dataesr/scientific-paragraphs-categorization huggingface dataset:
AUTHORS' CONTRIBUTIONS N. Rigoudy set up the collaborative network with S. Chamaillé-Jammes, organized data collection, and cleaned-up and managed the dataset with V. Miele. V. Miele developed the model and the graphical-user-interface. S. Chamaillé-Jammes initiated the project with N. Rigoudy and now manage it with V. Miele. B. Spataro provided assistance with the use of the LBBE PRABI computing cluster. All four make up the DeepFaune team. Other authors contributed data on their behalf or on behalf of their institutions. Perche), DREAL Centre-Val-de-Loire (PNR Perche), Fédéraction Départmentale des Chasseurs de la Marne (CERFE), Réseau SNCF (CERFE), Autoroutes SANEF (CERFE).
ACKNOWLEDGMENTS This work was performed using the computing facilities of the  CC LBBE/PRABI. Funding was provided by the  French National Center for Scientific Research (CNRS). This work has benefited from discussions initiated in the  Statistical Ecology Research Group (EcoStat)  of the CNRS. V. Miele would like to thank the  LECA laboratory  for having hosted him in Chambéry, and  L. Humblot  for insightful discussions about the GUI. The DeepFaune team would like to thank  E. Chetouane  for his contribution to the GUI. Also, for many partners, field technicians and students contributed largely to data collection, and they are acknowledged for their critical work. Some partners could not share images at the time of our study but actively participated in the discussions around future model developments:  PNR des Ardennes, PNR de Chartreuse, PNR du Haut-Languedoc , PNR du  Haut Jura ,  PNR du massif des Bauges, PNR de Préalpes d'Azur, PN de la Vanoise and PN des Calanques.
2018). Crucially, we trained our decoder on an independent dataset (EEG recorded while listening to an undistorted narrative) to test our prediction that stimulus reconstruction tracks the enhanced neural representation of sensory content as opposed to specific features of degraded speech. Finally, our study departs from previous reports by rigorously controlling the extent to which differences in sensory processing and neurophysiological activity can be ascribed to top-down mechanisms. Given the exquisite sensitivity of auditory cortical ensembles to spectro-temporal speech features  (Holdgraf et al. 2016) , exposure to clear speech might induce bottomup changes in cortical activity that facilitate subsequent encoding of the degraded stimulus. The present study avoids this potential confound by using visual information (written text) to instill top-down expectations about the linguistic content of degraded stimuli  (Wild et al.
Stimuli A total of 80 pairs of English sentences were constructed. These sentence pairs had similar (but not identical) grammatical structures and lengths (11. 7 words on average, range: 8-15). Each sentence was vocoded using Apple OS's noise-to-speech command "say" (voice: "Alex"; gender: male; sampling rate: 44. 1 kHz; rate: 200 words/min). Each vocoded sentence was approximately 3. 5 s long and was then concatenated 3 times to obtain audio files of ∼ 10. 5 s. The sounds were saved in the audio interchange file format (AIFF) and converted to the MPEG-1 Audio Layer III (MP3) format using the "Swiss Army Knife of Sound" (SoX) command line utility. We then used publicly available scripts written for PRAAT  (Boersma and Weenink 2011)  to turn clear speech into sine-wave speech (SWS) and noise-vocoded speech (NVS).
1A ). The amplitude of the degraded speech was equalized across all sentences and the duration was adapted to a fixed 10. 5 s interval using the VSOLA algorithm. In addition to these sentences, in the training session, we also played an audiobook (Cat-Skin from Grimms' Fairy Tales, LibriVox) to participants for a duration of 11 38. The properties of the speech (female voice, rate, etc. ) were not modified except for the overall volume (same volume as degraded sentences). All auditory stimuli were delivered using the Psychtoolbox extension (v3. 0. 14; Brainard 1997) for Matlab (R2018b; The MathWorks, Natick, MA, USA) running on Linux. The stimuli were played using speakers placed in front of the participant.
EEG acquisition and preprocessing The EEG was continuously recorded during both the training (audiobook) and test trials (noisy speech) from 64 Ag/AgCl EasyCap mounted active electrodes. The recording was acquired at a sampling rate of 500 Hz using a BrainAmp system in conjunction with BrainVision Recorder (v1. 21. 0402; Brain Products GmbH, Gilching, Germany). AFz served as the ground electrode and FCz as the online reference. Off line preprocessing was performed in MATLAB R2019b (v9. 7. 0. 1319299; The MathWorks, Natick, MA, USA) using custom-build scripts incorporating functions from the FieldTrip (v20200623;  Oostenveld et al. 2011) and EEGLAB (v2019. 1; Delorme and Makeig 2004)  toolboxes. For the training data, the EEG was segmented in a single epoch from 5 s before the start of the audiobook to 5 s after its end.
Pupillometry acquisition and preprocessing Eye-movements and pupil size on both eyes were recorded with a Tobii Pro TX300 system (Tobii Pro) at a sampling rate of 300 Hz. We recorded good-quality data in only 17 participants. One participant had incomplete data (43/96 trials). The eye-tracker was calibrated at the start of each recording. Blinks were detected as interruptions of the eye-tracking signal on each eye independently (maximum duration = 5 s). For each of these blinks, the pupil size was corrected by linearly interpolating the median signal preceding the blink onset ([-0. 1, 0]s) and following the blink offset ([0, 0. 1]s). The corrected signal was then low-pass filtered below 6 Hz (2-pass 4th-order Butterworth filter) and the pupil size for each eye averaged together. The continuous averaged pupil data were then epoched according to the presentation onset ([-1, 11]s) and both the 1st and 2nd presentation windows were baseline corrected using the average pupil size before the 1st presentation ([-1, 0]s).
Next, square-root compression was applied to the smoothed signals and the power in each frequency band was log-transformed. Finally, the auditory envelope was computed by summing the envelope of the 128 gammatone filters and downsampled to 100 Hz. For each presentation of the (training or test) stimuli, we processed the EEG recordings as follows: ICAcorrected data epochs were rereferenced to the average of all EEG electrodes, bandpass-filtered between 2 and 8 Hz using a 2-pass finite-impulse response (FIR) filter, and then resampled at 100 Hz. We trimmed the EEG epochs so that the start and end corresponded to the start and end of the stimulus presentation. We then used the multivariate temporal response function (mTRF) Toolbox (v2. 0; Crosse et al. 2016)  for Matlab to build a linear model between auditory and EEG signals from the training session (clear speech).
Time-frequency decomposition EEG data from test trials were subjected to spectral (time-frequency) analysis. Preprocessed datasets were rereferenced to the average of linked mastoids. Spectral power estimates were then computed for epochs spanning -2 to 12 s relative to stimulus onset over a frequency range of 1-30 Hz (1 Hz increments) using the ft_freqanalysis function (Hanning taper length = 1 s; 100 ms increments). As in the stimulus reconstruction analysis, time-frequency analysis was limited to the first iteration of each sentence presentation period (timepoints spanning [0. 5, 3] s; first and last 0. 5 s omitted to avoid spurious/confounding effects pertaining to sentence onset/offset and spectral leakage). Channel-level spectral power estimates were averaged across time for each trial, and averaged across trials for each factorial combination of sentence type, prior condition, and presentation order.
2013). Simpler random effects structures were selected when the maximal model failed to converge, generated a singular fit, or when random effects could be reduced in complexity without significant impairment of model fit  (Matuschek et al. 2017). Random intercepts were also specified for sentence items in all models; EEG electrode channel locations were included as random intercepts in the spectral power models only (see  Liebherr et al. 2021 , for a similar approach). Subjective clarity ratings following the 2nd presentation were modeled as ordinal data using (logit-linked) cumulative link mixed-effects models (i. e. proportional odds mixed-models). These models were fit via the Laplace approximation using the clmm function from the ordinal package (v2019. 12-10; Christensen 2019) in R. No assumptions about the distance between cut-point thresholds were specified.
Linear mixed-effects models for spectral power (averaged over time and frequency bins; first sentence iteration only) and stimulus reconstruction scores (first sentence iteration only) were fitted using the lmer function from the lme4 package (v1. 1-27. 1; Bates et al. 2015). In addition to the fixed effects described above (which were again introduced in a sequential fashion to enable model comparison), an ordered factor encoding the clarity rating on the 1st presentation was included as a covariate. Model diagnostics were assessed with the aid of the performance package (v0. 8. 0;  Lüdecke et al. 2021). The significance of main effect and interaction terms for each winning model was assessed using likelihoodratio chi-square tests from Type-II analysis-of-deviance tables obtained via the RVAideMemoire package (v0.
9-81; Hervé 2021) for the cumulative link mixed-effects models; equivalent tables were obtained from the car package (v3. 0-12; Fox and Weisberg 2019) for the linear mixedeffects models. Significant effects were disambiguated using posthoc contrasts (Tukey-corrected for multiple pairwise comparisons; Sidak-corrected for pairwise interaction contrasts) obtained from the emmeans package (v1. 7. 1-1; Lenth 2021), which was also used to estimate marginal mean predictions for model visualization. For completeness, β coefficients (log odds in the case of the cumulative link mixed-models) and standard errors (SEs) are reported alongside corresponding analysis-ofdeviance results for significant model terms. Model predictions and individual-level estimates were visualized with the aid of the tidyverse package (v1.
madagascariensis. This selection of extant species, although modest in terms of species numbers of Oxudercidae and Table  1. Species and specimens used in this study. Birdsong et al. [60] , Hoese and Gill  [28]  and Gill and Mooi  [15]  are used for morphology of many or all species. Colour codes for molecular data: Agorreta et al. [12]  = green; Near et al. [63]  = blue; Thacker et al. [10]  = red; for not color-coded cells see entries in Genbank via given accession numbers. Gobiidae, was sufficient to achieve a molecular 'backbone' as its phylogenetic analysis produced a well-resolved tree that fully agrees with published hypotheses (see Results). Ten fossil gobioid species were added to the extant taxon set (Table  1 ). We selected those that we had examined in previous works  [11, 42-44, 46, 62] , and added also the oldest known putative gobioid so far, †Carlomonnius quasigobius (Table  1 ).
Preparation of the molecular data matrix Molecular sequence data for extant species were assembled based on previously published sequences of five markers: rDNA (12S rRNA, tRNA-Val, 16S rRNA), cytb, rag1, zic1, and sreb2. The sequences were mainly from the study of Agorreta et al. [12] , supplemented by some data from Near et al. [63]  and Thacker et al. [10]. Molecular data were downloaded from GenBank, aligned in AliView 1. 26  [88]  with MUSCLE  [89] , followed by manual adjustment and exclusion of ambiguous regions where necessary. Individual gene alignments were then concatenated into a supermatrix (6271 bp) with SeaView 5. 0. 4  [90]. For details and GenBank accession numbers see Table  1. All data matrices are available on figshare (https://figshare. com/s/ed10b9a5ac382f856a20).
Phylogenetic analyses We conducted phylogenetic analyses for the extant species based on the morphological character matrix, the molecular supermatrix, and based on the combined molecular and morphological (total evidence) datasets. Adding all ten fossils to the total evidence character set resulted in a collapse of the molecular backbone (see Discussion for possible reasons). Therefore, we used a step-wise approach: (i) a single fossil species was added, (ii) two fossil species of the same genus were added, (iii) four fossil species were added. We used the Bayesian Markov Chain Monte Carlo (MCMC) approach implemented in MrBayes versions 3. 2. 6 and 3. 2. 7a [91] , as well as implied-weights maximum parsimony (IW-MP,  [92] ) implemented in TNT 1. 5  [93]  to infer phylogenies. In MrBayes, we assigned the Mkv+G model  [94, 95]  to the morphological data, and separate GTR+G models  [95, 96]  to each molecular partition.
Total-evidence approach. -The Bayesian phylogeny inferred from the total-evidence dataset (= combined molecular and morphological data) including only extant species is topologically identical to the molecular phylogeny but shows maximum support for Rhyacichthyidae and Rhyacichthyidae + Odontobutidae (1. 00 and 1. 00 vs. 0. 62 and 0. Phylogenies including extant and all ten fossil species. Morphological data. -In the Bayesian phylogeny, the 5brG clade (Gobiidae + Oxudercidae)-including the two fossil Gobius spp. -is recovered only with moderate support (0. 70), and internal relationships are again unresolved (Fig  5A ). The rest of the tree is topologically similar to the one based on the morphology of the extant species only (Fig 4B ), Thalasseleotrididae are now recovered with slightly higher support (0.
The two fossil Gobius spp. are included in the 5brG in a clade with Gobius niger, Discordipinna and Lesueurigobius. The two †Eleogobius spp. are sister to 5brG but monophyly of the genus remains unresolved. †Carlomonnius quasigobius is placed in a clade containing Eleotridae and Kribia nana (Butidae). †Pirskenius is monophyletic (64%) but its position, as well as that of the remaining three fossil species, is not further resolved. Total-evidence approach. -The Bayesian phylogenetic analysis of the total-evidence dataset including all 29 extant and the ten fossil species produced a consensus tree with a largely collapsed backbone (Fig  5B ) compared to the tree based on only the morphological data set of the same taxa (Fig  5A ). It contains numerous polytomies and shows poor support for many deeper nodes.
Total-evidence phylogenies including one to four fossil species. In the following analyses, a single fossil was added to the data set of the extant species and the trees were inferred based on the morphological and molecular data (total-evidence approach). †Carlomonnius quasigobius. -The Bayesian phylogeny is topologically identical to the molecular and the total evidence phylogenies based on extant species only; †Carlomonnius †Eleogobius brevis and †E. gaudanti. -The Bayesian phylogeny inferred from the total-evidence dataset including all extant species and either †E. brevis or †E. gaudanti (S14, S15 Figs in S1 File) is topologically identical to the molecular phylogeny, and almost no decrease of support values is seen. †Eleogobius brevis is recovered as sister to Gobius niger with good support (0.
Discussion In this study, we have assembled for the first time a dataset comprising molecular and morphological data for Gobioidei that encompasses both extant and fossil species. This approach was necessary as a phylogenetic analysis of the extant species based solely on their morphological characters could only resolve those clades for which morphological apomorphies are known, i. e. 5brG, Thalasseleotrididae, Thalasseleotrididae+5brG, and Eleotridae [see  15, 28] , while Butidae, Odontobutidae and Rhyacichthyidae each were recovered as paraphyletic. The overall objective was to investigate whether a fossil gobioid species can be confidently placed at family level in the tree of the extant Gobioidei using a Bayesian or Maximum Parsimony totalevidence phylogenetic approach. The results reveal mostly well supported placement at family level when a single fossil species is added to the total evidence data set of the extant species, especially in the Bayesian setting.
and †Pirskenius spp. was indicated in the Bayesian and Maximum Parsimony trees (S25, S26 Figs in S1 File), which is possibly due to their specific combination of plesiomorphic (e. g. seven spines in the first dorsal fin, nine anal-fin rays) and apomorphic traits (e. g. 12 abdominal vertebrae, presence of interneural gap). Our study also yields some new insights from a methodological point of view. Adding morphological data from only extant species to the molecular dataset had practically no influence on the tree topology and support values (S3 Fig in  S1 File ). Likewise, the inclusion of a single fossil or of two congeneric species did not change the tree topology, only sometimes some support values decreased (Figs  6 7 8 ). However, when all fossils were included in the total evidence phylogenetic framework, the resolution of relationships between families and most fossil taxa dramatically collapsed (Fig  5B ).
Conclusion We have presented a total-evidence dataset comprising molecular and morphological data of 29 extant gobioid species representing all families. Bayesian and Maximum Parsimony analyses revealed that this dataset is sufficient to achieve a molecular 'backbone' that fully conforms to previous molecular work. The new dataset can be used to analyze the family assignment of fossil skeletal-based gobioid species using Bayesian and Maximum Parsimony total-evidence phylogenetic approaches, which has not been possible before. Our phylogenetic analyses confirmed the family assignment of those fossil gobioid species for which such a placement had been proposed in previous works. It is thus evident that comparative morphology remains an appropriate method to classify some gobioid fossils. However, our phylogenetic analyses could also suggest relationships of fossil gobioid species for cases where the comparative approach did not yield conclusive results.
Acknowledgments For providing technical assistance and access to specimens from the SNSB-ZSM collection we thank  D. Neumann  and  U. Schliewen , respectively, (both  SNSB-ZSM, Munich, Germany ), with special thanks to the latter for help in the acquisition of specimens and insightful discussions. We are grateful to  E. Bernard  ( NHMUK, London, UK ),  S. Merker  ( SMNS, Stuttgart, Germany ),  M. Parrent  ( MRAC, Tervuren, Belgium ), and  T. Přikryl  ( Charles University, Prague, Czech Republic ), who all helped to study the specimens kept in the collections of their institutions. Sincere thanks go to  W. Schwarzhans  ( Hamburg, Germany ) for providing photographs of the otoliths of Lesueurigobius sanzi, Asterropteryx semipunctata, Eucyclogobius newberry, Thalasseleotris iota and Dormitator maculatus (shown in Fig 3) and  D.
However, the more variants are shared, the more identifiable the data are, raising the possibility that individuals may be identified according to specific haplotypes. Other obstacles to routine variant sharing across laboratories include practical technical reasons (not everyone shares and stores data in the same way) and time (the need to keep the database up to date and curated, with someone to take responsibility for any discrepancies). Variant sharing is also predicated upon using the same nomenclature (e. g. , Human Genome Variation Society [HGVS]) and reporting against the same transcripts. In cases of multiple transcripts, the specific 'disease transcript' must be used, but this is not always known. Laboratories should make reasonable efforts to ensure that the transcript they are using is expressed in erythroid cells.
AC K NOW L E D GE M E N T S The BSH General Haematology Task Force members at the time of writing this good practice paper were Mamta Garg, Charlotte Bradbury, Emmy Dickens, Savio Fernandes, John Grainger, Ciaran Mooney, Noémi Roy, Barbara Delasalle, Nicola Svenson, Sara Stuart-Smith and Suzanne Docherty. The authors would like to thank them, the BSH sounding board, and the BSH guidelines committee for their support in preparing this good practice paper. The authors would also like to thank the EHA Guidelines Committee as well as EHA Executive Office for their support in preparing and publishing this good practice paper. Noémi Roy, Lydie Da Costa, Roberta Russo, Paola Bianchi, Maria del Mar Mañú-Pereira, Elisa Fermo, Immacolata Andolfo, Barnaby Clark, Melanie Proven, Mayka Sanchez, Richard van Wijk, Bert van der Zwaag, Mark Layton, David Rees and Achille Iolascon all took part in the data synthesis and writing of the guidelines.
, 2008; de Sousa Mendes et al. , 2022). The lack of standardization of wet chemistry methodologies  (Viscarra Rossel and Bouma, 2016)  coupled with situations of low data availability, or socalled low data regimes, also have long caused calibration models overfitting small and often noisy datasets  (Torres Astorga et al. , 2018). To avoid such overfitting often involves careful data pre-processing and dimensionality reduction  (Viscarra Rossel et al. , 2022) , which are all pervading themes in MIRS soil data processing and statistical learning in general  (Hastie et al. , 2009). The situation of data scarcity is, however, changing rapidly today with the availability of large, and growing, high-quality and less region-specific libraries of MIR-scanned soil samples. A strong example is the spectral library maintained by the National Soil Survey Center (NSSC) Kellogg Soil Survey Laboratory (KSSL) from the United States Department of Agriculture (USDA-NRCS), which is available to the global scientific community.
, 2019b; Wijewardane et al. , 2018) , in particular when the relationship between the spectra and these properties is non-linear as for instance for K ex. This inferior performance is known to be related to the fact that this soil property has no direct or strong spectral features to support the calibration model and is only inferred in relation to other spectrally active soil components  (Janik and Skjemstad, 1995; Sanderman et al. , 2020). However, if such a relationship exists and can be learned, it is necessary to have classes of models such as CNN capable of (i) extracting indepth information from the spectra for better calibration purposes and (ii) exploiting a large and diverse spectral library. To the best of our knowledge, the estimation of K ex at a level of performance sufficient for nuclear remediation using a large MIR spectral library and a CNN algorithm has never been tested until KSSL database has been made available to the public recently.
Our second hypothesis is that CNN-based soil property prediction benefits from merging MIRS information from a wide range of soil types (Soil Taxonomy Orders), even when data are not equally distributed across all orders. By merging, soil orders with a low amount of data in the KSSL dataset also benefit from the CNN leaning process, reusing, when possible, spectral features learned on well represented soil orders. Such a feature would provide a major opportunity to address the lack of data on the fate of radionuclides in poorly explored environments such as arid, tropical and monsoonal climates. Therefore, in this study, our first aim is to assess the ability of CNN models to predict K ex in soil when trained on a large MIRS spectral library with a sufficient level of accuracy for the remediation of radioactive contamination of agricultural land.