In this study, using large scale datasets and a multi-method approach, we show that abdominal obesity assessed using WHRadjBMI is a causal risk factor for pancreatic cancer, in line with epidemiological evidence  [20] [21] [22]. The mechanisms underlying the obesity-pancreatic cancer co-morbidity remain unclear. However, several factors such as in ammation, insulin resistance and hyperinsulinemia are potential mechanisms linking obesity to cancers including that of the pancreas  3, 23. Notably, majority of these factors are hallmarks of the metabolic syndrome which correlate with abdominal obesity  24. Therefore, it is not surprising that our results show that WHRadjBMI rather than BMI is a causal risk factor for pancreatic cancer. Furthermore, the metabolic syndrome is considered a predictor of T2D  25.
As such, our results need validation in larger datasets. Finally, additional work to properly control for T2D in this analysis would be needed due to the complex relationship between obesity and T2D, more so in Mendelian randomization. In conclusion, we show that abdominal adiposity measured through WHRadjBMI, may be a more important risk factor for pancreatic cancer, compared to total adiposity. Our results provide relationship between the metabolic syndrome component and a higher risk for pancreatic cancer, with T2D being a potential driver of this association. Furthermore, we demonstrate the importance and therefore encourage the assessment of measures of obesity in clinical practice and research in the context of pancreatic cancer risk.
ETHICAL APPROVING AND FUNDING This research has been conducted using the UK Biobank resource application number  35327. This research was in part funded by the  World Cancer Research Fund (WCRF UK)  and  World Cancer Research Fund International  ( 2017/1641 ), the  Diabetes UK  (BDA number:  20/0006307 ), the  European Union 's  Horizon 2020 research and innovation programme (LONGITOOLS ,  H2020-SCI-2019-874739 ),  Agence Nationale de la Recherche  ( PreciDIAB ,  ANR-18-IBHU-0001 ), by the  European Union  through the  "Fonds Européen de Dévelopment Regional" (FEDER) , by the  "Conseil Régional des Hauts-de-France" (Hauts-de-France Regional Council ) and by the " Métropole Européenne de Lille" (MEL, European Metropolis of Lille ).
During this automated procedure, the raw dataset is compared against potential flags corresponding to more than 40 criteria that specifically refer to all operation phases related to the calculation of Hg concentrations and calibration  (D'Amore et al. , 2015) , thus marking GEM readings as "valid", "warning", or "invalid". The quality-assured and quality-controlled dataset was then generated from the site manager under consideration of the previously flagged dataset, field notes, logbooks, and site characteristics  (Magand and Dommergue, 2022). RM was sampled with a significantly lower frequency than GEM, with collection times varying from 5. 8 to 14. 1 d to allow for the concentration of enough RM mass on the dedicated 0. 45 µm PES filter. It has been shown that PES membranes can collect RM (gaseous oxidized mercury + particlebound mercury) quantitatively, similarly to cation exchange membranes  (Dunham-Cheatham et al.
Volatile organic compound (VOC) observations We make use of VOC observations to characterize the origin of sampled air masses and the extent to which they were impacted by surface influences. These data were generated in the framework of the OCTAVE project (http://octave. aeronomie. be; last access: 25 May 2022), which aimed to better understand the transport and role of VOCs in tropical regions  (Verreyken et al. , 2019 (Verreyken et al. , , 2020 (Verreyken et al. , , 2021;; Rocco et al. , 2020). VOCs were measured by two highly sensitive protontransfer-reaction quadrupole mass spectrometers (hs PTR-Quad-MS; Ionicon Analytik GmbH, Innsbruck, Austria) belonging to the Royal Belgian Institute for Space Aeronomy (BIRA-IASB) and the Laboratoire des Sciences du Climat et de l'Environnement (LSCE).
The BIRA PTR-MS was deployed at Maïdo in multiple ion detection mode to generate a near-continuous 2-year dataset of VOCs (from 19 October 2017 to 26 November 2019)  (Verreyken et al. , 2021). The LSCE PTR-MS measurements were recorded during the intensive field OCTAVE campaign at three different locations (Maïdo observatory, Le Port coastal urban city, and the tropical Bélouve forest)  (Rocco et al. , 2020). Principal recorded VOCs were acetaldehyde (CH 3 CHO), acetone (CH 3 COCH 3 ), methanol (CH 3 OH), formic acid (HCOOH), acetic acid (CH 3 COOH), acetonitrile (CH 3 CN), benzene (C 6 H 6 ), toluene (C 7 H 8 ), isoprene (C 5 H 8 ), monoterpenes (C 10 H 16 ), methyl vinyl ketone (MVK), methyl ethyl ketone (MEK) and dimethyl sulfide (DMS), a volatile organic sulfur compound. More technical and analytical information can be found in  Verreyken et al.
Data treatment and statistical tools All data analysis was performed with R 3. 6. 0 (R Core Team, 2019) and using the "tidyverse" collection of R packages  (Wickham et al. , 2019). Most visualizations were done with the R package "ggplot2"  (Wickham, 2016) , while the site overview map (Fig. 1 ) was generated with QGIS (QGIS Development Team, 2022), and the graphical abstract was created with Inkscape (Inkscape Project, 2022). Digitization of figures was done with WebPlotDigitizer  (Rohatgi, 2021). We worked with hourly averages whenever possible. Hourly averages were defined as follows. For GEM, CO, CO 2 , CH 4 , O 3 , and VOCs, the hourly average at hour "h" corresponds to all data taken between "h" and "h + 1"; for example, the hourly average for 09:00 corresponds to all observations between 09:00 and 09:59.
S2). To estimate GEM in the LFT, we thus selected nighttime data and additionally removed all GEM observations sampled in air masses with a specific humidity higher than a seasonally variable threshold. As a threshold, we used the monthly median nighttime specific humidity at coordinates for Maïdo and 800 hPa (∼ 1950 m a. s. l. ) as obtained from the ERA5 global reanalysis  (Hersbach et al. , 2020)  (see Supplement Sect. S3). Our estimate of GEM in the LFT (Fig. 3 ; see the data table in Supplement Sect. S4) shows a significant seasonal variation during the 9 months of observations, with remarkably low mean GEM concentrations of 0. 66 ng m -3 (SD: 0. 07 ng m -3 ) in DJFM, corresponding to the wet season or austral summer. For each of the 9 months, monthly averaged LFT GEM was considerably lower (0.
(2021). Maïdo LFT GEM is also somewhat lower than modeled Hg in  Horowitz et al. (2017). As for seasonality, the minimum of Maïdo LFT GEM lies somewhere in between the modeled Hg minimum of  Horowitz et al. (2017)  Figure  3. Estimated concentrations of atmospheric GEM, CH 4 , CO, and O 3 in the lower free troposphere (LFT) of the SH tropical Indian Ocean, as inferred from dry nighttime air masses arriving at Maïdo. LFT time series (blue lines) were obtained by selecting only nighttime data (23:00-05:59 LT, green lines) from all observations (orange lines), followed by the application of a specific humidity filter. The shaded area encloses the mean ± 2 times the standard error. and  Shah et al. (2021) , albeit somewhat closer to  Horowitz et al. (2017). Important caveats to the comparison with GEOS-Chem results have to be mentioned.
Data availability. Maïdo L2 GEM data (https://doi. org/10. 25326/352) are freely available  (Magand and Dommergue, 2022)  at https://gmos. aeris-data. fr/ (last access: 11 August 2022) from the GMOS-FR data portal coordinated by IGE (Institut des Géosciences de l'Environnement -Grenoble, France; technical PI: Olivier Magand) and maintained by the French national center for atmospheric data and services (AERIS). Monthly averaged Maïdo L2 GEM data for different data selections (all data, nighttime only, LFT) are given in Supplement Sect. S4. Maïdo RM data are given in Supplement Sect. S8. Maïdo (O)VOC data as acquired with a hs-PTR-MS instrument in the framework of the OCTAVE project are freely available under https://doi. org/10. 18758/71021061 (Amelynck et al. , 2021). The hourly means of CO 2 , CH 4 , and CO concentrations and a set of meteorological parameters are available on the Carbon Portal (https://www.
norvegicus. Monadjem et al. (2011)  compared movement patterns and possible interactions of Mastomys natalensis (a native rodent species) and R. rattus in distant sites of Tanzania, Malawi and Namibia using telemetry and Rhodamine B marker. In the capital city of Niger, Niamey,  Garba et al. (2014)  analysed the distribution of native and invasive rodents in a series of sites corresponding to habitation districts, cultivated gardens and industrial zones. They showed the dominance of the native M. natalensis over the invasive R. rattus and M. musculus and spatial segregation between them, which they interpreted as the result of an ongoing native-to-invasive species turn over. Hima et al. (2019)  assembled an important dataset on commensal small mammal distribution in a series of localities | 3 of 24 GRANJON et al.
| Data treatment To test for the quality of sampling and the associated representativeness of the set of specimens caught relative to the actual communities sampled, diversity analyses were performed following the principles of  Chao et al. (2014). Rarefaction and completeness curves corresponding to locality and area samples of specimens obtained were built based on data from Table  1 , using  Chao et al. (2016)  iNEXT package. Using a multivariate approach, we explored and described our data using a three-step procedure, taking into account the type of variables (quantitative or qualitative) considered. As quantitative variables, we determined (i) species abundances (i. e. number of individuals trapped) by trap, room, locality and group of localities, and (ii) capture rates (i. e. number of individuals of a given species divided by trapping effort) for each locality.
, 2011) , with the aim of describing the relationships between these two types of data (rooms treated through partial least squares (PLS) regressions and mammals described through a cPCA). The method is a multiblock PLS regression (mbpls) applied to the particular case of a single-response dataset. Block of response variables are explained by a large number of explanatory variables which are divided into K meaningful blocks. All the variables -explanatory and dependent -are measured on the same localities. The main results are summarized using overall graphical displays. All data were analysed using ade4 R package  (Chessel et al. , 2004; Dray et al. , 2007; R Core Team, 2022). Then, a Pearson's Chi-squared test was realized on the contingency table enumerating the numbers of captures of the seven main species of the small mammal community in the room types recorded, in order to evaluate whether room types may explain the local distribution of each species.
The habitat component of the ecological niche of each species was further evaluated using its distribution in the different room types (considered as integrative descriptors of microhabitat) recorded. Following  Pianka (1973) , we used two indices to characterize each species niche and their overlap between species pairs:   O ij = ∑p ij p ik /√( ∑ p 2 ij ∑ p 2 ik ) , where p ij and p ik are the proportions of the ith room type used by the jth and the kth species, respectively Finally, we examined co-occurrence patterns through the analyses of presence-absence matrices with "null model" randomization tests of marginal row and column totals  (Gotelli, 2000; Gotelli & Ulrich, 2010)  using pairs software  (Ulrich, 2008). Aggregated/random/ segregated pattern of co-occurrence of species pairs was inferred from the p value associated with the Z-score for each pair of species, either using the global dataset (from all 49 localities) or local datasets (per locality and per district in large cities).
There, M. natalensis has apparently resisted the arrival of R. appears rattus, which occurred at the end of the 1990s  (Bâ, 2002) , since its dominance in this city has continued until this period (unpublished data). This situation also occurs in villages of Upper Guinea where M. natalensis was found as the main commensal species  (Fichet-Calvet et al. , 2009) , as well as in a number of localities of Niger, including the majority of its capital city (Niamey) districts  (Garba et al. , 2014; Hima et al. , 2019). In our dataset, the other native rodent species which stay dominant in only a small number of localities are M. erythroleucus and P. daltoni in the extreme East of the study area, where M. musculus is apparently progressing and is expected to replace them in a near future. As found by  Hima et al.
possible, from village chiefs to local contractors. Fundings came from the CHANCIRA (Contract "ANR-11-CEPL-010") and ENEMI (Contract "ANR-11-JSV7-0006") projects. Additional funding and logistical support came from Research Unit CBGP ("Centre de Biologie pour la Gestion des Populations"). The comments and suggestions of one referee were very useful in improving the final version of this work.
, 2023, 9, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1002/ece3. 10539 by Inrae -Dipso, Wiley Online Library on [09/10/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License data repository DataSuds (https://doi. org/10. 23708/ PQTQDA) as a living and updating resource. The static version of this data set is also stored as a supporting information data file in Granjon et al. (2021) (as Commensal_small_mammals_southern_Sene-gal_2012_2015. csv). 20457758.
Introduction Satellites for Earth observations is a topic for research and development, which has been historically taken-on by academia and institutions. More recently by private operator are working on this topics. The observation devices are based on different technologies such as visible light collection, radar and hyperspectral imaging, interferometry. These technologies can be combined to be used together. This, in association with an increasing number of satellites in service, has led to an important rise in the amount of data collected that has to be transmitted to the ground, which is then processed and stored in a database for further use  (Sudmanns et al. (2020) ). Traditionally, satellite data transmission relies on radio-frequencies  (Elsey (1968) ). Data may transit on Data Relay Satellites (DRS) like in International Telecommunication Union (2017);  Radhakrishnan et al.
The SWOT mission launch is foreseen for year 2022 and addresses both oceanographic and hydrological communities. It aims at accurately measuring the water level of the oceans, the rivers and the lakes. It is expected that the SWOT mission will generate about 20 Petabyte (PB) of data during the mission lifetime which corresponds approximately over 7 TB-per-day. Even if the data format is not fully defined yet for the SWOT mission, the kind of data generated by such missions is usually stored in Hierarchical Data Format 5 (HDF5)  (Trott et al. (1996) ) files. The data volume issue has been addressed by the implementation of different compression schemes  (Devarajan et al. (2019) ;  Welton et al. (2011) ) in the tools for the manipulation of the HDF5 formats of the Computational Fluid Dynamics (CFD) General Notation System (CGNS) and benchmarked by previous works like in  Delaunay et al.
(2019) ;  Di and Cappello (2018). The most recent pone shows that the combination of shuffle preprocessing and deflate lossless compression (level 4) provides good results. Moreover, it states that in the case of using a lossy compression, a required precision must be defined by the scientists as a Number of Significant Digits (NSD) for each dataset variable. Naturally, when using lossy compression algorithms, the full precision of the data is not always available, which can be problematic for a scientific use of the data. In return of the lost of precision, lossy algorithms generally have a better compression rate than the lossless ones. Since scientific data needs to keep the original precision, lossless compression algorithms are preferable when possible. A key point that need to be evaluated is the consistency of the compression level depending on the nature of the data and the homogeneity of compression time and throughput.
Dataset The scientific community relies on CGNS which is a binary unstructured hierarchical format (Diane  Poirie and et al. (1998) ; Christopher  Rumsey, Bruce Wedan and Poinot (2012) ) implementing Advanced Data Format (ADF)  (Owen and Daniel (1998))  and HDF5  (Folk et al. (2011) ) to store observational data. It aims at providing a standard for recording and recovering computer data associated to the numerical solution of the equations of fluid dynamics. It also implements shapes, up to three dimensions: 0-D point; 1-D line; 2-D triangle and quadrangle; 3-D tetrahedron, pyramid, pentahedron, hexahedron. Due to the data volume, not only the transmission and storage are problematic, the access and the read/write time are significant bottlenecks for both post-processing and simulations  (Soumagne et al.
(2010) ). The dataset used for this study corresponds to the SWOT mission products. The SWOT mission will generate two types of products: the high-resolution products, which are dedicated to the hydrology thematic, and the low-resolution products, which are mostly dedicated to the oceanography domain. Basically, L0 data contain raw telemetry; L1 Single Look complex means that each pixel encodes its magnitude (I and Q) and therefore contains both amplitude and phase information. Each I and Q value is encoded using 16 bits per pixel but stored in 32-bit floating point datawords. The Pixel Cloud product (L2_HR_PIXC) contains data from the KaRin instrument configured in High Resolution (HR) mode -i. e. the HR mask is enabled; it corresponds to the pixels that are detected as being over water. The "Pixel Cloud product" is organized into sub-orbit tiles for each swath and each pass, and this is an intermediate product between the L1 Single Look Complex products and the L2 lake/river ones.
As illustrated in Figure  1 , the product granularity is 64 km × 60 km tile. The dataset used for this study is a simulated golden dataset generated using data obtained from the SWOT mission and although, it might not be the most recent, it is based on the one used in  Delaunay et al. (2019) ; for better comparisons. It   consists of four HDF5 files. Two of them contain the measurements and the two others contains synthetic generatedd data 90 divided into fields as follow: signal 1 (s1   - In order to estimate the compression performance on each type of data, every single field had been extracted and compression/decompression was performed on each of them in the testbench. The Figures 2, 3 and 4 illustrates how different the fields are from each other (extracted from the SWOT file). Figure  2  shows a high entropy and low correlation between the samples, while Figure  3  shows a high correlation and quite high entropy in the signal.
Lossless Compression Algorithms Several compression approaches have been proposed through the past decades in the domain of earth observation and scientific data. HDF5 data representation is widely used in the community, thus naturally came the need of compressing this file structure. They consist of building a dictionary of redundancies in the signal, then, only the dictionary and the indexes of the words are transmitted. By themselves, these approaches perform well, but they require the analysis of huge portions of the data before being able to start the compression. Moreover, they are more well-suited for strings than floating-point encoded samples. For this reason, some works focused on using data preconditioners to reorganize the data. Shuffle, for example, analyses the entire file or chunks of data, to reorganize the values to get consecutive similar-valued samples, this way, it permits to the encoders compressor to perform well.