dataesr/scientific-paragraphs-categorization huggingface dataset:
Other radar system constraints or optimizations for satellite systems are discussed by  Rodriguez (2018)  and (ESA, 2019, chapter 5), with sampling issues further analyzed by  Chelton et al. (2019). Although it was the first deployment of the KaRADOC instrument and only a limited dataset could be acquired due to the necessary adjustment process, the DRIF4SKIM campaign clearly demonstrated that surface geophysical velocities can be measured by a pulse-pair method with a Ka-band measuring at 12 • incidence. The campaign data are consistent, with a 10% bias, with a Geophysical Model Function (GMF) that expresses the geophysical Doppler as the sum of the range component of the Total Surface Current Velocity, and a Wave Doppler that is a weakly varying function of the sea state, of the order of 2.
a b s t r a c t Several seed and seedling traits are measured to evaluate germination and emergence potential in relation with environmental conditions. More generally, these traits are also measured in the field of ecology as simple traits that can be correlated to other adaptative traits more difficult to measure on adult plants, as for example traits of the rooting system. Methods were developed for deep high throughput phenotyping of hundreds of genotypes from dry seed to the end of heterotrophic growth. The present dataset comes from a project on genotyping and phenotyping of populations of genotypes, with different geographic and genetic origins so as to increase genotypic diversity of sugar beet in terms of germination and early growth traits, evaluated at low temperatures. Data were collected in relation to the creation of the first sugar beet crop ontology.
This dataset corresponds to the first automated phenotyping of a population of 198 genotypes and 4 commercial control varieties and is hosted on INRAE public depository under the reference number doi. org/10. 15,454/AKNF4Q. The equipment and methods presented here are available on a phenotyping platform opened to collaborative research and adaptable for specific services for characterizing thousands of genotypes on different crops or other species. The phenotyping values can also be linked to genomic information to study the genetic determinism of the trait values. © Value of the Data Seed and seedling traits are increasingly measured in the field of ecology as simple traits that can be used to describe species diversity. A deeper phenotyping of genetic diversity in crops is also necessary to better understand their tolerance to varied environmental conditions and determine more precise breeding traits.
The dataset was collected in relation to the sugar beet crop ontology which was also created within the same project  [8]. The data set provides the range of values measured for each variable on a large population of genotypes. The ontology defines a general international framework to collect and compare variables' values. The range of values measured here could be compared with measurements on other genotypes at intra-and inter-species level to determine the range of variation of these traits' values and provide a better understanding of adaptation of genotypes or species to different agroor ecosystems  [9, 10].
Data description The dataset contains 28 variables measured for each genotype. Data is represented by three groups of variables (Table  1 ) measured on a population of 198 sugar beet genotypes plus 4 standard commercial varieties. This data was obtained at high throughput on automated equipment. The first group of variables describes the dry seed. An innovative tool (X-ray microtomography) was used to investigate the internal anatomy of sugar beet seeds. This technology generates high-resolution images of internal seed structures. An automated image processing protocol was developed to extract data on traits of interest. The obtained data provide precise measurements of volume, surface area and shape of seed embryo, perisperm and seed coat. The second group describes seed germination speed measured as mean germination time and time to reach 50% and 70% germination, and final germination rate at three temperatures 5 C, 10 C and 20 C, obtained by automated acquisition and analyzes of images.
3D dry seed measurements Tomography was performed on 25 seeds per genotype. Images were obtained using an NSI X-50 manufactured by North Star Imaging (Minesotta, USA). The system has a focus tube with a focal spot up to 1 mm, a flat-panel detector with a resolution of 256 x256 and an adjustable turntable. All seed samples were scanned at a voltage of 45 kV and a current of 178 mA, a rotation step of 0. 5 acquiring 1080 images of the transmitted signal after passing through the object. The reconstructed data are represented by 2D images combined into one image stack and ordered by their position in the z-axis of the imaged sample. The 3D volume reconstruction is obtained from multiple cross-section 2D images of the object, using the software supplied with the machine. The stack comprises (976 Â 976 x 976) voxels in the (x, y, z) referential.
The 3D image can either be visually interpreted or analyzed using image processing tools in order to extract relevant information. An automated image processing pipeline (IPP) was developed to extract sugar beet seed features from raw data. This pipeline was developed using Avizo image processing modules where the scripts were written in TCL scripting language (Ousterhout, TCL/TK) and MATLAB (The MathWorks, Inc. , Natick, Massachusetts, United States). The IPP consists of two successive tasks of morphological operations. The first task was designed to individualize the sugar beet seeds in each image Ixyz into separate objects where each scan has a fixed number of 25 seeds. Some pre-processing steps such as noise removal and segmentation were applied to separate the voxels that correspond to sugar beet seeds from the sample holders, and also to generate 3D binary masks Mxyz that represent only the voxels that belong to the sugar beet seeds according to equation  (1).
The mean germination time  [13]  was calculated for the data in each experiment as well as percentile germination parameters (time to reach 50% and 70% of germination, see Table  1 ) that were extracted from germination curves. Early counts after 17 days at 5 C was measured as an additional evaluation of seed vigor. Final germination rates were also reported after 28, 15 and 6 days, respectively, at 5 C, 10 C and 20 C.
, Natick, Massachusetts, United States). Data source location The genotype population was obtained from a cross between an elite commercial genotype used in European sugar beet (Beta vulgaris L. ) grown area and an exotic accession of Beta vulgaris maritima from Denmark. Institution: Florimond Desprez; City/ Town/Region: Cappelle-en P ev ele; Country: France. Latitude and longitude for collected samples 50. 5167; 3. 1667 Data accessibility Repository name: URGI Plant and Fungi Dataverse Data identification number: https://doi. org/10. 15454/AKNF4Q Direct URL to data: https://doi. org/10. 15454/AKNF4Q -2D image processing of seed germination: using Fiji image processing package based on ImageJ, http://fiji. sc/. Data format Raw Parameters for data collection 3D X-ray data were acquired on dry seeds.
Observations and mapping We use data from the Equatorial Stations subphase (25 April to 6 June 2019) of the OSIRIS-REx Detailed Survey phase, which ran from 7 March through 6 June 2019. The subphase was composed of data collection from seven spacecraft stations, each positioned close to the equator at a different local solar time and ~5 km above the surface  (6). As OVIRS is a point spectrometer, with a circular 4-mrad field of view, each spectrum covers all wavelengths simultaneously for a spot on the surface  (16). At each local solar time, the spacecraft nodded along lines of constant longitude at ~2 mrad s -1 to achieve nearly global OVIRS coverage as the asteroid rotated below. Although OVIRS observed most latitudes and longitudes at each station, the individual spots were not centered at the exact same locations from station to station.
Spatially-resolved data sets were also acquired in March 2019, very close to Bennu's perihelion. These contain hits of a 3. 4-µm absorption feature, but the band depth was likely affected by thermal fill-in, an effect whereby greater thermal emission at high temperatures increases the thermal flux at the band wavelength, thereby weakening absorption features  (17). Those spectra were also noisier at long wavelengths > 3 µm, owing to higher instrument detector temperatures. Bennu's globally averaged 3. 4-µm absorption feature, as observed during the Detailed Survey, is distinct from that seen on other C-complex asteroids (Fig. 1 ). Previous observations of a 3. 4-µm feature in other disk-integrated asteroid spectra, including  (24)  Themis and Themis-like asteroids, have been attributed primarily to aliphatic organic molecules (e.
Prior to science analysis, each spectrum is resampled onto a common wavelength axis with 2-nm spectral resolution <2. 4 µm and 5-nm resolution from 2. 4 to 4. 3 µm. These resampled spectra were then fitted with a model of a thermal tail, which was subtracted, then divided by the solar flux to produce reflectance spectra  (24). Surface feature maps were constructed using a Lommel-Seeliger disk correction to remove latitudinal effects. For band depth and shape comparisons, the background continuum was removed using a model fitted to each spectrum  (24). error from the mean. (B) Regional-scale (~900 m 2 ) spectra at the same phase angles as the global spectra, and their mean with standard error (hatching). Points show the data and lines are after smoothing with a 9-spectral channel width Gaussian.
Conversion to reflectance Each calibrated radiance spectrum is resampled onto a common wavelength axis with 2-nm spacing below 2. 4 µm and 5-nm spacing above 2. 4 µm, removing any spectral overlap for ease of analysis. The thermal tail is then removed from each spectrum. Multiple methods were investigated for thermal tail removal: i) a single blackbody with a single emissivity, ii) a single blackbody with any emissivity scaling and a fixed slope, iii) an unscaled blackbody with a fit linear slope, iv) a Gaussian distribution of blackbody temperatures, and v) a Bayesian temperature-emissivity separation. After trials across several large OVIRS data sets, method ii) did the best job fitting the majority of the spectra and removing thermal radiance. Occasional bad thermal tail fits do occur, primarily at high phase angles, but they are obvious in the 1s error maps and are excluded from any analyses.
Thus, the scale factor likely represents the fractional areal coverage of surface temperatures close to or above the derived temperature with the spot. While the scale factor does not represent a true emissivity, it is consistent with the terrain roughness or shadow coverage observed within a spot, e. g. ,  Fig 4. Because it is a best fitting model, all the thermal radiance is removed with this method regardless of the spectral slope (Fig. S2, A  and B ). By subtracting a smooth function, the thermal tail removal does not add noise to the spectrum, though it lowers the overall signal to noise ratio (SNR) particularly at the long wavelengths (Fig. S2, C ). Internal instrument calibration source data acquired close in time were used to calculate SNR without any outlier screening or resampling applied, and then scaled to the Bennu signal level.
Supplemental Text Ancillary Data File Information Full resolution versions of the OVIRS maps are available as ancillary Data Files in Flexible Image Transport System (FITS) format. Each file is composed of 6 columns: shape model facet number, latitude, longitude, facet radius, value, and 1s uncertainty. File headers include additional data product information, including the map content. The files are: Data File 1 = carbon band area, Data File 2 = 2. 74-µm hydration band depth, Data File 3 = temperature, Data File 4 = 0. 55-µm reflectance, Data File 5 = 0. 5 to 1. 5-µm spectral slope, Data File 6 = temperature-detrended hydration band depth. Some small artifacts (e. g. , filter segment jumps) remain in the OVIRS spectral data and can make their way into the maps. Every effort has been made to filter out bad values including values with abnormally high uncertainty.
Acknowledgments: We are grateful to the entire OSIRIS- REx Team  for making the encounter with Bennu possible and to  C. W. V. Wolner  for editorial review. We thank the developers of the JMARS open source software (https://jmars. mars. asu. edu/open_source) for assistance with Bennu-specific visualization (47). Funding: A. A. S. , H. H. K. , V. E. H. , D. S. L. , H. C. , J. P. E. D. C. R. , D. N. D. , S. A. S. , D. R. G. , L. L. A. R, and C. A. B were supported by  NASA  under Contract.
Other challenges include the extension of value-added scientific products to socially relevant downstream services and engagement with communities to develop initiatives that will Forecasting services can play an important role in both incident decision making and the design of response services. Monitoring, forecasting and, to some extent, detection of marine pollution depend mainly on reliable and timely access to environmental data, observations and predictions products. These products provide an overview of the current and future state of meteorological and oceanographic conditions. They can also be used to drive pollutant fate prediction models, either directly or by providing boundary conditions for high-resolution nested models. Access to large geophysical datasets that are interoperable with regional and sub-regional observation and modeling systems, using standard service formats and specifications such as those provided by CMEMS, is an undeniable achievement.
The Drift Committee was responsible for providing the maritime authorities with consistent and relevant information on oil drift, observations and forecasts on a daily basis. MOTHY was used daily during the aerial surveillance and recovery period using oceanic forcing fields provided by CMEMS. Significant discrepancies were found between ocean models. Finally, the drift predictions closest to the observations were obtained with the currents provided by the ocean model of lower resolution (Fig. 11 ). This shows the importance of having several ocean models and the difficulty of using high-resolution ocean forecasts that generate many eddies whose precise location is sometimes difficult to model. This example highlights the main challenge for data providers in the future, namely improving the accuracy of current forecasts.
SAR and AIS Coupled Detection SAR imagery provides a great potential in observing and monitoring the marine environment. Ship detection in SAR imagery is well advanced  (Crisp 2004) , and knowledge about vessels position and type yields a wide range of applications, such as maritime traffic safety, fisheries control, illegal discharges and border surveillance. Applications developed in this area are vast, and the evolution of SAR sensors capabilities and the availability of large SAR image dataset coupled with cooperative positioning data (such as AIS-automatic identification system ones) allows a much finer monitoring for man-made oil pollution  (Pelich et al. 2015a ). The AIS system allows the tracks of all vessels (Fig. 14 ) in the vicinity of the spill (AIS data from 01:00 to 06:47 UTC).
Perspectives In the coming decades, we expect that satellite measurements will achieve a very high resolution in space as well as a more frequent revisit in time to track pollution on hourly basis within one kilometer or less (atmosphere) and a few meters' (ocean) footprints. At the same time, many in situ sensors will provide additional allowing better estimation of vertical information (atmosphere) and drifts due to currents and surface winds (ocean). These new datasets will boost research on pollutants variability and transport and afterward increase the accuracy of atmospheric and ocean models. The large increase in data collection amount will be challenging for the processing systems, based on data science new developments (machine/deep learning and artificial intelligence) which will be in the near future massively used.
CHR subjects who subsequently developed psychosis (n=50) were distinguished from those that did not (n=213) on the basis of lipid profile at baseline, using a model with an AUC = 0. 81 (95% CI = 0. 69-0. 93). CHR subjects who became psychotic had lower levels of ether phospholipids than CHR individuals who did not (p<0. 01). Collectively, these data suggest that lipidomic abnormalities predate the onset of psychosis, and that blood lipidomic measures may be useful in predicting which CHR individuals are most likely to develop psychosis.
17 %. The RSD for the lipid concentrations in the pooled control samples were on average 17. 8% and for the retention times on average 0. 39%. The identification was carried out in pooled serum sample, and with this information, an in-house database was created with m/z and retention time for each lipid. Identification of lipids was carried out by combining MS (and retention time), MS/MS information, and a search of the LIPID MAPS spectral database  16  , and in some cases by using authentic lipid standards. MS/MS data were acquired in both negative and positive ion modes in order to maximize identification coverage. The confirmation of a lipid's structure requires the identification of hydrocarbon chains bound to its polar moieties, and this was possible in some cases.
Statistical methods At the baseline, the lipidomics dataset was divided into two study groups, (1) individuals at clinical high-risk for psychosis (CHR, N=263) and (  2 ) healthy controls (HC, N=51). Subsequently, during the follow-up period, some CHR subjects underwent symptomatic remission, such that they no longer met clinical criteria for the CHR state (CHRr), some has persistent symptoms of the CHR state (CHRp), and others transitioned to psychosis (CHRt) (Figure  1A ). The data were log2 transformed. Homogeneity of the samples were assessed by principal component analysis (PCA)  18  and no outliers were detected. The R statistical programming language (version 3. 6. 0)  19 was used for data analysis. Principal component analysis (PCA) was performed using 'prcomp' function included in the 'stats' package.
'Heatmap. 2', 'boxplot', 'beanplot', 'gplot', and 'ggplot2' libraries/packages were used for data visualization. The effect of different factors such as age, sex, BMI and other demographic variables on the lipidomics dataset were evaluated. The data were centered to zero mean and unit variance (autoscaled). The relative contribution of each factor to the total variance in the dataset was estimated by fitting a linear regression model, where the normalized intensities of metabolites were regressed to the factor of interest, and thereby median marginal coefficients (R 2 ) were estimated. This analysis was performed using the 'Scater' package. 10 Clustering of the lipidomic data was applied by using the mclust R package (version 5. 4. 5)  20. Mclust is a model-based clustering method, where the model performances are evaluated by the Bayesian Information Criterion (BIC).
05), VIP scores  22  > 1) and T-test; p-value < 0. 05, were listed as significant. P-values were subjected to False Discovery Rates (FDR) adjustment using 'p-adjust'. The 'qpgraph' R package (version 2. 18. 0) was used for partial correlation network analysis. The 'qpNrr' function was used to estimate the non-rejection rates (NRRs) of the correlation between the lipids and/or the clinical variables. About 10,000 tests for non-rejection were performed for each pair of variables included in the correlation matrix. This analysis was performed separately for the HC, CHRp, CHRt and CHRr groups. All the spurious correlations/associations with (NRR ≤ 0. 5) were removed. The Spearman's rank correlation coefficient was estimated using 'rcorr' function coded in the 'Hmisc v4. 2-0' package. In order to understand the relative importance of lipid clusters, clinical and demographic variables in stratifying HC and CHR subjects at the baseline, we performed logistic ridge regression (LR) modelling  24.
Here, 70% of the lipidome data were used to train the model and 30% was used as a test data. LR modelling was performed using 'cv. glmnet' function deployed in R-package 'glmnet v2. 0-18'. The dataset was sampled for 10,000 times. All the LR models with AUC > 0. 60 (with 10fold cross-validation) were considered. The mean AUC, log odds ratios and standard error for each variable were estimated. LR models were then developed to stratify CHR vs. HC, CHRt vs. (CHRp + CHRr) and CHRr vs. (CHRp + CHRt). Twenety-one significantly changed lipids (p < 0. 05) between HC, CHRp, CHRt, CHRr were used either singly or in combination for LR modelling. A recursive feature elimination scheme was implemented for the optimal selection of the lipids. The lipids in LR models were either incorporated or removed in an iterative manner, starting with all 21 lipids (p <0.
05). The models were adjusted for age, gender and BMI. Accuracy of prediction was determined by AUCs. The mean AUC of a model was estimated by bootstrapping, i. e. 1000 times re-sampling with replacements and partitioning (training (70%) and test (30%) sets) the lipidomic dataset using 'createDataPartition' function coded in 'caret 6. 0. 84' package. The model with the highest mean AUC was considered to be the best model which was assessed by their Receiver Operating Characteristic (ROC) Curves using 'pROC 1. 15. 3' package. Regularized ridge models in 'cv. glmnet' requires a hyper-parameter 'l'. Here, lminimum that corresponds to the minimum crossvalidation (CV) error was determined by 10-fold CV. To identify differences in the serum lipid levels between the CHR individuals with high or low Global Assessment of Functioning (GAF) scores  14  at follow-up, we divided the CHR group into two subgroups with either high GAF (>65) or low GAF (≤ 65) at follow-up, corresponding to a relatively good or poor level of functioning, respectively.
Lipid signature of CHR group Lipidomic analysis, using UHPLC-MS, was performed on baseline serum samples from clinically high-risk individuals (CHR; n = 263) and healthy controls (HC; n = 51) (Figure  1A ; Table  1 ). A total of 173 identified (based on matched UHPLC-MS/MS spectra) lipids were included in the final lipidomic dataset. Among these lipids, the following classes were represented: cholesterol esters (CEs), ceramides (Cers), lysophosphatidlycholines (LPCs), lysophosphatidylethanolamines (LPEs), phosphatidlycholines (PCs), phosphatidylethanolamines (PEs), phosphatidylinositols (PIs), sphingomyelins (SMs), and triacylglycerols (TGs). As expected, the variables that explained most of the lipid variance were BMI, sex, and age (Figure  1B ). Therefore, the confounding effects of these variables were taken into account in subsequent analyses.
They thought to be scavengers of free radicals, and may act as endogenous antioxidants  38, 39. Our data thus raise the possibility that CHR individuals who develop psychosis may have an increased vulnerability to oxidative stress. This is of particular interest, as an independent body of work has implicated oxidative stress in the pathophysiology of psychosis  40. We have also collected measures of oxidative stress from the present sample and will assess the relationship between these and ether phospholipids in a forthcoming study. An unexpected finding was that within the total CHR sample, there were marked sex differences in lipid profile. Although the trend of lipid changes, when comparing CHR and HC individuals, was the same for both sexes, sex differences were much more pronounced in the CHR group than in HCs.