dataesr/scientific-paragraphs-categorization huggingface dataset:
This pattern is mainly a function of the availability and inclusion of open-air vs. sheltered sites (caves, rockshelters) in the dataset  19. In addition, it may highlight differential depositional and preservation conditions of stone artefact assemblages on the North European Plain and in high-latitude landscapes, which have been strongly affected by glacial activity, for example across the northeastern extent of the study area. Some of the included lithic data from Eastern and parts of Central Europe may thus be less reliable than their Southern and Western European counterparts and this is in part also a consequence of the varying scope and intensity of Late Glacial research in these regions in recent decades. This situation is also well-reflected in the distribution of average quality scores across macro-regions plotted in Fig.
Usage Notes Data structure and spatiotemporal scale. The dataset  19  records multi-scale observations on lithic technology for each macro-region (Fig. 7 ). Data on reduction technologies and toolkit composition are provided as NAC-level presence/absence matrices (macro-regional scale) but the resolution of the two data domains differs, as toolkit trait variables refer to highly generalized lithic tool morphotypes while technological traits represent a mix of well-established descriptive variables and more synthetic observations. Variability in the technology domain is thus not directly comparable to variability in the toolkit domain, but we generally expect that technology-toolkit relationships reflect relevant information on technical choices and cultural evolution. In other words, patterns in macro-regional technological data should be related to the structural organization of the corresponding toolkits, but the relationship must not always be strictly co-evolutionary as technical trade-offs may for example promote informal tool-use not documented in the dataset  43.
Diversity in tool outlines may similarly be expected to be elevated in cases where lithic technology is rooted in a conceptual and operational distinction between lithic blank and tool production, so that tool confection (modification) becomes the key means to install and differentiate tool functions, or, alternatively, reflect the grounding diversity of forms obtained during blank production, or a combination of both. These and cognate hypotheses can empirically be tested using the present dataset  19  , and changing structuration principles at the intersection of technology, toolkit structure and artefact shape may in this way be discovered and pinpointed in time and space. Inter-domain dynamics. The multi-domain and multi-scale structure of the dataset  19  supports the systemic analysis of long-term dynamics of lithic change.
By quantifying the amplitude and rate of change in varying data domains, researchers can examine patterns of synchronicity or lack thereof in the evolution of stone artefact technologies on a pan-European scale, and in this way contribute to debates on the temporality of material culture change  [48] [49] [50] [51] [52]  as well as broader discussions on the evolution of cultural systems  53, 54. Confronting the dynamics of change across data domains thereby contributes to the identification, quantification, and hence qualification of different tempi and modes of change, possibly revealing that technological choices, toolkit compositions, and artefact shapes are subjected to differential dynamics of long-term technological evolution. Under which conditions such asynchronies in lithic evolution emerged, or whether we should instead regard such patterns as the default mode of material culture change, is an important emerging question and the dataset  19  makes it possible to begin systematically addressing it.
Inter-domain analysis and comparison finally provide a new means to assess the general character and the complexity of cultural evolution between ~15,000 and 11,000 years ago. Complexity can for example be understood as a property of domain interaction, and mapping out domain-specific dynamics of change can thus provide new perspectives on questions of linearity and nonlinearity in the millennial-scale evolution of Late Pleistocene/earliest Holocene human forager technologies. Further remarks and perspectives of data usage. Archaeological data are voluminous, distributed across many language and publication formats. They are rarely available in standardized digital formats. Mindful of the analytical decisions that had to be taken in transforming qualitative observations and legacy images into the dataset  19  presented here  55, 56  , we recommend due diligence in its usage.
That said, computational tools are offering new avenues for research within lithic analysis specifically  57, 58  , and for understanding large-scale patterns of cultural evolution  59. Data availability is a key element in this endeavor  60, 61  and the dataset  19  presented here is the largest of its kind at this moment, assembled through the integration of domain-specific (Late Glacial archaeology and lithic technology) and computational expertise. By linking original sources to coded data, we offer users the possibility to trace the material of interest back to source for validation. As our technical validation indicates, data availability and quality varies by region, creating an imbalance between Western Europe and the remainder of the study region. Especially in parts of Central and Eastern Europe, data availability is compromised by research historical factors, while taphonomic factors may have led to generally poorer preservation conditions in Northern Europe.
In assembling the present dataset  19  , our aim has been to facilitate inter-regional analysis. Users seeking to ask questions at smaller geographic scales should be mindful of the regional integration of information within the toolkit and technology domains. This integration precludes comparisons of lithic technological variability between sites and at within-region scales. Furthermore, the 2D outline data derives from legacy sources and different production styles may impact accuracy  62. Drawings and photos have, however, been shown to provide reliable information for geometric morphometric analysis  40, 63  , lending confidence that these data can be used for comparative inter-regional analyses. Notably, the acquisition of additional outline data for lithic artefacts is straightforward, and we hope that users will complement the dataset  19  at hand with further specimens.
Code availability The full dataset  19  and the code used in this paper are freely available at Zenodo: https://doi. org/10. 5281/ zenodo. 7940337. All data, analyses, and visualizations presented in this paper were prepared in R 4. 2. 2 under Ubuntu 18. 04. 5 LTS (64-bit) using the following R packages: data. table (≥1. 14. 8), dplyr (≥1. 1. 2), forcats (≥1. 0. 0), ggforce (≥0. 4. 1), ggplot2 (≥3. 4. 2), ggpointgrid (≥1. 2. 0), ggridges (≥0. 5. 4), magrittr (≥2. 0. 3), Momocs (≥1. 4. 0), outlineR (≥0. 1. 0), raster (≥3. 6-20), readr (≥2. 1. 4), remotes (≥2. 4. 2), rgeos (≥0. 6-2), rworldmap (≥1. 3-6), sp (≥1. 6-0).
In freshwater ecosystems, Perkinsea sequences are abundant and form clusters exclusively composed of environmental sequences. This indicates the existence of unrecognized diversity that likely plays significant but overlooked ecological roles in the food web dynamics  [25] [26] [27]. In marine environments, Perkinsea sequences account for less than 1% of the reads of DNA/RNA datasets  28. However, they are widely distributed in coastal areas, especially in sediments, with a significant proportion of reads detected in RNAderived samples. These results suggest metabolic activity of detected Perkinsea and potential participation in the 'seed bank' of microbial communities  28, 29. Nevertheless, these studies have limited geographical coverage, mainly focusing on specific geographical regions in the Northern Hemisphere  28  , giving only a partial view of the Perkinsea diversity and distribution.
In this paper, we conducted a comprehensive analysis of a metabarcoding dataset, which includes sequences of the V4 region of the SSU rRNA gene from diverse ecosystems, including marine, land water and soil environments (the EukBank dataset), with the aim to investigate the diversity, distribution, and environmental preferences of the Perkinsea lineage in a wide range of samples from land to aquatic ecosystems  8. Overall, our results underscore the significance of incorporating large-scale eDNA surveys to gain a comprehensive understanding of overlooked microeukaryotic organisms that may be related to parasites implicated in disease outbreaks worldwide.
Perkinsea reference phylogenetic tree We conducted a phylogenetic analysis of Perkinsea to classify the Amplicon Sequence Variants (ASVs) retrieved from the EukBank dataset (details provided in the following section). For the analysis, we selected 95 sequences that encompassed the known diversity of Perkinsea  8, 19, 25, 28, 30  and seven sequences as outgroup references [Ichthyodinium chabelardi (AB264776), Noctiluca scintillans (AF022200), Amoebophyra sp. (AF472555), Hematodinium perezi (EF065717), Symbiodinium microadriaticum (M88521), Amyloodinium ocellatum (AF080096) and Alexandrum ocellatum (U27500)]. The sequences were aligned using MAFFT V 7. 407  31  with the E-INS-I algorithm and trimmed automatically with trimAl v1. 4. rev15  32  , discarding regions with more than 75% gaps using a -gt parameter of 0.
The EukBank database and Perkinsea ASVs phylogenetic classification The EukBank database is a compilation of eDNA surveys that employed high-throughput sequencing methods (Illumina MiSeq and Roche 454) targeting the hypervariable region V4 of the SSU rDNA sequence  37. Briefly, raw sequences were obtained from the EMBL/EBI-ENA EukBank umbrella project. When applicable, reads were trimmed with Cutadapt (https:// github. com/ marce lm/ cutad apt/  38  with specific parameters tailored to extract fragments covered by the primers sets TAReuk454FWD1 and TAReukREV3 from the V4 region or the SSU rRNA gene  39. Identical sequences were merged with VSEARCH  40  , followed by clustering with Swarm 41. Subsequently, chimera detection was conducted using the --uchime_denovo function in VSEARCH  42  , and low-quality sequences were filtered out.
The final set of ASVs was obtained based on occurrence patterns, utilizing a modified version of the Lulu algorithm  43  , which can be found at https:// github. com/ frede ric-mahe/ mumu. Taxonomic classification of the ASVs was performed using the curated EukRibo database version 1. 0  44  , employing the global pairwise alignment approach (--usearch_global from VSEARCH). This database was generated by the UniEuk consortium  37. A phylogenetic placement method was applied to ASVs initially affiliated with Perkinsea  45, 46. The sequences were added to the reference alignment using MAFFT with the --addfragments and --keeplength parameters and then placed onto the reference tree using the evolutionary placement approach EPA-ng v0. 3. 8  46. The results were subsequently analyzed using Gappa  47.
'Soil' samples were divided into 'Cropland (Temperate/Tropical)' , 'Forest (Temperate/Tropical)' , Grassland (Temperate) and 'Land soil (Temperate)'. To analyze the composition of Perkinsea diversity, a non-metric multidimensional scaling ordination plot (NMDS) based on the Bray-Curtis dissimilarity was conducted using the vegan package v2. 5-7  49  Samples with more than 10,000 reads and at least one Perkinsea ASV were extracted from the EukBank dataset and rarefied using the rrarefy function from vegan package. From each environmental type, 15 samples with the highest Shannon diversity were selected to create a subset of samples representing the highest observed diversity. This approach was adopted to avoid outliers, as many samples contained only one or a few ASVs with very low abundance.
Subsequently, the samples were transformed using the Hellinger method  50  with the decostand function, and the NMDS analysis was performed using the Bray-Curtis dissimilarity index with the metaNMDS function. To test the hypothesis of significant difference between the communities of the main categories and between environments, we used a non-parametric test of significant difference ANOSIM (ANalysis Of Similarities) with the function anosim from the vegan package and pairwise. adonis from pairwiseAdonis package 51 using the parameters sim. method = bray. For the community structure analysis based on phylogenetic distance, the Picante and Phyloseq R packages were used  52, 53. The alignment of the ASVs and the outgroup sequences, obtained from the previous Perkinsea ASVs phylogenetic classification, was extracted.
The best-ML tree and the rarefy table were used to calculate the sample distance using the weighted UniFrac method. The same samples used in the NMDS analysis were also employed for a principal coordinates analysis (PCoA). Additionally, Faith's Phylogenetic Diversity (PD)  54  and the Nearest Relative Index (NRI)  55  were calculated for each environment. The phylogenetic difference between the environments was tested with pairwise. adonis using the UniFrac weighted distance matrix. To explore the global distribution of Perkinsea, the proportion of ASVs related to each taxonomic group observed in the different geographical regions was plotted on a world map. The environmental preferences or occurrences were investigated by calculating the number of ASVs observed in each environment, represented using a chord diagram created with the R package circlize 56.
ASVs related to potential novel Perkinsea organisms To test the novelty of each ASV, two criteria were selected: the similarity of each ASV to its closest related sequence from a curated Perkinsea dataset of sequences obtained from NCBI and the likelihood weight ratio (LWR) derived from the placement of the ASVs into the reference phylogenetic tree. The LWR serves as an indicator of the confidence in the placement of the ASVs within the phylogenetic tree  47. A low LWR value suggests a less reliable representation of the ASV's taxonomy in the phylogeny. Since our phylogeny aimed to encompass the known diversity of Perkinsea, ASVs with low similarity to reference sequences from the database and low LWR are likely associated with potential novel organisms whose taxonomy is unknown. We classified an ASV as a novel if its similarity percentage was below the mean similarity with the reference dataset (< 94.
Xcellidae distribution in the open ocean In our dataset, we identified a notable presence of ASVs related to Xcellidae organisms, which were unexpectedly and widely distributed in the deep open ocean (below 200 depth meters). To further investigate this enigmatic group, we focused on the published dataset from the Malaspina 2010 Circumnavigation Expedition  58. This dataset is unique because it includes a wide range of marine open ocean samples from surface to bathypelagic zone. For each sample, the V4 region of the SSU rRNA gene derived from DNA and RNA templates of the pico-eukaryotic fraction (between 0. 2 and 3 µm) was sequenced. Samples were processed using DADA2  59  and taxonomically annotated as described in Obiol et al. 60. ASVs related to Perkinsea were extracted and subjected to the same phylogenetic analyses, as described above, to confirm their affiliations within the Perkinsea lineage.
Phylogenetic classification of environmental Perkinsea sequences Using a phylogenetic approach, we investigated the diversity and community structure of Perkinsea ASV sequences retrieved from the published EukBank dataset  37. We first conducted a phylogenetic analysis using a reference tree derived from an alignment of 1790 characters, which included representative alveolate groups and environmental sequences. This analysis successfully retrieved all major clusters described in the literature, including NAG01, Perkinsidae, Xcellidae, Parviluciferaceae, and environmental clusters (referred to as 'Perkinsea environmental cluster 01-04'). These clusters exhibited robust node support in both maximum-likelihood and Bayesian inferences (TBE > 0. 8 and PP > 0. 90, Fig. S1 ). However, a few sequences, such as M.
nigrum (MN721813. 1 and MN721814. 1) and P. dinoexitiosum (MZ663823. 1 and MZ663830. 1), displayed low branch support, underscoring the uncertainty in their phylogenetic placement as described in previous analyses  12, 13. From the EukBank dataset, we extracted 1647 ASVs preliminary classified as Perkinsea group using a pipeline developed by the EukBank consortium (https:// unieuk. net/). The taxonomic affiliation was checked using phylogenetic analysis with our reference dataset. Out of the 1647 ASVs, the phylogenetic analysis confirmed that 1568 ASVs were not chimeras and branched with known Perkinsea. Perkinsea ASVs represented 1,075,904 reads and were found across 4034 samples. These ASVs were placed throughout the phylogenetic tree, illustrating the extensive diversity retrieved within the Perkinsea lineage (Fig.
Xcellidae distribution in open oceans One noteworthy finding was that the ASVs related to Xcellidae were mostly detected in the Mesopelagic zone (between 200 and 1000 depth meters) and exhibited a global distribution across all the different oceans (Fig. S6 ). However, it is important to consider that the detected eDNA could originate from various sources, including free-living organisms, potentially infected host organisms, metabolically inactive or dead cells, or free DNA  28. To investigate this hypothesis, we conducted a case study using the published Malaspina Expedition circumnavigation dataset. The expedition took place from 2010 to 2011 and involved sampling the tropical and subtropical regions of the Atlantic, Indian, and Pacific Oceans. This expedition is unique because (i) it covered vertical water column profiles spanning seven depth meters, ranging from the surface to 4000 depth meters and (ii) both rDNA and rRNA were used as templates for environmental sequencing.
Using this dataset, we investigated the 'relative' ribosomal activity of Xcellidae in marine samples using DNA and RNA-derived sequencing  58. We identified 90 ASVs related to Xcellidae. These ASVs contributed to less than 0. 1% of the total reads in both DNA and RNA samples. We analyzed the rRNA/rDNA ratio, which can be a proxy for the 'relative' ribosomal activity. Our results highlight that, in most mesopelagic samples, the Xcellidae ASVs exhibited higher relative rRNA contributions than rDNA. This indicates a potential active ribosomal activity, suggesting the presence of putatively living organisms related to Xcellidae in the mesopelagic zone of the marine water column (Fig. S8 ).
Discussion In recent years, high-throughput sequencing or metabarcoding has been extensively used in eDNA surveys to investigate the diversity and distribution of protists  61. The availability of large datasets, such as the EukBank project, which compiles 13,055 unique samples from a wide range of habitats worldwide (including marine, land water and soil), as well as the MetaPR2 dataset comprising over 5036 samples for the V4 region and 1166 samples for the V9 region of the SSU rRNA  62  , presents a unique and valuable opportunity to explore the community structure of Perkinsea in a wide range of ecosystems.
6% of similarity) to known species, with only one ASV classified as novel (Fig. S7 ). Until now, only five species have been described within the Xcellidae family as parasites of 20 fish species belonging to five orders of teleosts  8. This result potentially indicates the presence of sequences related to species that are currently not represented in public databases. The detection of novel ASVs with low sequence similarity to known species within all known groups, including Perkinsidae, Parviluciferaceae, and Xcellidae, as well as those without any known taxonomic group, suggests that the diversity within Perkinsea may be much wider than previously thought. This is important because it raises intriguing questions regarding these putative organisms' nature (e. g. , symbiotic or free-living).
The Xcellidae group has remained enigmatic as it has not been previously reported in environmental surveys, except for only one ASV from the bathypelagic zone of the Gulf of California  15. However, our analysis has provided, for the first time, the distribution of genetic signatures related to Xcellidae in open ocean samples, particularly in the mesopelagic zone (between 200 and 1000 depth zone), where these sequences dominate the Perkinsea dataset. The mesopelagic zone is a particular zone of the water column as it harbors the largest biomass of fish in open ocean  74. Therefore, the presence of Xcellidae genetic signatures in this zone raises the possibility of these sequences being associated with infectious agents of fish. Indeed, Freeman et al. 15  proposed that Xcellidae infections may occur through contact between fish and the benthos or fish-to-fish transmission.
However, this study analyzed ASVs derived from DNA templates that could correspond to eDNA, free-living life stages released during their life cycle, and living or dead hosts. To overcome this issue, we analyzed the Malaspina dataset, which includes sequences derived from DNA and RNA templates. We showed that most of the Xcellidae ASVs were also recovered from the RNA template in the water column, suggesting the presence of potentially 'ribosomally' active life stages (Fig. S8 ). These findings provide valuable insights into the potential activity and ecological significance of putative organisms related to Xcellidae in the mesopelagic zone of the open ocean.
1 ). Only coral colonies entirely visible in the videos were considered, excluding those only partially visible. Information on coral taxonomic identity, morphotype, size, and position within transects are recorded in a data spreadsheet, enabling the characterization of coral community structure for each survey as well as population dynamics between surveys. Individual coral performances in terms of survival, growth, and recruitment are characterized as described in  Kayal et al. (2015). New recruits were defined as small corals absent from the 2021 video transects and visible in 2022. Other coral dynamics (fragmentation, fission, fusion, and migration) were also quantified, but not considered in this study. Data extractions from the video transects were coded in Python complemented with OpenCv (https://opencv.
Data analysis For each year (2021 and 2022), we characterized coral community abundance, composition, and size-distribution of the six dominant coral genera, for which we used individual coral dynamics between the two surveys to estimate annual demographic performances in survival, growth, and recruitment. We accounted for size-dependent variation in our estimations of coral survival by relating survival probability to year y+1 to initial size at year y using generalized linear mixed-effect models accounting for random effects of longitudinal observations on individual colonies  (Kayal et al. 2015). Similarly, size-dependent growth was characterized by relating final size at year y+1 to initial size at year y. Coral sizes were logtransformed to reduce data dispersion, and the models were checked for assumptions of residual homogeneity.
Results The main source of data for the grid-level analysis is the Gridded Population of the World (GPW) collection, now in its fourth version (GPWv4). This dataset models the distribution of the human population (counts and densities) on a continuous global raster surface and we match it with climate data of a similar resolution, thus effectively dropping locations that are not inhabited, or are projected to be inhabited. Since the release of the first version of this global population surface in 1995, the essential inputs to GPW have been population census tables and corresponding geographic boundaries. The purpose of GPW is to provide a spatially disaggregated population layer that is compatible with datasets from social, economic, and earth science disciplines, as well as remote sensing.