dataesr/scientific-paragraphs-categorization huggingface dataset:
The English Channel/European Atlantic coast In the Pertuis Charentais dataset, there is a significant positive correlation between Foram-AMBI and OM, and between AMBI and OM (R 2 = 0. 67 p = 0. 001 and R 2 = 0. 66 p = 0. 001; Fig. 7A  and 7B ; respectively). All foraminiferal species occurring at the sampling sites are assigned in EGs. Foram-AMBI ranged from 1. 9 to 3. 3 (Fig. 7A ) and AMBI from 1. 2 to 4 (Fig. 7B ). They are significantly correlated (R 2 = 0. 56, p < 0. 01; Fig. 7C ). Based on Foram-AMBI, EcoQS varied between good and moderate (Table  8 ). Both indices agreed that the best EcoQS was at the reference sampling station R1. Assessments of EcoQS derived from Foram-AMBI and AMBI partially match but were in some cases contradictory. There was 50% of full agreement between the two indices derived-EcoQS (Fig.
Assignment of benthic foraminifera from intertidal and TWs to EGs according to TOC response patterns It is generally accepted that EcoQS obtained with AMBI (and Foram-AMBI) are valid only if species unassigned to EGs account for less than 20%  (Borja and Muxika, 2005). In the two validation tests of the Foram-AMBI, there were virtually no unassigned species in the Pertuis Charentais dataset, and only 16. 9% in the Harbor of Cagliari, warranting the validity of the EcoQS assessment. This suggests that enough species from intertidal and TWs have been assigned to test the lists over a large spatial scale in Europe. For decades, increased supply of OM to coastal waters has been considered to have a negative effect on ecosystems  (Pearson and Rosenberg, 1978). As a consequence, TOC has become a typical indicator of environmental stress.
Test of the species' assignments In both validation datasets, Foram-AMBI yielded a positive significant correlation with organic matter content indicating that this sensitivity index calculated with the present study lists reflects the change in EcoQS along an OM gradient. In the Pertuis Charentais, the un-impacted station R1 had the lowest value, indicating a higher proportion of sensitive species. It was therefore possible to discriminate between this reference station and the impacted ones. Note that all stations are situated at the same level in the tidal zone and in similar habitats, so that differences in EcoQS are not due to the natural features of each of the sampling stations. In the harbor of Cagliari, Foram-AMBI adequately distinguished between high and good EcoQS. In the Pertuis Charentais, a significant positive correlation between Foram-AMBI and AMBI values (based on macrofauna) confirmed previous correlations found between foraminiferal and macrofaunal communities and indices  (Alve et al.
It appears relevant to keep organizing benthic foraminifera taxonomical workshops to intercalibrate the different taxonomical schools. Since taxonomy is still mostly based on morphological criteria, taxonomical names used for the same foraminiferal species are rather inconsistent between researchers. For marine species, the WoRMS database provides an authoritative and comprehensive list of marine species including also information on synonymy. It is continuously revised, ensuring that it is kept up-to-date. Benthic foraminifera are included in the database  (Hayward et al. , 2020a) , but foraminiferal studies rarely refer to it, unlike researchers working on benthic macro-invertebrates, for instance, for whom WoRMS is the database of reference for species names. It was therefore a true challenge to "homogenise" species names between studies to build up an accurate and reliable master table for this present work.
To clearly and easily compare different foraminiferal studies, similar species concepts and consistent use of names are necessary. At the moment, WoRMS seems to be a relevant option to archive accepted scientific names (and associated synonyms) of benthic foraminiferal species. However, to ensure a high quality and to obtain the acceptance of the WoRMS database by a larger part of the community, the section concerning foraminiferal species needs the input of much more researchers working on foraminifera  (Hayward et al. , 2020b). Additionally, molecular analyses and a detailed morphological analysis based on SEM images of sequenced specimens have to be combined to confirm or refute the taxonomical identifications. The contribution of molecular studies has yet to be implemented in WoRMS.
requirements. Furthermore, this study does not ensure that Foram-AMBI and comparable methods will perform in all European waters. There is hence an urgent need to apply and test sensitivity-based foraminiferal indices in different ecosystems and against different pollution sources. Otherwise, the community of foraminiferologists also suffers from severe taxonomical heterogeneity. A great effort must be devoted in the near future to encourage intercalibration exercises. Datasets from the Mediterranean Sea coast used for species assignments. Location of the different areas is shown on Figure2. TOC: total organic carbon, LOI: loss of ignition. Assigned species from the English Channel/European Atlantic intertidal and TWs per ecological group (EG). For each species, accepted scientific name and AphiaID (source: WoRMS), optimum and tolerance range to TOC are also reported.
Acknowledgments The authors are grateful to  Elisabeth Alve  and  Frans J. Jorissen  for their comments on earlier versions of the manuscript that greatly helped to improve it. Maria-Belen Sathicq  helped V. M. P. B. in handling the optimos. primeR package. Jean-Pierre Debenay  is thanked for his help and advices at the very early stage of this work. The authors are grateful to the  Swiss National Science Foundation , the  Agence de l'Eau Artois-Picardie , the  Communauté d'Agglomération du Boulonnais , the  Université de Lille , the  Université du Littoral Côte d'Opale , the  Laboratoire d'Océanologie et de Géosciences  for their financial support to FOBIMO workshops in Fribourg (Switzerland),  Wimereux (France)  and  Texel  (The Netherlands); and to  Silvia Spezzaferri  and  Henko de Stigter  for organizing and hosting the workshops in Fribourg and Texel.
Acknowledgments This review is based upon work from the  Work Group5  of the  COST Action   CA18116  (Aniridia: networking to address an unmet medical, scientific, and societal challenge), supported by  COST (European Cooperation in Science and Technology). The  European Joint Programme on Rare Diseases  ( EJP RD 2020 ) is acknowledged for its support to NL, LL, DA, MJC,  NS, RAP (AAK-INSIGHT). The work of  LL  and  NS at the Dr. Rolf M. Schwiete Center for Limbal Stem Cell and Congenital Aniridia Research at Saarland University, Germany  was supported by the  Dr. Rolf M. Schwiete Foundation.
Background Some of the most popular applications in bioinformatics, including multiple sequence alignment [1], sequence database search [2] and protein family profiling  [3, 4] , employ sequence weighting schemes as a way to mitigate the effects of non-independence of homologous sequences. For example, a database may contain many closely related sequences from one species (like humans) and its close relatives, while other more distantly related species might be under-represented. To address possible problems associated with these biases, several sequence weighting schemes have been proposed over the.
Other sequence weighting schemes have also been proposed, although they have seen fewer applications. Maximum discrimination sequence weighting  [11]  is a complex approach that aims to optimally distinguish homology from chance alignments in database searches. Henikoff and Henikoff  [12]  proposed a method that splits sequences into clusters based on sequence similarity, and assigns equal weights to sequences in the same cluster and a total weight of 1 to each cluster  [12]. Vingron and Argos weighted sequences proportionally to their average distances from all other sequences  [13] ; Sibbald and Argos proposed an approach in which a sequence receives more weight if it is more isolated in sequence space  [14]. Altschul et al. measured evolutionary correlations among sequences using branch lengths in the phylogeny, and then calculated sequence weights using the inverse of the variance-covariance matrix  [15] ; Gotoh developed a fast approximation of this method  [16].
As shown by the examples above, this new weighting scheme has a number of possible applications, from gene family profiles and multiple sequence alignment evaluation to ecology and conservation biology. The aim of our work is to provide a new sequence weighting scheme that is robust to the choice of application and scenario, therefore giving the possibility of improving the accuracy of these bioinformatics tasks, while at the same time being sufficiently computationally efficient to be used on large datasets. As an example, we focus on the task of inferring character frequencies at an alignment column. Inference of character frequencies is not only important for gene family profiling, but also for modeling evolutionary fitness, calculating conservation scores, and visualizing sequence logos  [34] [35] [36] [37] [38].
Calculating PNS scores via simulation We can calculate PNS by simulating sequence evolution along φ. If we are interested in weights w s , at each iteration we start by sampling a root character from the equilibrium distribution. We then sample its descendant characters and the mutation events along the branches of φ using standard methods (e. g. [42] , "method 2" of  [43, 44] ) until we reach all the tips, recording each substitution that occurs and hence which tip characters are PIBD. Note that it is not possible to achieve this using software such as evolver  [45]  or "method 1" of INDELible  [43]  that only simulate the start and end state of each branch, and do not distinguish between characters that are PIBD and those that happen to match following multiple substitutions. For each iteration we associate a score of 1/i to a tip of φ if its observed character is PIBD to the characters of exactly i tips.
15, • observed frequencies corrected using the GSC94  [9]  weights and Eq. 15, • observed frequencies corrected using the PNS weights w s from Eq. 1 combined with Eq. 15, • observed frequencies corrected using our PNS weights conditional on data, w D s from Eq. 2, combined with Eq. 15, • observed frequencies corrected using our fast approximate PNS w s weights (Eq. 14) combined with Eq. 15, • Bayesian variants of the methods above, and • ML phylogenetic inference (only of equilibrium character frequencies) with PhyML v3. 1  [58]. All the methods above, except FastTree and PhyML, were implemented in custom Python scripts available from https:// bitbu cket. org/ nicof may/ novel tysco res. In addition to the basic simulation scenario, we also consider variant scenarios in order to investigate how certain parameters can affect the results: • We consider alternative tree lengths, which we obtain by multiplying all branch lengths in the tree in Fig.
Sequence weights We implemented all the considered weighting schemes and all simulations within custom Python scripts (https:// bitbu cket. org/ nicof may/ novel tysco res), making use of the phylogenetic python package dendropy  [56]. We implemented all the algorithms presented in the Methods section for calculating weights w s and w D s , and used comparisons of weights from different algorithms to assess the correctness of the implementations. PNS shows similar trends to previous weighting schemes HH94 and GSC94, assigning higher weights to phylogenetically isolated taxa and smaller weights to taxa within clades with many other closely related taxa (Fig. 2 ). In particular, PNS seems to show an intermediate 'intensity' compared to the two other schemes. GSC94 weights are the most extreme, assigning the highest weights of any scheme to the most evolutionarily isolated taxa in the tree of Fig.
Computational demand Computational efficiency is one of the main requirements for applicability of weighting schemes, in particular when considering large datasets; for this reason, here we compare the computational demand of different approaches. Calculating sequence weights based on a phylogeny (PNS and GSC94) usually requires limited computational demand, with the dominant cost being the estimation of the phylogeny itself (Fig. 3  and Additional file 1: Fig. S4 ). One exception to this are the w D s weights of Eq. 2: these, being conditional on the data observed at a specific alignment column, need to be re-computed for each position. Calculation of these weights requires time cubic in N, and so it is not surprising that these weights are slower than phylogenetic inference. The other slowest method for character frequency is phylogenetic ML (PhyML), which also needs to be run once for each alignment column.
All other approaches are at least one order of magnitude faster than PhyML and w D s in estimating character frequencies, and are practical also for larger trees (see e. g. Fig. S4  where we included 1000 closely related taxa). Calculating weights w D s and estimating frequencies by ML, instead, becomes infeasible on such larger trees. Estimating frequencies using HH94 weights is the fastest of the methods considered, as it does not require prior estimation of a phylogenetic tree, and it might therefore be one of the few possible choices available for extremely large datasets. The second fastest approach is GSC94, followed by the w s weights, and finally by the w s weights, although Each plot contains values for 10 replicates of the scenario of the unscaled tree in Fig. 1  and nucleotide data.
Accuracy of character frequency inference Here we assess the ability of different weighting schemes, including those derived from our new PNS methods, to facilitate inference of column-specific character frequencies. We measure the accuracy of an approach by calculating, at each alignment column, the Euclidean distance between simulated and inferred character frequencies. ML phylogenetic inference with PhyML is almost always the most accurate method (Figs. 4  and 5 ). This is perhaps not surprising, given that this approach fully models the effects of varying equilibrium character distributions on character evolution along the phylogeny. However, this approach is also the most computationally demanding, and the advantage of schemes based on sequence weights is that they can be much faster, in particular on datasets with many sequences or many alignment columns.
Each plot contains 10 replicates, and each replicate contains 800 alignment columns evolved under the background nucleotide frequencies (a, c and e), or 200 alignment columns evolved under equilibrium nucleotide frequencies sampled from a Dirichlet distribution with α = 0. 1 (b, d and f). Horizontal black dashed lines aid comparison by showing the median error of the first method (frequencies extracted from character counts). In a and b the tree branch lengths were scaled by a factor of 0. 2; in c and d by a factor of 1. 0; and in e and f by a factor of 5. 0. Each plot shows results for a particular character frequency inference method, indicated on the x-axis. Results from additional methods (e. g. Bayesian approaches) are shown in Additional file 1: Fig. S5  column (Figs. 4  and 5 ). GSC94 and w s weights seem to give more accurate results than HH94 and w s weights, in particular within very biased datasets (Fig.
Using sequence weights conditional on the data at the specific column, i. e. w D s from Eq. 2, unexpectedly does not seem to improve accuracy (Additional file 1: Fig. S5 ) while, as shown in Fig. 3 , it does significantly impact computational demand. For these reasons, we do not generally recommend the use of weights w D s. Using a Bayesian approach to character frequency inference means that the prior on character frequencies can affect the result of the inference. This can have a positive effect if the prior distribution is based on reliable evidence from sources other than the currently considered dataset. However, in our simulations we consider a completely arbitrary prior (corresponding to observing one character of each type at the considered alignment column) and this has the effect of slightly shifting the inferred frequencies closer to a uniform distribution (Additional file 1: Fig.
4E  and 5C, E ). This however usually comes at the cost of additional computational demand, especially considering that our scores require the availability of an inferred phylogenetic tree, and considering that this might not be feasible for extremely large datasets. PNS and GSC94  [9]  weights both require a phylogenetic tree estimate, and both show very similar performance in our main simulation scenario, with GSC94 marginally outperforming PNS. However, we demonstrate that, unlike GSC94 weights, PNS are not affected when the assumption of tree ultrametricity is violated (Fig. 6 ), and similar patterns are expected with respect to the robustness to the position of the tree. This shows that PNS are particularly versatile in applicability, as one would expect from their formal phylogenetic derivation.
Over most scenarios, the most accurate method for position-specific character frequency inference seems to be standard phylogenetic ML inference; however, this approach is also very computationally demanding, and is not suitable for large datasets. Character frequency inference, our example use-case for PNS, has a number of important applications. Character frequencies are fundamental parameters used in HMM profiling of protein families  [3, 6] , and our scores could therefore improve approaches to this task. Our scores could also be used to improve character frequency estimates used within alignment column-wise conservation scores  [36] [37] [38] , frequently defined as where p(j) is the frequency of character j at a given alignment column, and B is the number of characters ( B = 4 for nucleotides and B = 20 for amino acids).
Satellite based turbidity retrieval improved drastically with the introduction of semi-empirical relationships  (Nechad et al. , 2009). The most widely used semi-empirical turbidity algorithm was published by  Dogliotti et al. (2015) , where both red and NIR reflectances are used to cover a wide range of turbidity values. Validation data from multiple coastal sites located in the temperate to tropical parts of the Earth were used, with a strong focus on estuarine environments. Validation data from permafrost coasts is missing in this algorithm, even though permafrost coasts represent one third of the world's coastlines  (Lantuit et al. , 2013). In this study, we calibrate and test the Arctic Nearshore Turbidity Algorithm (ANTA), an ocean color algorithm that directly relates water leaving reflectance to turbidity in Arctic surface nearshore waters.
Regional setting The tuning of the ANTA is based on in-situ measurements from the nearshore waters around Herschel Island Qikiqtaruk (HIQ), located at the Yukon coast in the western Canadian Arctic. In-situ samples taken at Adventfjorden in the western part of the Svalbard Archipelago were used as a test dataset to assess the transferability to other nearshore areas in the Arctic. HIQ is surrounded by the Yukon Coast nearshore waters, which are part of the Canadian Beaufort Shelf (Fig. 1 ). The Mackenzie River is the main freshwater source of the Canadian Beaufort Shelf  (Hill et al. , 1991). The Mackenzie freshet in May marks the beginning of the ice break-up at the shelf with a peak discharge of up to 35000 m 3 /s (O'  Brien et al. , 2006; Yang et al. , 2015). Coastal erosion at HIQ occurs mostly during the open-water season, when the sea is not covered by ice, which typically spans from mid-June to late September  (Solomon, 2005).
Each radiometric measurement sequence lasted for 5 min. While the downwelling sky-irradiance (E D ) was sampled throughout the complete 5 min pointing the zenith at the highest location of the coastal ship, the radiance was measured as total upwelling (L U ) and sky radiances (L D ), respectively, for 2 min each. Radiance measurements were taken in 135 • relative to the solar zenith angle and in nadir/zenith angles of 40 •  (Mobley, 1999). In-situ turbidity measurements in Adventfjorden were taken on June 14 th , 2019, and August 8 th , 2019, to match Sentinel 2 data acquisitions. 10 locations were sampled on both dates (Fig. 2 ). Triplicate measurements were conducted using a Thermo-Eutech TN-100 turbidity meter.
05 and T(NIR) if R W (red) > 0. 07, with a linear blending in the transition. The A and C parameters were calculated for R W (red) and R W (NIR) using non-linear least-square regression for each of the above named sensors (Table  1 ). The performance of the ANTA was compared to the original semiempirical turbidity algorithm presented in  Dogliotti et al. (2015)  by applying both algorithms to the dataset presented by  (Klein et al. , 2019)  that consists of 35 Scenes of the HIQ nearshore zone recorded by Landsat TM, ETM+ and OLI between 1986 and 2016. The 35 scenes were grouped based on their prevailing wind conditions (changing winds, NW, and ESE  (Klein et al. , 2019) , to calculate mean turbidity per used algorithm and wind condition.
One scene was recorded on June 14th by Sentinel 2A and one on August 8th, 2019 by Sentinel 2B. Match-up scenes for Landsat 8 are not available, and the spatial resolution of Sentinel 3 is not sufficient to resolve the small-scale sampling grid (Fig. 2 ). Landsat imagery was downloaded from the United States Geological Service (USGS) Earth Explorer as Level1T product, which means with georeferencing but without atmospheric correction. Sentinel 2 and Sentinel 3 imagery was downloaded from the European Space Agency (ESA) Copernicus Hub as Level1C product. Atmospheric correction towards water leaving reflectance (R W ) was performed using the ACOLITE software version 20210114. 0 (Landsat 8 and Sentinel 2,  Vanhellemont, 2019; Vanhellemont and Ruddick, 2018)  and the ACOLITE software version 20210421.
Test and transfer of the ANTA In-situ surface turbidity data from Adventfjorden were used as test data to evaluate the performance of the ANTA developed for the HIQ nearshore area for another nearshore Arctic coastal area. Fig. 9  shows that the ANTA is capable of resolving both low and high turbidity values. Sediment pathways and filaments of high turbidity are well resolved close to the river outlet in the SE part of Adventfjorden, as well as in the NW towards Isfjorden. The results reveal that lowest turbidity values (up to ~ 10 FNU) are typically overestimated (Fig. 10 ), which is similar to the results obtained from the HIQ imagery. This feature is related to the Sentinel 2 sensor design: it was designed to focus on land applications and the reflectance over low-turbidity waters are much lower than over land.
The ANTA was applied to Sentinel 2 imagery from Adventfjorden in Svalbard to assess its performance outside the Canadian Beaufort Sea, where the calibration data was collected. Despite strong differences in sources and distribution of suspended sediments between these sites, with Adventfjord being impacted by higher freshwater and inorganic sediment inputs from land-terminating glaciers, the ANTA proved to be robust and transferable. However, future research has to focus on the influence of the SPM particle size distribution and composition on the ANTA, and on the influence of organic content. Since high cDOM concentrations generally correlate with high freshwater input, it is recommended to use the turbidity algorithm with caution in the vicinity of large river deltas. The best performance is expected close to strongly eroding coasts with high organic Carbon content, where conditions are likely to be comparable to Herschel Island Qikiqtaruk, where the calibration data was collected.