dataesr/scientific-paragraphs-categorization huggingface dataset:
Genetic Variability For the three datasets, similar values were obtained among the population samples for H o , unbiased H e and A R , confirmed by one-way ANOVA detecting no significant differences among samples (P > 0. 05; Supplementary Tables  S1-S3 ). A generalized heterozygote deficit was detected, as evident from the positive values obtained for the inbreeding coefficients (F IS ) in all the populations (Supplementary Tables  S1-S3 ). Locus-by-locus analysis showed that the distribution of F IS values is skewed toward positive values, though the majority of the loci show F IS values very close to 0 and few of them negative values (Supplementary Figures  S1-S3 ).
Genetic Structure at the Large Scale The non-hierarchical AMOVA carried out on the 19 population samples (the overall dataset, 512 polymorphic loci) provided a clear indication of genetic structuring at the large spatial scale, with a statistically significant global multilocus F ST (F ST = 0. 03243, P < 0. 0001). Considering the 171 pairwise F ST (Table  2 ), all 90 comparisons involving a location from the Mediterranean Sea and a location from the Black Sea were statistically significant after correction for multiple tests (F ST range = 0. 04208-0. 08079; P < 0. 0001), indicating a clear genetic differentiation between the 2 seas. In addition, among Mediterranean samples 14 comparisons out of 45 were significant after multitest correction (F ST range = 0. 00809-0. 02075; P < 0. 05); in this case, pairwise differences were mainly due to the deviation of the sample from Montenegro (BOK, 9 significant comparisons).
The North-Western (FRN) and the South-Western (TUN) samples were not genetically different despite the geographic distance, and they differed only weakly from the Central Mediterranean ones (3 significant comparisons, 2 of them involving the BOK sample). No significant pairwise F ST was detected within the Black Sea. Hierarchical AMOVA showed that the genetic variation of the dataset can be best partitioned into 2 groups, one including the 10 Mediterranean population samples, and the other one the 9 Black Sea's population samples (F CT = 0. 05286, P < 0. 0001), maximizing the genetic variance between groups. Similarly, the DAPC identified two genetic clusters as the optimal clustering solution based on the lowest BIC (Figure  2A ), with the 2 clusters corresponding to the Mediterranean Sea and the Black Sea population samples (Figure  2B ).
Genetic Structure at the Basin Scale The lack of genetic structure of the Black Sea and the signal of genetic differentiation in the Central Mediterranean found with the overall dataset were confirmed by the more powerful basinspecific datasets. The non-hierarchical AMOVA conducted on Adriatic-Ionian dataset (811 polymorphic loci, 8 population samples) pointed out that most of the genetic variation (>99%) arose from the withinpopulations level and only 0. 69% from the among-populations level, with, however, a statistically significant global F ST (P < 0. 0001). A number of pairwise F ST values were statistically significant after correction for multi-test (9/28;      clustering solution for three groups: the first group with the population sample from Tremiti islands (TRE) alone, the second group including BOK sample and the Croatian sample (KOR), and the last including the three other Italian samples (OTR, POC, and TOG), the Greek (OTH-A) and Albanian (KAP) samples (F CT = 0.
00312, P < 0. 05). The scatterplot of the discriminant analysis set on the original populations reflected the weak genetic differentiation detected among these population samples (Figures  4B, C ). The Black Sea dataset (998 polymorphic loci, 9 population samples) revealed a small and not significant global multilocus F ST value (F ST = 0. 00278, P = 0. 59584) and no pairwise F ST value resulted to be significant within this basin (Table  4 ). Both Bayesian clustering methods conducted by ADEGENET and STRUCTURE found only one genetic cluster (BIC values of the DAPC in Figure  5 ; STRUCTURE plot not reported). As expected due to the lack of genetic structuring, the visual assessment of the between-population differentiation of the original populations showed all the samples strongly overlapping (DAPC plots, Figures  5B, C ).
Outliers Detection The outliers detection conducted on the overall dataset identified 25 loci putatively under positive selection. 1. Fourteen out of these 25 loci were detected by both software, and 11 only by BAYESCAN. The exclusion of these loci resulted in a substantial reduction of the global genetic variance of the dataset (F ST = 0. 01152; P < 0. 0001), but the general pattern of structuring was maintained, with the best clustering solution for two groups (Mediterranean population samples against Black Sea's population samples; F CT = 0. 01565; P < 0. 0001) and with 86/90 significant pairwise F ST distances between the Mediterranean and the Black Sea. The scan of the Adriatic-Ionian dataset by both software detected 2 putative outliers, whose exclusion caused a small   1. decrease of the molecular variance (F ST = 0.
Genetic Structure at the Large Scale A marked difference in genetic structure comparing the Mediterranean and the Black Seas was found. This result confirms and strengthens the limited genetic data obtained for this species  (Ladoukakis et al. , 2002) , which hinted at reduced gene flow between the two seas. A similar differentiation was previously reported for other species (including anchovies,  Magoulas et al. , 2006; sticklebacks, Mäkinen and Merilä, 2008; sprats, Limborg et al. , 2012; bluefishes, Miralles et al. , 2014). The effect of oceanographic features like the narrow Dardanelles and Bosphorus straits and the existence of environmental barriers (salinity, temperature) limit the exchange of water and species between the two seas and are suspected as a probable cause. The extent of isolation of the Black Sea makes it a potential refuge for temperate and cold water species under future warm conditions.
Genetic Structure at the Basin Scale In contrast to the Western Mediterranean, the Central Mediterranean, represented by Adriatic-Ionian mussel population samples, showed a weak but significant genetic differentiation, which was not affected by the presence of a few outliers. Our results confirm and complement previous reports that indicated, despite a limited sampling effort, genetic heterogeneity in the Adriatic for this species  (Giantsis et al. , 2014a,b). In our study, differences were weak, e. g. , Bayesian clustering failed to detect genetically differentiated clusters, and significant pairwise F ST comparisons were found mostly in pairing involving BOK sample (Montenegro). However, hierarchical AMOVA clearly indicated that genetic variation of the dataset can be best partitioned into three groups: a first group consisting of TRE sample (Tremiti Islands) alone, a second including BOK and KOR (i.
ACKNOWLEDGMENTS We are thankful to the following people for their precious scientific inputs, and for their help for logistics and collecting samples on the field:  F. Boero  and  S. Fraschetti  ( University of Naples Federico II, Italy ) and  S. Bevilacqua  and  A. Terlizzi  ( University of Trieste, Italy ),  S. Reizopoulou  and  M. A. Pancucci-Papadopoulou  ( HCMR, Greece ),  S. Beqiraj  ( Department of Biology, Faculty of Natural Sciences, University of Tirana ),  E. Hajdëri  ( Catholic University "Our Lady of Good Counsel, " Tirana ), and  V. Neglia  and  N. Tolou  ( CRIOBE, Université de Perpignan ).
FUNDING This research was supported by the  European Community 's  7th Framework Programme  ( FP7/2007-2013 ) under Grant Agreement No. 287844  for the project " Towards COast  to  COast NETworks  of marine protected areas (from the shore to the high and deep sea), coupled with sea-based wind energy potential (CoCoNet, http://www. coconet-fp7. eu/)",and by the  University of Padua  grant  CPDA148387/14.
Pairwise compositional associations among groups were quantified by the RV coefficient of Co-Inertia Analyses (COIA), which was tested using Moran Spectral Randomisations (MSR) to take spatial autocorrelation into account. RV values were significant (P ≤ 0. 05) for each pair of groups, regardless of the soil heterogeneity (Fig. 1c ). The highest RV value reached 0. 746 (P < 0. 001; MSR test) between trees and ants. Average RV values (calculated over the four values per group) ranged from 0. 488 (SD = ± 0. 121) for earthworms to 0. 575 for spiders (SD = ± 0. 038). All groups also displayed significant co-inertia with soil variables, with trees and ants showing the highest associations (RV = 0. 623 and 0. 601, respectively). RV values were highly correlated to Procrustes residuals sum of squares (PRSS), after removing or not the plots on seasonally flooded forest and by using either taxonomic abundance/occurrence or presence-absence data (with r-Pearson values ranging from 0.
A significant RV between two groups indicates a significant coordinated turn-over of composition. We took multiple test effects into account in all of our tests by using a Šidák correction  64. It is worth noting that conceptually, COIA is very close to the Procrustes Analysis which usually leads to redundant results  65, 66. We thus performed Procrustes analyses (calculation of the Procrustes residual sum of square to quantify association between data tables  67  ) as a complementary approach to further verify consistency in our results. Testing coordinated response to soil variables for each pair of groups. In order to test the influence of environmental heterogeneity in explaining coordinated species turn-overs among pairs of taxa (questions 2a and 2b), we used the soil data and the co-inertia axes obtained from a COIA performed on each pair of taxonomic groups.
Finally, MEM were also used to test at which spatial scale(s) the overall soil heterogeneity and each individual soil variable explained the co-inertia among pairs of groups (see Appendix S2 for details). Indeed, MEM eigenvalues are directly correlated to Moran's I and therefore to the spatial scales at which variables are structured. All analyses were performed in the R statistical environment  69  , using packages car, ade4, adespatial, usdm, cocorresp, MatrixCorrelation, vegan, ape and mglmn (see references of these packages in Appendix S2). The R script as well as the soil and composition data tables used to perform our analyses are provided in Appendices S3 to S9.
Acknowledgements Authors thank the  Réserve Naturelle de la Trinité  (http://www. reserve-trinite. fr/)and the  Parc Amazonien de Guyane  (http://www. parc-amazonien-guyane. fr) for authorizations and access to the sites and additional fundings. Part of the material was collected during the " Our Planet Reviewed " Guyane-2015 expedition in the Mitaraka range, in the core area of the  French Guiana Amazonian Park , organized by the  MNHN and Pro-Natura international. The expedition was funded by the  European Regional Development Fund (ERDF) , the  Conseil régional de Guyane , the  Conseil général de Guyane , the  Direction de l′Environnement, de l′Aménagement et du Logement  and by the  Ministère de l′Éducation nationale, de l′Enseignement supérieur et de la Recherche. It was realized in collaboration with the  Parc amazonien de Guyane  (http://www.
As such these approaches offer a tool for remote sensing researchers that is distinctly lacking in the literature with respect to time-series data. This is addressed two-fold by presenting an overview of constrained clustering methods and how they can be adapted to this use case, and a comparative study of their performance on time-series datasets. The comparative study is achieved by selecting the most representative algorithms from the literature and adapting them to be suitable for time-series clustering where necessary. These are compared for crop classification which greatly benefits from the analysis of spatio-temporal data  [5, 15, 16, 17, 18, 19, 20, 21, 22]  and tree-cut detection  [23, 24]. As such, this article offers insight into the different formulations of constrained clustering algorithms, how they can be adapted for use in satellite image time-series, and insight into which algorithms are suitable for such applications.
A. Cluster Analysis Let O be a set of instances (data points) {o 1 ,. , o n } and d(o i , o j ) a dissimilarity (or a similarity) measure between any two instances o i and o j. The similarity or dissimilarity between instances can be computed from their features or given by a similarity graph. Partition clustering involves finding a partition of O into K non-empty and disjoint groups called clusters, C 1 ,. , C K , such that instances in the same cluster are very similar and instances in different clusters are different. Cluster homogeneity is usually formalised by a optimisation criterion, and clustering aims at finding a partition that optimises the given objective. Different optimisation criteria exist for distance-based clustering, the most popular being  [25] :.
A number of works give an in-depth background on clustering time-series  [30, 35, 36, 37, 38, 39, 40]. Keogh and Lin  [41]  define two categories of time-series clustering: • Whole Clustering: "The notion of clustering here is similar to that of conventional clustering of discrete objects. Given a set of individual time series data, the objective is to group similar time series into the same cluster"  [41]. • Subsequence Clustering: "Given a single time series, sometimes in the form of streaming time series, individual time series (subsequences) are extracted with a sliding window. Clustering is then performed on the extracted time series subsequences"  [41]. They then proceed to demonstrate that subsequence clustering is "meaningless" because "clusters extracted from these time series are forced to obey a certain constraints that are pathologically unlikely to be satisfied by any dataset, and because of this, the clusters extracted by any clustering algorithm are essentially random"  [41].
In remote sensing, the typical goal is to cluster time-series using all the available data, and this is a direct application of existing constrained clustering approaches. Therefore this work will focus on 'Whole Clustering'. It should be noted that DTW is not the only alternative to Euclidean distance for measuring dissimilarity between time-series. It is, nevertheless, often found that alternative dissimilarity measures are not significantly better than DTW in real-world datasets  [42, 31, 43, 44]  (the reader is referred to these references for a comprehensive review and comparison of the alternatives). To quantify the benefit (if any) of using DTW, this study will compare the performance of constrained clustering algorithms modified to use DTW to that using Euclidean.
The implementation by Wang et al. [65]  is formulated for two class problems (although authors describe how the method can be extended to multi-class problems in their article). That by Cucuringu et al. [86]  was modified but did not converge on all the datasets. Constrained 1-Spectral Clustering  [66] , Tabu search  [47] , and MIP-KMeans  [82]  were too slow once modified to use DTW and DBA. The method by Pelleg and Baras  [46]  updates the centroids heuristically using a formulation which does not have an obvious extension to DTW. Similarly, it is unclear how probabilistic methods such as the constrained EM algorithm  [88]  could be modified to use alternative distance measures and averaging techniques as it is not obvious how these would affect the probability estimates needed for their application.
B. Out-of-Sample Classification Since constrained clustering is a relatively expensive process, it may not be possible to cluster all available data. Using the clustering derived from a subset of the data, out-of-sample data points can be classified using, for example, a k-NN classifier. This allows the generalisation from a randomly selected subset of the data to the whole dataset. If the clustering is to be generalised beyond this, however, the user should be aware of problems arising from distribution mismatches between different domains  [90, 91].
A. Crop Classification Agricultural practices are a major concern because of their environmental impact, the amount of water they use and the quantity of food they are able to supply  [7]. Since satellite data offers observations with a high spectral, spatial and temporal resolution, applications of crop monitoring, identification of agricultural practices, or estimation of yield and crop parameters are numerous  [92, 93, 94]. Therefore a dataset that covers cultivated landscape characterised by mixed farming with a wide variety of irrigated and non-irrigated crops is used for evaluating the algorithms. The study area is located near Toulouse (Southwest France). The dataset represents 2 It is noted that more recent time-series image data is available, i. e. from the Sentinel-2 satellite; however, at the time of writing, there exists no associated reference data created using temporal evolution.
Reference data such as carte d'occupation des sols (COS) exist but are created using supervised methods and therefore make an unfair comparison. This work, therefore, relies on the RPG (registre parcellaire graphique) reference data, for which there are currently no releases that coincide with complete years of Sentinel-2 data. Nevertheless, the methods described are general and their application is not restricted to the type of data used in this evaluation. Reference data is the farmer's declaration of crops grown to the European Environment Agency for the Common Agricultural Policy.
Finally, the algorithms were executed with no constraints for comparison. 2) Parameter Tuning and Preprocessing: To fully explore the capabilities of the parameterised algorithms, parameter values were optimised by grid search on the training sets (except for CPClustering, which is parameterless). Parameter optimisation is an unrealistic use-case as in real-world applications as a training set is unlikely to be obtainable, however, it demonstrates the theoretical benefits of each algorithm. All the evaluated algorithms, except CPClustering, require the number of clusters K to be set a priori and this was taken from the reference data such that K = 12, see Figure  2d  (CPClustering relaxes this assumption by accepting a range of K and determining the best value automatically, however in these experiments K was fixed as with the other algorithms).
This score varies between -1 and 1 and evaluates cluster overlap such that s(o i ) = b(o i ) -a(o i ) max{a(o i ), b(o i )} , (3) where a(o i ) is the average dissimilarity of point o i to all other points within the same cluster and b(o i ) is the minimum, taken over all the clusters C k not containing o i , of the average dissimilarity of o i to all points of C k. Averaging this over all points in a dataset gives its silhouette score, such that S = 1 |O| o i ∈O s(o i ). (4) Higher scores indicate that points belong to clusters in which the other points are similar, and are dissimilar to the points in the next closest cluster. To assess the impact of using a pixel's temporal evolution in agricultural land cover classification, the first analysis is concerned with the pixel values from each individual image (i.
The time-series is composed of 10 images sampled over 3 years: A subset of the area was selected by randomly sampling 30, 000 pixel time-series. The ground truth locations of clear-cuts used for validation was manually created by remote sensing experts and validated in the field by forestry rangers, see Figure  4b , one of the challenges here being that clear-cuts represent only 0. 3% of the dataset. Furthermore, as is typical of remote sensing problems, the number of clusters that represent the non-clear-cuts part of the image is unknown. t As such, the number of clusters is arbitrarily set to k = 15. The evaluation is only concerned with clusters that include clear-cuts (this is selected by choosing the cluster the overlaps the clear-cut ground truth the most). As before, SAMARAH was used with both the DTW and Euclidean distance measures.
The constraints were given by an expert in remote sensing after viewing an out-of-sample classification of the whole image based on the unconstrained clustering of the subset described above, see Figure  4c. One set was given using the DTW metric, and another using the Euclidean metric (the number of constraints remained constant). If the added constraints concerned a sample that was not in the original dataset, the sample was added in order to guide SAMARAH to extract meaningful information (which resulted in 30, 037 samples for DTW and 30, 034 for Euclidean). The average recall and precision (of 10 repetitions, standard deviations are presented in brackets) of the unconstrained clusterings for DTW was 0. 367 (0. 138) and 0. 018 (0. 009) respectively and for Euclidean was 0. 558 (0. 141) and 0.
B. Distance Measure It was found in the Crop Classification dataset that the Euclidean distance offers the highest unconstrained performance in almost all of the algorithms. This is due to the relatively coherent nature of the data. The images are cloud free, which means that the feature vectors are noise free. The problem also lends itself to the Euclidean distance: the time-series are aligned in the time domain and the area of interest is spatially coherent, meaning that crops grow and are harvested in the same time-frame within different parts of the image. If these assumptions do not hold, DTW may offer better performance. From the Tree Cut dataset it can be concluded that if the phenomenon to be clustered has a temporal signature that varies between samples (e. g. cuts occurring at different dates) and these should be combined, DTW would be a better choice, otherwise (if the signals are temporally correlated and/or the uncorrelated signals should be separated) Euclidean can give better results.
To the best of our knowledge, however, the theoretical guarantees of the K-Means algorithm only hold when using the Euclidean distance. DTW barycenter averaging is a stochastic process and therefore the same guarantees are unlikely to hold when using it, nor does DTW satisfy the triangle inequality. An important aspect of the usability of these methods is the time taken to obtain a clustering. algorithms are implemented in the same language, and the timings are taken using each language's built-in timing. Finally, these timings only include the execution of the clustering algorithm itself and, as such, exclude processes such as reading data files. Furthermore, it excludes the training phase needed to select parameter values for Spec and CCSR and the time taken to calculate the distance matrix that is the input for Spec, CCSR, and CPClustering.
1 23rd August 2016, t 2 26th August 2016, t 3 10th May 2017, t 4 19th June 2017, t 5 23rd August 2017, t 6 25th September 2017, t 7 8th May 2018, t 8 2nd July 2018, t 9 24th July 2018 and t 1 0 16th August 2018. The dataset contains 10 areas of clear-cut: 8 appear at t 3 and 2 at t 7. The images are composed of one band that contains the computed NDVI (normalised difference vegetation index) values. The images were computed from Copernicus Sentinel-2 data (tile T32ULU) processed at level 2A/3A by CNES for Theia Land Data Centre.