dataesr/scientific-paragraphs-categorization huggingface dataset:
Importantly, all these data are freely available to the scientific community in the NASA SeaBASS public database (seabass. gsfc. nasa. gov, [25] ). The main objective of this study was to answer the following question. Can we accurately estimate Chl-a from c p measurements using few bands in the red spectral region and over a wide range of Chl-a values? Below we will show that indeed deep-learning neural networks can be trained to accurately estimate Chl-a from c p measurements at three bands in the red spectral region. These results open the way to new applications of c p measurements in natural waters, including the potential to develop new instrumentation to estimate Chl-a in situ.
Data description To train the deep neural network a large dataset of particulate beam attenuation coefficients (c p ) and absorption-based chlorophyll-a (chl-ACS) estimates was used. c p and particulate absorption data collected during numerous expeditions and processed as previously described  [9, 11]  were extracted from the NASA SeaBASS archive (Table  1 ,  [25] ). Due to missing chl-HPLC or missing c p data, only the following legs of the Tara Oceans expedition were used: AbuDhabi-Masqat, Alger-Barcelone, Ba-Ushuaia, Beyruth-Alexandrie, Ct-Rio, Easterisland-Guayaquil, Honolulu-SanDiego, Madagascar-Mayotte, Male-StBrandon, Masqat-Mumbay, Mauritius-Reunion, Mayotte-CT, Moorea-Honolulu, Mumbay-Male, Panama-Savannah, Reunion-Madagascar, Rikitea-Papeete, Rio-Ba, Sandiego-Panama, Savannah-NewYork, Sharm-Jeddah, Tanger-Alger and Valparaiso-EasterIsland.
Data division The c p and chl-ACS datasets described in section 2. 1 were divided into three separate datasets: training, validation and independent validation. All the datasets in Table  1 , excluding Tara Oceans, were divided into the training and validation datasets. The training dataset was used to train the model. The validation dataset was used to validate the model once it had been trained, however, as the data in the validation dataset came from the same expeditions as the training dataset, it was not considered truly independent. To independently validate our model, the Tara Oceans dataset was used, which consisted of data from an expedition in areas which were not included in the training dataset. The spatial and temporal distribution of the datasets can be seen in Figs. 2  and 3.
The training and validation datasets were divided by applying a stratified sampling: the initial dataset, excluding the Tara Oceans expedition, was divided into 500 log-scaled bins of chl-ACS between 0. 004 mg/m 3 and 9. 28 mg/m 3. Then, based on the observed maximum number of samples available in the bin with the fewest samples, samples were randomly extracted from each bin to generate the training dataset. Note that the bins at the extremes of the Chl-a range had fewer than 100 points, because fewer measurements were available. Finally, to ensure that each bin was represented in the validation dataset, when the number of samples within a bin was less than 60, a single randomly selected sample was moved from the training dataset into the validation dataset.
1 ). The width of the spectral ranges was limited by computational costs. A spectral width of 6 nm (4 different values for each specific range) was found to be suitably representative of the spectral features, while not requiring too many additional computational resources. The experimentation consisted of training models using several ranges around the spectral features and observing their bias and precision, until the wavelength ranges above were reached. Thus the initial training data set contained in each row the three c p values selected from the three wider λ 1 , λ 2 and λ 3 ranges as well as their corresponding chl-ACS value. This initial training dataset was then augmented as follows. The absolute value of c p varies due to different factors besides Chl-a  [15] , while the imprint of chlorophyll-a absorption on c p results in specific spectral features (Fig.
In the Huber loss function, α determines which part of the loss function is used based on the error, defined as |yf (x)| (see Eq. (  6 )). An α value of 1. 5 was selected for this model, which meant that if the error in the model was less than 1. 5 mg/m 3 then the MAE loss function was applied, and else the MSE was employed. This α was selected through experimentation and found as the best balance between robustness against outliers, that were especially important at lower chl-ACS concentrations, and accuracy. The model was tuned with the training dataset for 200 epochs, where one epoch is defined as one forward and backward training iteration of the network. Early stopping with a "patience" of 70 was used, which stopped the tuning if an improvement on the current best loss function was not reached for 70 consecutive epochs.
Validation dataset results The neural-network was successful at predicting Chl-a across a wide range of conditions. A strong relationship was found between chl-ACS in the validation dataset and the 9 neural-networks selected for the ensemble. Figure  7  shows the predicted chl-CP using λ 1 = 670 nm, λ 2 = 688 nm, λ 3 = 710 nm for one of the models selected for the ensemble. The prediction bias (-3%) and precision (23%) remained relatively consistent across the whole range of chl-ACS. The results of only one model rather than the ensemble are presented here as each of the 9 models in the ensemble had a different training-validation split as described in section 2. 2. The results of the remaining 8 models which formed the ensemble closely resembled the results in Fig. 7 , with a median bias of -7% and median precision of 23% across the 9 models.
Independent-validation dataset results Ensemble predictions (using c p at λ 1 = 670 nm, λ 2 = 688 nm, λ 3 = 710 nm) also compared favourably to observations for the independent-validation dataset (with +2% bias and a precision of about 22%). This result suggests that the ensemble can predict Chl-a regardless of the season or region in which c p is sampled (Fig. 8 ). However, the accuracy of the model appeared to degrade at lower chl-ACS concentrations (<0. 05 mg/m 3 ). On the other hand, the model was still able to accurately predict chl-CP at chlorophyll-a concentrations >3 mg/m 3. The relative prediction uncertainty, computed from the precision of the ensemble models, was <20% from the highest chl-ACS concentration down to about 0. 05 mg m -3 (Fig. 8(C )), indicating that at these concentrations model predictions could be trusted.
However, at chlorophyll levels below 0. 05 mg m -3 the uncertainty approached 100%, suggesting lower accuracy for these predictions, consistent with the validation data (Fig. 8(B) ). Figure  9  presents the relative bias and relative precision obtained for the independent-validation dataset when training the ensemble with different λ 1 and λ 2 combinations, while keeping λ 3 fixed at 710 nm. Even in the narrow ranges of wavelengths we selected, there were specific pairs of λ 1 and λ 2 that resulted in significantly more accurate predictions. Specifically combinations of longer λ 1 and λ 2 appeared to produce less biased results. On the other hand, Fig. 9 (B) shows that, regardless of λ 1 and λ 2 , the relative precision of the models remained consistently around 0. 22.
Use of a neural network Devising a mechanistic model to predict chlorophyll-a concentration (Chl-a) from three spectral channels of the particulate beam attenuation (c p ) is challenging because, besides the absorption by chlorophyll-a, multiple other factors simultaneously affect c p. We attempted this task with a deep neural network and were successfully able to predict chl-CP with a minimal bias. We believe the success of this method is due to the ability of the network to disentangle from the c p spectra the information about the absorption by Chl-a. This ability most likely arises from the flexibility of the neural network and the very large training dataset that we assembled. The model ensemble was based on randomising the initial weights of each model and on random sampling of the training dataset.
Regional and seasonal distribution of independent-validation dataset The results from the independent-validation dataset in Fig. 8  show that the network also performed well in geographic regions it had not been exposed to during training (Fig. 3 ). Therefore, the network was successfully trained to avoid regional or seasonal biases. Nonetheless, the results presented for the independent-validation dataset (Fig. 8 ) were somewhat noisier, (i. e. , with a larger bias at lower Chla concentrations) than those for the validation dataset (Fig. 7 ). We hypothesise that an important reason for this discrepancy may be that the independent-validation dataset was collected during the Tara Oceans expedition. During this expedition, the HPLC samples were collected by personnel who had received minimal training in water sampling and filtration and sometimes samples were shipped in sub-optimal conditions to the laboratory that conducted the HPLC analyses (E.
Boss, personal communication, 2020). In addition, 2 liters of water were sampled for HPLC analyses  [27] , but this water volume is probably too low to accurately determine Chl-a in oligotrophic waters. Furthermore, Tara Polar crossed many land sources which could have resulted in very different c p spectra from increased mineral concentrations. This line of reasoning could help in explaining the larger noise in Fig. 8  and the poorer statistics for Tara Oceans and Tara Polar in Table  1. Importantly, we do not mean to discredit the monumental efforts spent collecting and sharing the precious data on the Tara expeditions: we are simply trying to provide a plausible explanation for the larger noises observed for these datasets.
Performance of model at lower chl-ACS concentrations The model bias increased at lower chl-ACS concentrations (Figs. 7  and 8 ). The bias in Fig. 7  is interesting, because the network should have been more robust when validated against data from datasets that it was trained on. This bias could be due to the intrinsic noise of the ACS absorption measurements and to the lack of features in the spectral c p at very low chl-ACS concentrations (Fig. 1 ). As the chl-ACS concentration decreases the c p curve becomes smoother and loses the features that the neural network relies on to predict chl-CP. In addition, in clear waters the chl-ACS estimates approach their background noise, which is determined by the precision of the a p data  [11]. The relatively higher bias in Fig. 8  at very low concentration of Chl-a is due to samples from the South Pacific subtropical gyre that presents the clearest waters on the planet and specific phytoplankton assemblages.
This extremely low Chl-a values and ocean region were missing from the training database. This drawback could potentially be improved upon by re-training the models using additional independent measurements collected in these ultra-oligotrophic waters. The increase in prediction uncertainty at the same low Chl-a (Fig. 8(C )) is also most likely a result of the phenomena described above. As a result of these factors, the variability among model predictions increased at the lower chlorophyll concentrations causing the increase in uncertainty. This demonstrates that associating an uncertainty with every chl-CP prediction is important to decide the extent to which the predicted value can be trusted.
Performance of model compared to absorption A further test was performed to compare the performance of the attenuation-based chl-CP estimates against the absorption-based chl-ACS. For this test the chl-ACS was not de-biased. The chl-CP and chl-ACS were independently validated against HPLC measurements taken from the Tara Oceans expedition. chl-ACS achieved a bias of +14% and precision of 69%, whereas chl-CP achieved a bias of +1% and precision of 68%. Despite this difference in biases, due to the relatively large variability of the Tara Oceans dataset (Table  1  and Fig. 5(B )), we can only conclude that the two methods did not differ considerably, likely because the chl-CP method was trained using the chl-ACS data.
Information on the datasets used to train and validate the neural network, as well as summary statistics of the chl-ACS datasets before debiasing:"N. HPLC" and "N. ACS" are the number of Chl-a values in the HPLC and ACS datasets, respectively; "Range" is the range of Chl-a in the ACS dataset; δ and σ are the median and the robust standard deviation of the relative residuals between chl-ACS and chl-HPLC, respectively, before we subtracted the bias from each relationship (Eq. (3)). a and b are estimates of the parameters fitted to the equation chl-HPLC = aL b , where L = 0. 014 × chl-ACS n (see Eq. (1)). Numbers in brackets after a and b are their 95% confidence intervals.
Acknowledgments We would like to thank all the investigators who have collected and deposited their data in the  NASA SeaBASS database. We would also like to thank  E. Boss  and three other anonymous reviewers who provided constructive criticisms to an earlier draft of this manuscript. The Atlantic Meridional Transect is funded by the  UK Natural Environment Research Council  through its  National Capability Long-term Single Centre Science Programme, Climate Linked Atlantic Sector Science  (grant number  NE/R015953/1 ). S. Graban  and  S. Goult  acknowledge the  NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS)  for financial support. Computing time to train the model on the MAssive GPU cluster for Earth Observation (MAGEO) cluster was provided through NEODAAS. This study contributes to the international  IMBeR  project and is contribution number 351 of the AMT programme.
We also acknowledge that  NASA   Ocean Biology and Biogeochemistry program  grants  NNX09AU43G ,  NNX11AQ14G ,  NNX13AC42G ,  NNX15AC08G ,  NNX15AE67G ,  NNX10AT70G , and  NNX15AF30G  that funded the research expeditions during which many of the datasets used in this work were collected.
Data Availability The dataset containing all data on latitude, longitude, time, c p , and chl-ACS can be found at: https://zenodo. org/record/3953725#. XxmOtZ5KiUk The code and instructions required to estimate Chl-a from c p measurements using the ensemble of tuned neural networks can be found at: https://github. com/SebastianGraban/CMAP.
SEISMIC VELOCITY MODELS We compute seismic velocities at each location by using the temperature distribution of 130 thermal evolution calculations in 3D geometry for one crustal  [11]  and two mantle compositions  [12, 13]. Crust: Composition: Taylor & McLennan, 2009  [11]  Burnman  [14]  + SLB11 mineral database  [15]  Mantle Composition: Taylor 2013 (TAY13,  [12] ) vs. Yoshizaki & McDonough 2020 (YOS20,  [13] ) Perple_X  [16]  + SLB11 mineral database The seismic velocities are computed at each location by using the temperature distribution of the 3D models and pre-computed Perple_X tables. https://agu2020fallmeeting-agu. ipostersessions. com/Default. aspx?s=99-0F-61-E3-00-17-A0-24-A4-42-8E-FE-ED-E4-07-81&pdfprint=true&guestview=true 5/14.
RAY TRACING CALCULATIONS We use the TTBOX package  [17, 18]  to calculate ray tracing for the entire set of models. We apply the following steps: 1. Look for negative velocity gradients. 2. Compute P and S ray paths for a distance range from 5° to 65°. 3. Shoot P and S rays to the location of S0173a (29°) and S0235b (26°) events. This will not be possible if shadow zones are present at these distances. 4. Compute P and S travel time curves for distances between 0° and 45° to estimate the extent of possible shadow zones. We compute ray tracing for the entire set of 260 models (130 thermal mocles and 2 mantle compositions). We calculate 1D seismic velocity profiles by averaging our data along a great circle segment between InSight landing site and Cerberus Fossae. The source depths chosen for our analysis are 50, 100, and 150 km, since neither of the events shows a discernible surface wave train.
, 2018; Vidal-Melgosa et al. , 2015) , aim to rapidly identify microbial strains with high biocatalytic potential. Putative enzyme functions/specificities can also be predicted using bioinformatics pipelines, and a huge diversity of sequence-based data has been gathered in expert databases dedicated to the display and analysis of genomic, structural and biochemical information related to specific enzymes acting on polysaccharides, proteins and lipids. These combined efforts have produced a large and diverse set of enzymes that can be used as tools to investigate bioproduct structure and composition, and that needed to be databased. Carbohydrate-active enzymes (CAZymes; CAZy database; www. cazy. org) have been grouped based on comparisons of amino acid sequence, structure and catalytic mechanism.
Proteases (EC 3. 4; The MEROPS database; http://www. ebi. ac. uk/merops/) are another important class of enzymes involved in metabolism (protein trafficking, nutrient digestion, immune regulation, etc. ) and employed in industrial processes. Lipases and esterases (E. C. 3. 1. 1. 3; Lipase Engineering Database; http://www. led. uni-stuttgart. de) are other ubiquitous enzymes that play a central role in lipid Fig. 1. Illustration of the concept of enzymes as probes for exploring the structure and chemical composition of complex bioproducts. Using enzymes as probes hinges on first knowing the physical-chemical properties, catalytic properties and specificity properties of the enzymes. Relevant data is then obtained by evaluating the effect of their catalysis, their interactions, and their dynamics in bioproducts as substrates, all from nanometric to macroscopic scale.
) to reach 5D/6D datasets. The acquisition of this kind of data has become possible with X-ray tomography in synchrotron facilities and with instruments available in labs enabling the observation of mm-to cmscale samples. However, dealing with bioproducts in water-buffered systems is still a challenge. Other photon microscopy techniques can handle such systems, but are generally suitable to only observe thin samples-the ability to work with thick samples would represent a significant technological leap forward  (Susaki and Ueda, 2016). Macroscope tools ('stereomicroscopes') and digital microscopes that allow macroscopic observations can help to acquire knowledge on the macroscopic structure of biological tissues. The samples can be observed without preparation, and the large depth of field of the instruments allows dynamic observation of several-centimetre samples in 3D.
This approach has already been applied in research on the mechanical behaviour of wood tissue  (Perré et al. , 2016). Finally, management of multimodal and multiscale datasets requires new algorithms to be implemented in bioproduct models, including key chemical, structural and spectral information to predict reactivity to enzyme action. Finally, EP science needs to look at the bigger picture so that synergies can be exploited to better investigate the structure of complex bioproducts and generate results that are transferable to industrial processes without overly expensive protein purification. In this context, it is essential to consider all the steps of the process before and after the catalytic step involving enzymes, in order to optimize the expected product yields and minimize the release of unwanted co-products (including inhibitors/inactivators).
Acknowledgements This work is supported by the  international M-ERA. NET  project  SIOX  (project  4288 ) and  H2020  project  ULPEC  (project  732642 ). M. S. acknowledges funding from  Slovenian Research Agency  (Grants No. J2-9237  and No. P2-0091 ). This work received support from the  ERC CoG MINT  (# 615759 ) and from a  PHC Van Gogh grant. M. B. thanks the  French Academy of Science  and the  Royal Netherlands Academy of Arts and Sciences  for supporting his stays in the Netherlands. This project has received funding as a transnational access project from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  823717 -ESTEEM3. N. G. and J. V. acknowledge  GOA  project " Solarpaint " of the  University of Antwerp.
Acknowledgements J. D. and S. S. contributed equally to this work. The  ESRF  and  NWO  are acknowledged for allocating the beam time at the  Dutch-Belgian beamline (DUBBLE, ESRF, Grenoble ) for the GIWAXS experiments. The authors are grateful to the  DUBBLE team  for their help during the beam time. Loredana Protesescu  and  Graeme Blake  are acknowledged for insightful discussions about the paper. S. K. is thankful for a research fellowship (Grant No. 408012143 ) awarded by the  Deutsche Forschungsgemeinschaft (DFG). G. P. acknowledges the  Zernike Institute for Advanced Materials  for the startup funds. J. D. and G. P. are grateful to the  China Scholarship Council.
2011 ). However, using molecular analysis in phytoseiid mites is not so easy  (Jeyaprakash and Hoy 2002)  and there are also some critical views to public GenBank database due to misidentification, contamination and nomenclatural errors/updates  (Tixier et al. 2011). To overcome this problem, the combination of molecular and morphological approaches (by DNA extraction of single mites and recovering the carcass of mites after DNA extraction) can be used  (Jeyaprakash and Hoy 2010; Tixier et al. 2010b). Another key point for molecular identification is to characterise intraand inter-specific variation to propose decision rules  (Tixier et al. 2017 (Tixier et al. , 2019)). The challenge is thus to obtain more sequences for more species and populations collected in different localities. Only two DNA sequences (ITS) of a species of Phytoseiidae from Turkey are present in GenBank (Kampimodromus ragusai Swirskii & Amitai;  Döker et al.
Data and phylogenetic analysis The sequences obtained were submitted to the Basic Local Alignment Search Tool (BLAST) in GenBank to check for potential contaminations (other mite families, insects, Human DNA …). Because of high identity level, all specimens herein considered belong to the family Phytoseiidae. Furthermore, as DNA sequences of the seven species are included in GenBank (and mainly deposited by one of the authors of this article), the blast process allowed to assign without any doubts the present DNA sequences to the correct species name. To build the phylogenetic tree, the sequences of the species herein obtained and some retrieved in the public GenBank database were also used. The GenBank sequences considered were mostly deposited by M-S Tixier, co-author of this paper. The sequences obtained were cleaned using BioEdit v.
Identification and phylogenetic tree After alignment, the sequences had a total of 610 nucleotides (including gaps), used for phylogenetic tree construction, and 263 out of the 610 nucleotides were conserved. The aligned sequences had 42. 8% GC-content (57. 2% AT), on average. 34 ITS sequences were obtained: Amblyseius andersoni (Chant) (one specimen), Amblyseius swirskii Athias-Henriot (2 specimens), Euseius finlandicus (Oudemans) (19 specimens), Kampimodromus aberrans (Oudemans) (3 specimens), Neoseiulus californicus (McGregor) (6 specimens), N. reductus (2 specimens) and Phytoseiulus persimilis Athias-Henriot (one specimen). Accession numbers of sequences in GenBank database are included in Table  1. The phylogenetic tree based on ITS-region is presented in Fig. 2. The phylogenetic tree structure is similar to the ones obtained by  Tsolakis et al.
(2012)  and dos Santos and Tixier  (2016, 2018) , as almost all sequences are issued from these works. The genus Euseius seems to be paraphyletic as it should include the genus Iphiseius. The genera Neoseiulus and Amblyseius seem to be polyphyletic. All the specimens of the seven species herein considered are included in clades containing specimens of these species, showing a correct assignation and identification using this molecular marker. The genetic distances between the DNA sequences herein obtained and those of specimens of the same species retrieved from GenBank are very low (0-0. 77%; Table  2 ). It is interesting to note that some interspecific genetic distances are sometimes very low especially between N. fallacis and N. californicus (0. 9%) and between P. persimilis and P. macropilis (0.