dataesr/scientific-paragraphs-categorization huggingface dataset:
2005; Munzner 2014). A substantial part of the human brain's capacity is allocated to processing images, so it makes sense to invest in good visualisations. A well-established literature-and techniques such as layout algorithms-help us define what a "good" network visualisation is  Herman et al. (2000). 2 Reinforce reproducibility and transparency. Reproducibility means that applying the same technique to the same dataset will always produce the same interpretive result (even if the technique has a stochastic component). Transparency means that how the technique operates is clear to the researcher, who can therefore assess which technique best suits her purpose, and explain that assessment to her peers. 3 Not foreclose the possibility of updating via abductive reasoning. Algorithms alone do not decide how parameters should be set to get optimal readability.
Readability of a dataset representing an ethnographic corpus depends on the research question that the researcher brings to those data. By implication, the values of the parameters must be co-determined by the ethnographers, who possess rich empirical and theoretical knowledge of relevant contexts. 3 4 Combine harmoniously with other steps of the data processing cycle, such as coding and network construction. This means making sure that the interpretations of the data and their network representation are consistent across the whole cycle. With that in mind, we turn introducing our candidate techniques. We claim that all of them satisfy more or less equally Conditions 3 (parameters are set by the researchers), 4 (in ethnographic research, representing a corpus as a network of co-occurring codes is an established technique, and ethnographers find visual analysis of such networks fairly intuitive and compelling), and the reproducibility condition in 2 (the only stochastic components come into play in layout algorithms, and they produce visually equivalent layouts).
Setting b * = 2 -which means only discarding "idiosyncratic" edges -leads to an 85-90% decrease in the number of edges. Setting b * to 4 leads to a decrease of about two orders of magnitude in the number of edges. Figure  3b  shows the decrease in network density (number of edges divided by the number of nodes) as the technique is applied with increasing values of association depth b *. Discarding edges with b(e) = 1 reduces density by 70-75%, but again in two out of our three datasets they remain well above the value of 4 edges per node.
Simmelian backbone extraction Another way to exploit the network's topology to identify its most important edges is to extract its Simmelian backbone. A network's Simmelian backbone is the subset of its edges which display the highest values of a property called redundancy  (Nick et al. 2013 ). An edge is redundant if it is part of multiple triangles. The idea is that, if two nodes have many common neighbours, the connection between the two is structural. This method This method requires we choose a value for a granularity parameter γ. We empirically set γ to be equal to the average degree of the CCN induced from each dataset, rounded to the nearest integer. For each pair of codes n 1 , n 2 in the network, redundancy is com- puted as the overlap between the γ strongest-tied neighbours of n 1 and those of n 2.
At this point, the network can be simplified based on the redundancy value of each edge. We start dropping the lowest-redundancy edges, then gradually increase the redundancy threshold to obtain smaller and smaller networks. As we drop edges with higher and higher redundancy, the number of nodes decreases, but not very rapidly and with a more or less linear pattern for all datasets. The number of edges drops rapidly for low values of the minimum redundancy, but then decreases much more slowly when the network's minimum redundancy rises above 5. Consequently, edge density sees a rapid drop in the early phases of the simplification, after which it becomes more or less constant. Throughout the simplification process, the density of all three datasets stays over the threshold value of 4 (Fig. 5 ).
In this section we compare the relative merits of the two strategies and four techniques, based on the criteria set at the beginning of this section. We discuss four aspects: interpretation of the simplification techniques themselves; harmonious integration with  the pre-and post-simplification phases of the data processing cycle; quantitative effectiveness; and preservation of structural information in the simplified networks. This discussion is summarised in Table  4  and exemplified by Figs. 6  and 7. The two simplification techniques based on edge weight are likely to be more intuitive to qualitative researchers without extensive training in network analysis. The measures of edge weight we adopted are grounded in the familiar practice of ethnographic coding; this results in straightforward definitions of what a strong edge is, and how simplified networks are obtained.
We now turn to how well structural information is preserved through network simplification. The first two methods yield simplified networks that preserve structural information, in the sense that their structure are not pre-determined by the method themselves: for example, the modularity  (Newman and Girvan 2004)  of the simplified networks varies across our different datasets. The third and fourth method use topological information for the simplification process itself, and they both predetermine the structure of the simplified network. Simplifying a CCN to the subnetwork formed by the codes with the highest core value invariably leads to a very dense network. The best a human analyst can do with it is ignore the edges altogether, and treat it as a list of important codes. Simplifying it to a Simmelian backbone invariably leads to highly modular simplified networks.
Do different techniques select the same codes and edges? A priori, we expect different techniques to select into the simplified networks codes and co-occurrence edges that are different, but not completely different from technique to technique. Different techniques prioritise different edges, and, therefore, codes. At the same time, the key co-occurrences are likely to meet the criteria of every technique. In order to quantify the extent to which different techniques converge onto the same set of codes and edges, we proceed as follows. First, we apply each of the four techniques to each of our three datasets. For each technique-dataset pair, we compute a maximal interpretable network  (MIN). By this we mean the largest network that is still interpretable by a human analyst. We then take the four MINs of each dataset, and compare them pairwise by computing the Jaccard indices on their nodes and edges.
An application We use a subset of the POPREBEL corpus to show how each of the four simplification techniques can be seen as broadly corresponding to a paradigm in anthropology -a convergence that attests to the utility of such a synthesis. This application is not meant as a full methodological primer. Rather, it means to be a "proof of concept", and shows the possibilities of synthesising quantitative and qualitative techniques in the service of ethnographic insight. More applications can be found in  Davidov et al. (2022). These data were gathered in the spring and summer of 2021  (Cottica et al. 2022). They consist of 17 semi-structured interviews with Polish-speaking Internet users, who used social media to seek and share information about health against the backdrop of the COVID-19 pandemic.
Availability of data and materials The (pseudonymized) datasets analysed during the current study are available in the Zenodo repositories:  Cottica and Melançon (2016) , Cottica and Hassoun (2021a), Cottica and Hassoun (2021b), Cottica et al. (2022). The codes co-occurrence networks were built from those same data, simplified, and visualized using Tulip. Both the Tulip graph file and the Python code to run the different simplifications algorithms under Tulip are available at https:// doi. org/ 10. 5281/ zenodo. 78808 25/. The Python script is designed to be used inside the Tulip Python IDE. It builds a graph hierarchy which is used to get the number of nodes and edges for each simplification step to eventually construct Figures 2 to 5.
g. ,  Shinevar et al. , 2015). Nonetheless, this assumption is not substantiated by experimental data on the deformation of calcic-amphibole under deep-crustal temperature and pressure conditions. To address this issue, we have conducted axial compression experiments on hot-pressed polycrystalline aggregates composed of 80 wt. % calcic-amphibole (pargasite in Table  S1  in Supporting Information S1) and 20 wt. % garnet with average grain sizes ranging from 30 to 75 μm at temperatures of 750-1,050°C, confining pressures of 1. 8 GPa, and constant strain rates varying from 1. 0 × 10 -4 s -1 to 6. 0 × 10 -6 s -1. To assess the influence of mineral mode on amphibolite rheology, we also performed experiments on polycrystalline aggregates comprising either 100% amphibole or 50% amphibole +50% plagioclase.
All experiments were performed in a 5 GPa modified Griggs deformation apparatus. The experimental conditions and mechanical data are summarized in Table  S2  in Supporting Information S1. After the experiments, samples were cut and polished for microstructure measurement. Examination of the microstructures and CPO of the deformed specimens facilitated the characterization of prevailing deformation mechanisms in each mineral and elucidated the variations in strength. See Texts S1 and S2, Figures  S1-S3  in Supporting Information S1 for experimental details and analytical methods.
Acknowledgments We are grateful to  Chujian Liu  for the fabrication of parts for sample assemblies. Wenjing Li  and  Zhonghang Wang  are thanked for their guidance on HT/HP experiments and EBSD measurements. C. Nevado  and  D. Delmas  prepared high-quality polished thin sections for EBSD measurements. This work was supported by the  National Natural Science Foundation of China  (Grant  42172250 ), the  Postdoctoral Research Foundation of China  ( 2021M701563 ), the  MOST  special fund from the  State Key Laboratory of GPMR , and the  Shenzhen Science, Technology and Innovation Commission Program  ( 20220815114624002 ).
Data Availability Statement Data: The raw stress-strain data and EBSD data (CTF files) are available from the Figshare Repository: https://doi. org/10. 6084/m9. figshare. 20227329. Software: The publicly accessible MATLAB (MATLAB, 2021a) toolbox, MTEX, that was used to perform the analysis of EBSD data is openly available for download: https://mtex-toolbox. github. io/.
Lentinula is a broadly distributed group of fungi that contains the cultivated shiitake mushroom, L. edodes. We sequenced 24 genomes representing eight described species and several unnamed lineages of Lentinula from 15 countries on four continents. Lentinula comprises four major clades that arose in the Oligocene, three in the Americas and one in Asia-Australasia. To expand sampling of shiitake mushrooms, we assembled 60 genomes of L. edodes from China that were previously published as raw Illumina reads and added them to our dataset. Lentinula edodes sensu lato (s. lat. ) contains three lineages that may warrant recognition as species, one including a single isolate from Nepal that is the sister group to the rest of L. edodes s. lat. , a second with 20 cultivars and 12 wild isolates from China, Japan, Korea, and the Russian Far East, and a third with 28 wild isolates from China, Thailand, and Vietnam.
Results Twenty-Four New Lentinula Genomes. We sequenced the genomes of 11 Lentinula individuals from Asia-Australasia and 13 individuals from the Americas, which correspond to 11 of the lineages that are resolved with rRNA and tef1-α genes (SI Appendix, Table  S1 )  (10). Seven genomes were assembled using PacBio long reads, while the others were produced using Illumina. Genome completeness as estimated by BUSCO 5. 3. 2 (Benchmarking Universal Single-Copy Orthologs) (  26 ) is 98. 80 to 99. 93% with an average of 99. 51% (OrthoDB v10, Basidiomycota)  (27). All but L. aciculospora are haploid genomes. We added four published genomes (three L. edodes and one L. novae-zelandiae)  (13, 16, 18)  for a core comparative dataset of 28 Lentinula genomes (Dataset S1). Lentinula genomes average 40. 48 Mbp (Figs.
1  and 2 ). Lentinula aciculospora JLM2183 has the largest genome (56. 71 Mbp), but almost half the genome (45. 97%) is composed of transposable elements. Lentinula raphanica has the lowest repeat content (0. 58 to 10. 86%) but more coding regions with thousands more genes than the other species. The sizes and repeat contents of Lentinula genomes are comparable to those of other basidiomycetes  (28, 29). To characterize gene presence/absence variation (PAV) across Lentinula, we used an orthology-based approach. We considered orthogroups to be present in a strain if at least one protein-coding sequence was assigned to that orthogroup by OrthoFinder  (30)  without considering gene length or copy number. The core genome of Lentinula comprises 5,329 orthogroups that are shared among all included strains, while 9,763 orthogroups show PAV across the dataset without being restricted to any monophyletic group.
Orthogroups unique to lineages are rare, except in L. raphanica, which has 636 lineage-exclusive orthogroups containing 809 to 868 genes per genome (SI Appendix, Figs. S1  and S2 ). Lentinula Has a Conserved White Rot Decay Apparatus. Substrate use efficiency is a key determinant of productivity in cultivation. Accordingly, we characterized genes encoding plant cell wall degrading enzymes (PCWDEs), including carbohydrate-active enzymes (CAZys)  (31)  and decay-related oxidoreductases, as well as cytochrome P450s (CYPs) (SI Appendix, Table  S2 , Figs. S3-S12, and Datasets S2 and S3). The numbers of CAZy genes in L. raphanica (average 480) and other species of Lentinula (average 477) were not significantly different (P value of the rank-sum test: 0. 52). Overall, the composition of PCWDEs and CYPs is highly uniform across Lentinula species, reflecting their conserved ecological role as white rot saprotrophs on hardwood substrates, mainly Fagales.
S15 ). These genes are up-regulated in fruiting bodies, suggesting that they may be responsible for biosynthesis of lenthionine and formaldehyde. Six alternative phylogenetic topologies with varying placements of L. aciculospora and L. aff. lateritia were rejected using topology constraint tests implemented in IQ-TREE V. 1. 6. 1  (35)  (SI Appendix, Fig. S19  and Table  S4 ). Moreover, the same tree topology was recovered in analyses using six different subsampled versions of the dataset, which were partitioned according to alignment length, DVMC, median long-branch score, percent parsimony informative sites, saturation, and treeness divided by RCV. Examination of long-branch scores per taxon  (36)  suggests that long-branch attraction is not a likely source of error (SI Appendix, Fig. S20 ).
At ε = 0. 0002, L. raphanica collapsed to a single species cluster, and L. lateritia and L. novae-zelandiae were lumped into a single species (yielding eleven species). At ε = 0. 0003, L. edodes s. lat. also collapsed to a single species cluster (yielding nine species). Cultivated Strains Are Concentrated in One Lineage of L. edodes s. lat. To expand sampling of L. edodes s. lat. , we assembled and annotated 60 genomes (21 cultivated and 39 wild) from Illumina reads published by Xiao et al. (19)  (SI Appendix, Fig. S21 ). Concatenated phylogenetic analyses  (39)  and species tree analyses (40) yielded the same major topology as that obtained with the core dataset (SI Appendix, Figs. S22  and 23 ). Within L. edodes s. lat. , three lineages were resolved, including 1) a "cultivars + wild" (CW) group   19 ) plus the two southeast Asian strains reported here, and 3) the Nepalese isolate HRB7682, which was again placed as the sister group of the rest of L.
edodes s. lat. The CW, W, and Nepalese groups in L. edodes s. lat. correspond to the three species resolved with the 28 genomes of the core comparative dataset under the MSC model. In addition to these, a paraphyletic "mixed" (M) assemblage of genomes was resolved, comprising seven wild isolates and one cultivar (ZP85) from the study of Xiao et al. (SI Appendix, Figs. S22  and S23 ). The CW, W, and M groups have broadly overlapping geographic ranges in continental Asia and correspond to the cultivars, wild 1, and wild 2 groups, respectively, delimited by Zhang et al. (17).
Lentinula edodes s. lat. Contains At Least Two Admixed Populations. To assess population structure in L. edodes s. lat. , we constructed two SNP datasets, one containing 3 × 10 6 SNPs scored from genome assemblies and the other with 8,777 SNPs scored from Illumina reads (Fig. 4  and SI Appendix, Fig. S24 ). SplitsTree  (41)  analyses divided the W and CW groups into two widely separated clusters, with M1 and M2 intermediates, which is consistent with gene-based phylogenies (SI Appendix, Figs. S22  and S23 ). In Admixture analyses at K = 2 (the optimal value for the number of subpopulations) or K = 3, all eight individuals from the M group contain a mixture of SNPs assigned to the CW and W groups, but at K = 4, the six isolates of M2 are resolved as a unique population, while the two isolates of M1 (YS84 and YS88) are still resolved as mixtures of the CW and W groups (Fig.
5 ), which is consistent with the population structure resolved with Admixture. In an unconstrained analysis, where the M groups were not designated as hybrids, the best model suggested introgression between the CW group of L. edodes s. lat. and the ancestor, the Asian-Australasian clade, but there is no support for this scenario in other analyses. ITS Is Polymorphic in L. edodes s. lat. Phylogenetic analyses of 51 ITS sequences extracted from genomes or downloaded from GenBank resolved 16 terminal groups in Lentinula, including the 15 groups identified by Menolli et al. (10)  and the Vietnamese L. platinedodes (2) (SI Appendix, Fig. S25 ). Our genome dataset includes exemplars of eleven of the ITS-based lineages (SI Appendix, Table  S1 ). Nine of the ITS-based lineages are congruent with genome-based lineages.
raphanica, which is reported on eight orders of angiosperms, including Fagales  (5, 10). Lentinula raphanica is also notable for its broad geographic distribution, from the Gulf Coast of North America to the southern Atlantic coast of Brazil  (10). The genome of L. raphanica contains 636 orthogroups that are not found in any other Lentinula species, including 507 with no known homologues in any organism when aligned with the NCBI nr database (May, 2021; Fig. 2  and SI Appendix, Fig. S1 ). It is not clear whether the expansion of L. raphanica onto new substrates in diverse habitats is related to the acquisition of the "orphan" genes in this group. The uniformity of PCWDEs and CYPs across Lentinula, including L. raphanica, suggests that adaptation to new hosts and environments is not correlated with gains or losses of genes encoding known groups of decay-related enzymes (SI Appendix, Figs.
S3-S12, Table  S2 , and Datasets S2 and S3). Fossils and molecular clock analyses suggest that Fagales originated in the Cretaceous period  (44) , making it plausible that they include the original substrates for Lentinula. Species of Lentinula are often described as having flavors and aromas reminiscent of cabbages, radishes, or garlic, which are due to the presence of the organosulfur compound lenthionine. (Indeed, the specific epithet raphanica alludes to radishes. ) The unique tandem and distal duplications of genes encoding γ-glutamyl transpeptidase may be responsible for this trait and thus are potential targets for future breeding efforts (SI Appendix, Figs.
Mating Compatibility Underestimates Species Diversity in Lentinula. The number of species in Lentinula has been controversial. To estimate species limits, we performed Bayesian analyses under the MSC model using the 28-genome core comparative dataset. However, such approaches have been criticized on the grounds that they may misinterpret population structure as evidence for species boundaries  (45, 46). Moreover, the number of species clusters that we obtained was sensitive to collapse height (ε) settings in STACEY ranging from thirteen at ε = 0. 0001 to nine at ε = 0. 0003 (Fig. 3 ). To evaluate alternative scenarios, we referred to prior taxonomy. Six described (or provisional) species were resolved in all STACEY analyses, including L. aff. lateritia, L. aciculospora, and the four species of the L.
Lentinula novae-zelandiae (2) Lentinula aff. lateritia G4 (2) Lentinula raphanica  (6)  Lentinula boryana complex (6)  There is no taxonomic precedent for splitting L. raphanica or L. edodes, but L. lateritia and L. novae-zelandiae were recognized as discrete species in the monograph of Lentinula by Pegler  (1). Lentinula novae-zelandiae has a stipe covered with fibrillose scales and a wrinkled pileus, whereas L. lateritia is smooth, and the two species are geographically isolated. Accordingly, we consider the 13-species scenario obtained at ε = 0. 0001 to be the most plausible. Under that scenario, the W and CW groups and isolate HRB7682 from Nepal of L. edodes s. lat. would represent three independent species (exemplars of M1 and M2 were not included in the core dataset) (Fig. 3 ). Geographic ranges of the CW and W groups broadly overlap in central and southwest China (particularly Yunnan, Sichuan, and Shaanxi provinces; SI Appendix, Table  S1  and Dataset S1)  (17) , but the potential for movement of cultivars makes it difficult to assess whether strains of the CW group collected in the wild are truly native or are escaped cultivars, as suggested by Zhang et al.
Genome Sequencing and Annotation. Genome and transcriptome sequencing were performed under the auspices of the Department of Energy Joint Genome Institute (JGI) Community Science Program at the JGI in Walnut Creek and Berkeley, CA. Genomes were sequenced using Illumina and PacBio technologies and assembled with Falcon 0. 7. 3 and 1. 8. 8 (PacBio) and SPAdes 3. 12. 0 and 3. 13. 0 or Velvet 1. 2. 07 (Illumina). All genomes were annotated using the JGI Annotation Pipeline and made available via the JGI fungal genome portal MycoCosm (http://mycocosm. jgi. doe. gov/Lentinula) (56). The 60 L. edodes s. lat. genomes published as unassembled Illumina reads by Xiao et al. (19)  were assembled using SPAdes v. 3. 13  (57)  with reference to the L. edodes W1-26 genome (13) and assessed using BUSCO 5. 3.
2 with the OrthoDB v10 Basidiomycota database (SI Appendix). Analyses of Functional Genes. Genes encoding PCWDEs in a core dataset of 28 Lentinula genomes were annotated using the CAZy pipeline  (31). Genes encoding decay-related oxidoreductases (class II peroxidases [POD], glucose-choline-methanol oxidoreductases, copper radical oxidases, and multicopper oxidases) were further analyzed by querying Lentinula genomes with well-characterized genes from various Agaricomycetes (SI Appendix) or by searching according to the EuKaryotic Orthologous Groups (KOG) classification, followed by phylogenetic and sequence-based structural analyses. Phylogenetic analyses of genes encoding class II peroxidases from Lentinula and 28 other Agaricales genomes analyzed by Ruiz-Dueñas et al. (58)  were performed using five "generic peroxidase" sequences of Stagonospora nodorum (Pleosporales) to root the tree.