dataesr/scientific-paragraphs-categorization huggingface dataset:
Such practical and user-friendly tools also accelerate the education of electrophysiologist trainees and promotes the adoption and expansion of data harmonization efforts, such as BIDS and Neurodata Without Borders. Every instance of data processing is logged, with the filenames of the data used and time stamps of execution. These simple, yet powerful features document the provenance of data derivatives and analysis results. Custom IN analysis pipelines assembled for elementary processing blocks of the software's library can be shared with collaborators, publishers and the scientific community. Pipelines are constructed via the GUI and saved as Matlab files. The open-source code of IN-Brainstorm is thoroughly documented, verifiable and can benefit from contributions from any user via GitHub.
left) shows IN-Brainstorms' GUI for unsupervised spike-sorting. Raw files are dragged and dropped into the GUI process box before a spike-sorting tool is selected from the IN-Brainstorm toolkit. Next, spike events are detected on each electrode and classified according to their putative neuronal generators. The unsupervised spike events produced overwrite the online counterparts that were detected during data acquisition. The output of the spike-sorting process (Fig. 3 Box 1) is automatically registered to and accessible from the IN-Brainstorm database and linked to the corresponding raw file. The spike events are labelled in a principled manner (per channel and source cell number -Fig. 3Box 2).
Imputation software uses two main strategies: the first is based on pedigree and Mendelian segregation  [6] [7] [8] , and the second relies on linkage disequilibrium  [9, 10]. Some authors use sequentially or in a given combination both approaches  [11]. The first strategy is the one implemented in algorithms like Lander-Green  [12] , Elston-Steward  [13]  or Monte-Carlo sampling algorithms  [14, 15]. The second strategy is commonly used for samples with low levels of kinship and unknown ancestors, relying instead on the linkage disequilibrium between markers within the reference population. It uses heuristic algorithms as Expectation Maximization (EM) algorithm, coalescence models and Markov's hidden strings (HMM)  [16, 17]. Recently, a study has compared eight machine learning methods to impute a genotype dataset, but results are of lower quality than those from Beagle, a reference software in the domain of imputation  [18, 19]  which is based on the forecited second strategy  [20].
Genotype imputation We used the FImpute software (v 2. 2)  [11] , as many studies have already pinpointed its good performance for imputation when compared to many other alternatives  [16, 35, 43, 44]. FImpute can use different sizes of rolling windows with a given overlap to scan the genomes of target and reference datasets. The pedigree information is used to increase imputation accuracy. Therefore, FImpute combines both formerly stated strategies for imputation: that based on pedigree and that on LD. A first round of genotype imputation was performed to predict 1% of missing data still existing on the SNP chip panel. The second and most substantial imputation scheme was between the genotypic data from the chip SNP (SNPchip) and the sequence data (SNPseq). To assess imputation accuracy, a leave-one-out cross validation scheme was performed among the 43 sequenced individuals.
The SNPseq were masked for one individual at a time, and this individual with only SNPchip data was subsequently imputed with the rest of individuals. To challenge the imputation scheme, an additional set of 6 unrelated individuals with sequences were added to the target panel. We estimated imputation quality (or accuracy) using various statistics. One was the proportion of alleles correctly imputed by each leave-one-out individual (across SNPs, one proportion per individual and per chromosome: Propi), and by positions (across individuals, one proportion per position: Props) (further explanations in Fig. 1 ). The proportion of alleles correctly imputed by SNP might be subjected to frequency-dependent bias, in the sense that imputation could be correct more often than not when the imputed allele is already highly frequent.
1. 13 from the gVCF-GATK results files); minor allele frequency (FreqOri); the ratio between SNPchip density and SNPseq density in nonoverlapping 500kb windows (RatioDensity); the p-value of an exact Hardy-Weinberg Equilibrium test (hweOri) for each site as defined by  Wigginton et al. (2005)   [47]  and the level of unique information contributed by each SNP given the level of LD with neighbouring SNPs, and calculated as the weight (Weight) obtained by the LDAK5 software  [48]. The variation of the imputation quality variables (Props, lbProps and cProps) were analysed according to the different factors by a principal component analysis. The factor's relevance to describe the imputation quality variables were quantified with a Boruta algorithm which is a wrapper built around the random forest classification algorithm implemented in the R (R Core Team 2015) package Borut  [49].
Linkage Disequilibrium Plink software  [50, 51]  was used to estimate the linkage disequilibrium parameter D'  [52]  in the SNPchip dataset and after imputation in the SNPseq dataset. Both sets were previously phased. The SNPseq dataset was further filtered based on Props (> 0. 9) and cProps (> 0. 8) variables, in order to provide for the LD analysis positions with few or no errors after imputation. Fig. 1  Metrics for the assessment of imputation quality and accuracy by individuals and by SNPs. The first upper panel depicts an example of a toy genotyping matrix containing the allelic doses, with markers in columns and individuals in rows. First two individuals correspond to complete genotypes from sequences; next two to sequences with masked positions to be imputed for quality assessment; and last individual to one genotype from the SNP array.
Mapping and genotype calling results Sequence datasets for every individual were mapped on the P. trichocarpa reference genome v. 3. 1. In average, 91. 7% of reads were mapped, 76. 5% were paired and only 2. 2% were singletons. The genome coverage was calculated by individual, and it varied between 4X and 52X, with a mean coverage of 13X (Table  S1 [see Additional file 1]). A total of 27,475,756 SNPs and Indels were detected by gVCF-GATK, whereas 26,489,941 SNPs were detected by Freebayes (Table  2 ). After scoring the SNPs on a quality criterion (Phred score > 30), the number of trimmed positions were twice as many with gVCF-GATK than with Freebayes (Table  2 ). Among the remaining positions, some were monomorphic within P. nigra individuals but different from the reference sequence: about 1 million for gVCF-GATK and twice as much for Freebayes.
Annotation A total of 93. 4% of SNPchip and 99. 79% of SNPseq were annotated (Table  4 ). Most categories in the annotation catalog were enriched in the SNPseq compared to the corresponding levels of enrichment in the SNPchip. In the   [47]  ; Weight: LD weight estimate with the LDAK5 software; FreqOri: original allelic frequency in the sequenced individuals; QUAL: per-site SNP quality from the calling step; DEPTH: sequencing depth per site summed across all individuals ; RatioDensity: ratio between SNPchip density and SNPseq density in a 500kb window. "ShadowMean", "shadowMax" and "ShadowMin" correspond to effects obtained by shuffling the original attributes across objects and used as a reference for deciding which factors are truly important exonic region, SNPs were categorized depending on different mutation types.
Discussion In this study, we have shown that substantial (26-fold) densification in marker coverage is possible in up to 1000 individuals through imputation from a few sequenced nodal individuals  (43). Simultaneously, we have achieved imputation qualities higher than 0. 84, which is sufficient for a heterozygous species like poplar but may be insufficient when working with species involving inbred lines. This imputation quality is similar to the one obtained on horses  [34]  with Impute2 software or in cattle  [33] , and higher than the one obtained on chickens  [35]. The study is based on a subset of a breeding population in black poplar, with a relatively low effective number of contributing parents, which could explain partly the success of the imputation. However, this situation is far from exceptional and could be easily found in many other species going through breeding activities, where an elite of a few dozens of parents can contribute substantially to next generation  [54].
Acknowledgements The authors acknowledge the "GIS peuplier", the  UE GBFOR and PNRGF (ONF)  for access, maintenance, and sampling of plant material. The authors want to thank  Vincent Segura  and  Aurélien Chateigner  for their valuable discussions;  Vanina Guérin  and  Corrine Buret  for their work on DNA extraction for the genotyping and the sequencing;  Aurélie Chauveau  and  Isabelle Le Clainche  for libraries construction, sequencing and Infinium genotyping;  Elodie Marquand  and  Aurélie Canaguier  for data processing and management. EPGV group acknowledges also CEA-IG/CNG by conducting the DNA QC and by providing access for their Illumina Sequencing and Genotyping platforms.
Funding This study was funded by the following sources : sequencing and genotyping data by the  INRA AIP Bioressource, EU NovelTree  ( FP7 -211868 ),  EU Evoltree  ( FP6-16322 ), and  INRA   SELGEN funding program  (project  BreedToLast ) ; the PhD grant of MP by  INRA   SELGEN funding program  (BreedToLast) and by  Region Centre -Val de Loire funding council.
Availability of data and materials This Whole Genome resequencing project has been submitted to the international repository Sequence Read Archive (SRA) from NCBI RA as BioProject BreedToLast PRJNA483561. The datasets analysed during the current study are available in the INRA Data Portal repository, https://data. inra. fr/privateurl. xhtml?token=0f26535e-4c12-4907-8d2b-ce69e39c1ee0.
Despite the major improvements anticipated with the PACE instruments, gaps/issues remain to be filled/ tackled. They include dealing properly with whitecaps, taking into account Earth-curvature effects, correcting for adjacency effects, accounting for the coupling between scattering and absorption, modeling accurately water reflectance, and acquiring a sufficiently representative dataset of water reflectance in the UV to SWIR. Dedicated efforts, experimental and theoretical, are in order to gather the necessary information and rectify inadequacies. Ideas and solutions are put forward to address the unresolved issues. Thanks to its design and characteristics, the PACE mission will mark the beginning of a new era of unprecedented accuracy in ocean-color radiometry from space.
, 1999; Bailey et al. , 2010). Since the heritage AC does not perform water vapor correction, except for out-of-band effects using the  Gordon (1995)  method, the algorithm must be enhanced to include such a correction, if spectral regions with modest to large water vapor features are to be used for ocean color science (i. e. , 600-710 nm). Recently, the heritage AC algorithm maintained by NASA  (Mobley et al. , 2016)  was extended to include the hyper-spectral algorithm of ATmospheric REMoval (ATREM) for the estimation and correction of water-vapor transmittance  (Gao and Davis, 1997) , as detailed in  Ibrahim et al. (2018). ATREM calculates the transmittance using the HITRAN 2012  (Rothman et al. , 2013)  database at 0. 05 cm -1 wavenumber spectral resolution, which is down-sampled to the sensor spectral resolution.
The water vapor profile was assumed to be the US standard 1976 and the water vapor absorption coefficients were obtained from the HITRAN 2012 database  (Anderson et al. , 1986; Rothman et al. , 2013). Figure  7  shows the scatter plot between the assumed CWV in the VRT simulations and the retrieval of CWV by the AC algorithm. In this analysis, the water body was assumed black (not reflecting). We used three modes of retrieval. The first is using the average CWV retrieval using the strongly absorbing 940 nm band and the less sensitive 820 nm band shown in blue circles, with the error bar of 1 standard deviation due to changes in the simulated geometries. With this combination, the CWV retrieval shows a strong underestimation at higher water vapor concentrations. This is due to the combination of a weak ocean signal (in that case ocean surface reflectance) and a strongly absorbing 940-nm water vapor feature that leads to loss of sensitivity to further increases in CWV.
25) and 865 nm (>0. 10). In the estuary, the water reflectance spectra are similar in shape and magnitude to those reported by  Doxaran et al. (2002) , even the feature around 750 nm. The high-resolution (30 m) images also reveal a sharp contrast between the turbid estuarine waters and the clearer offshore waters, i. e. , relatively little mixing, which would not be observed in coarse resolution (e. g. , 1 km) imagery. In the case where the NNs are used to provide a direct solution of the atmospheric correction problem it is not clear that this is applicable to the PACE OCI hyper-spectral data. As there is no relaxation to the observations based on the estimate of the atmospheric state, in this approach the NN estimate of water reflectance can only reproduce something close to what is in its training data set.
The numerical efficiency and fact that the spectral behavior is not tied to a specific absorption spectrum mean that this AC scheme should be applicable to PACE OCI. Saulquin et al. (2016)  generate prior distributions that are Gaussian Mixture Models for the water reflectance and the atmospheric reflectance, using the MERMAID in-situ matchup database. They then use a gradient descent search to try and find a minimum of the Maximum A Posteriori cost function. Since the Maximum A Posteriori criterion may not be concave they use 25 random initializations to try and eliminate solutions that are captured in local minima. While this method has been successfully applied to AC in complex coastal waters, it is not clear that the existing method for finding the Maximum A Priori solution is sufficiently efficient for global applications.
It is also important to note that all a priori assumptions are general, i. e. , no location-specific assumptions with exception of water fraction (i. e. , water or land) that is assumed a priori using the pixel geographic coordinates. Moreover, a single initial guess for aerosol and surface parameters is used. Therefore, the results retrieved from POLDER/PARASOL observations are fully driven by the measured total and polarized reflectances (6 wavelengths, 16 viewing directions, and 3 polarization states, i. e. , 192 data points for each inversion). In the future, some solid climatological information (e. g. , about land surface) can be included too. All radiative transfer calculations including calculation of Jacobians are performed during the retrieval and the solution is sought in continuous solution space.
As one can see from Figure  19 , the obtained values are in rather good agreement MODIS results for both the global distribution and for the magnitudes of obtained values. Finally, Figure  20  displays the comparison of PARASOL/ GRASP retrieved values of wind speed with the values provided by ECMWF ERA-Interim reanalysis data. Agreement is good between two data sets both for contour and location of specific geographical features and the correlation of the values. The agreement is especially encouraging taking into account that PARASOL/GRASP retrieval did not use any climatological or ancillary data. Thus the polarimetric multi-angular observations allow for robust retrieval of the wind speed. It should be noted that PARASOL/GRASP retrieval was performed for all non-cloudy ocean pixel including those in glint areas.
Second, several scattering angles are sampled in the observations, and the dependence with scattering angle is sensitive to aerosol type and size. This information may help to determine the proper aerosol type in atmospheric correction schemes, or at least to restrict the selection to a reduced set of possible (theoretical or statistical) models. Diner et al. (1999)  showed that sulfate (accumulation mode) and urban soot aerosols cannot be distinguished by observing at nadir in the red and infrared, while the multi-angle MISR data (9 geometries) in this spectral region are able to identify that an incorrect aerosol model has been assumed in the retrieval of optical depth. Gordon (1997) , following  Wang and Gordon (1994) , had earlier indicated that it is possible to atmospherically correct MISR imagery over water bodies using a single spectral band (near infrared), by comparing the angular distribution of TOA radiance with predictions using aerosol models and selecting the model that best matches the measurements.
Current satellite retrievals mitigate random and systematic errors over the open ocean by invoking standardized models for ocean surface spectral reflectance and aerosol scattering. However, in more complex waters, or when aerosol absorption is present at short wavelengths, traditional retrieval assumptions break down. The left-hand figure does not prescribe either an aerosol model or any surface spectral constraints, indicating that multiangle and polarimetric imagery provides the necessary additional information in the more general scenario. The right hand panel does not invoke any of the enhanced capabilities, including expanded wavelength range, expected of the PACE OCI, and therefore suggests a worse retrieval than could actually be obtained by the radiometer alone. Demonstrations that show the advantages of a MAP for atmospheric correction as applied to AirMSPI and POLDER are extremely promising, but still do not demonstrate how polarimeter data can be used to assist atmospheric correction of hyper-spectral radiometer measurements in complex coastal environments.
(1) At the simplest, they can improve our overall understanding of aerosol properties and distribution of aerosol type, which can be incorporated into heritage atmospheric correction lookup table and algorithm updates. (  2 ) MAP based aerosol retrieval could be performed prior to OCI atmospheric correction, and direct that algorithm to a particular aerosol type and/or provide the aerosol optical depth. (3) An atmospheric correction algorithm may be performed directly with MAP observations, and after spectral and geometric interpolation, applied to OCI. (  4 ) Both MAP and OCI observations are combined as inputs to a joint inversion. Implementation of any of these approaches would require that we overcome multi-sensor data fusion issues such as instrumental calibration, differing spatial resolution and varied geometry.
5 m -1 (440 nm) and 2 gm -3 , respectively, for the Case 2 waters. The fluorescence yield, which ranges from less than 0. 01 to 0. 10, was fixed at 0. 05. Aerosols were of maritime type, with optical thickness of 0. 2. The vertical profile of aerosol concentration was exponential with a scale height of 1 km. The TOA signal, including the coupling between oxygen absorption and aerosol scattering, was simulated with no noise using the quasi-single scattering approximation (a sufficient treatment to demonstrate feasibility). The HITRAN 2004 database was used to define spectroscopic parameters for oxygen absorption lines and compute oxygen transmittance. Absorption by water vapor was neglected. The simulations were performed for a single angular geometry, i. e. , solar and viewing zenith angles of 45°, and a relative azimuth angle of 90°.
As part of that effort, more hyper-spectral datasets are critical that provide a means to estimate reflectance of whitecaps over time and space under a wide variety of water types and whitecap conditions. New methods that seek to use the measured spectral information in near infrared wavelengths to estimate the contribution of whitecaps directly will provide a means to separate whitecaps from aerosols and other conditions where near infrared reflectance is non-negligible. Preliminary results have been conducted at the sea surface and more research is warranted to model the transmission of hyper-spectral reflectance from whitecaps through the atmosphere. Moreover, uncertainties need to be assessed when aerosol products are derived from at-sensor radiance that is "contaminated" by whitecaps and bubble plumes.
STRATEGY FOR ATMOSPHERIC CORRECTION The standard, 2-step approach to atmospheric correction of satellite ocean-color imagery has been used operationally by space agencies since the CZCS was launched in 1978 to process data from major large-scale ocean color missions into water reflectance. The approach has proved to be robust, sufficiently accurate, and useful for many applications, scientific and societal  (IOCCG, 2008). Its chief advantage over a variety of alternatives, as pointed out in sections Heritage Atmospheric Correction Algorithm and Alternative Algorithms, is that essentially minimal assumptions about water reflectance, the signal to be retrieved, are made. Algorithms have evolved and improved to account for new capabilities (e. g. , observations in the shortwave infrared to deal with turbid waters) and take advantage of new knowledge (e.
ACKNOWLEDGMENTS The PRISM flight over Santa Monica Bay was enabled by a  PRISM AITT grant  from  NASA  ESTO. We thank other sponsors including: the  NASA Earth Science Division  for the AVIRIS-NG instrument and the data analysis program Utilization of Airborne Visible/Infrared Imaging Spectrometer Next Generation Data from an  Airborne Campaign  in India NNH16ZDA001N-AVRSNG, managed by  Dr. Woody Turner ; the  Jet Propulsion Laboratory and California Institute of Technology President  and Director's Fund; the  Jet Propulsion Laboratory 20 Research and Technology Development Program ; and the  NASA Center Innovation Fund  managed in conjunction with the  Jet Propulsion Laboratory Office of the Chief Scientist and Technologist. RF acknowledges the technical support of  Mr. John McPherson ,  Scripps Institution of Oceanography.
Streamlines In References  [18, 28, [30] [31] [32] 40, 41]  the hydrodynamic streamlines were computed with the velocity field at a given time. In the present work, these streamlines are named static streamlines (SSL) and are computed with Matlab. In Section 3. 2, the instability structure is analyzed with the SSL. They are visualized in black in Figure  4  for example. In Section 3. 3, the real trajectories of the fluid elements and ions are computed with the numerical method described below. These trajectories will be named dynamic streamlines (DSL) owing to the use of the unsteady fluid and ion transfer. The trajectories are displayed in red in Section 3. 3. They are visualized in black in Figure  4  for example. In Section 3. 3, the real trajectories of the fluid elements and ions are computed with the numerical method described below.
Additional metrics, to be aligned for future use by stakeholders (e. g. , researchers, forecasters, and others), are also under consideration and development. The JCOMM in situ Observations Program Support Center (JCOMMOPS-www. jcommops. org) was established in 2001 and it provides a focal point of technical coordination across the ocean observing networks, encourages timely reporting and the conveyance of data and metadata, and develops appropriate tools for monitoring the status and reporting of the observing system. JCOMMOPS functions are critical for the integrative development, operations, tracking, and monitoring of the global system. In the recent years, JCOMMOPS has developed a new generation of Information System with an integrated metadata management and web applications. It has developed KPI to track network status and anticipated gaps.