dataesr/scientific-paragraphs-categorization huggingface dataset:
cross-validation of inferred etymologies In order to assess the internal consistency in etymology assignments among authors, as well as the validity of inferred etymologies (which represented 49% of the dataset) as compared to original etymologies, we cross-validated the dataset. We based cross-validation on 400 randomly picked inferred etymologies (~1% of the total dataset). For each etymology, we checked the original description and scored whether our inference: (1) matched, i. e. there was a clue in the text that the inferred etymology fitted the given name, like in Nemosinga atra  Caporiacco, 1947 , for which the abdomen is described as 'completely black' in the original paper (ater, atra, -um meaning 'black, dark-coloured'); (2) did not match, i. e. there was evidence for another explanation to the given name in the original description; or (3) neither matched nor mismatched, i.
Assignment of species geographic distribution We used the distribution column in our backbone taxonomy to classify species distributions at the continental level. Distribution strings for species in the World Spider Catalog are not standardized: they can mention single countries (e. g. 'Namibia'), multiple countries (e. g. 'Italy and Spain'), continents (e. g. 'Oceania'), ranges (e. g. 'from Europe to Korea') and other geographical units (e. g. 'Alps', 'unknown'). We used the wscmap dataset from the R package 'arakno' v. 1. 1. 1  (Cardoso & Pekar, 2022)  to convert each distribution string into a list of corresponding three-letter ISO country codes. For example, we translated 'North America' to the list 'CAN, MEX, USA,' and 'Egypt to Yemen' to 'EGY, SAU, YEM'. Next, we used the R package 'countrycode  ' v.
1. 3. 0 (Arel-Bundock et al. , 2018)  to convert these lists of ISO codes into presence/absence values per continent ('Asia', 'Europe', 'Africa', 'Oceania' and 'Americas'). We manually checked the final output, filling unmatched distributions and fixing errors. Additionally, since the package 'countrycode' provides a generic presence/ absence value for 'Americas', we manually reclassified species in this category into 'North America' and 'South America'. As we were interested in assessing cultural differences, we considered any occurrence below the US/Mexican border as 'South America'. We acknowledge that this may oversimplify a few cases, for example, when considering the complex colonial history of certain countries, but overall we assumed errors so introduced to be minor.
We acknowledge that, because these categories must add to 1, their values are not fully independent. Statistical tests of trends in each should thus be interpreted with some caution. We performed a generalized additive model with the proportion of etymologies as a response variable, testing the effect of a smooth function of year of publication (a discrete variable from 1757 to 2019) in interaction with the etymology category. We used additive models in order to account for possible nonlinear trends of the sampling period. We ran the model with the R package 'mgcv' v. 1. 8-35  (Wood, 2004 ) assuming a binomial distribution, a logit link function and a thin plate regression spline. We estimated the optimal amount of smoothing through generalized cross-validation. As the initial model was overdispersed (dispersion ratio = 1.
Finally, we tested whether taxonomists are more likely to choose a given etymology category over others across continents. We constructed six generalized linear models, one for each etymology type, testing the effect of the factor continent on the use of a given etymology type. As the response variables were binary variables (0-1 discrete), we specified a Bernoulli distribution and a complementary log-log link function (clog-log), as recommended in  Zuur et al. (2009)  for datasets with unbalanced sets of zeros and ones. To avoid confounding effects of including species that occur across multiple continents, we used for the analysis the subset of species occurring in a single continent, resulting in a total sample size of 44 541 observations.
spider etymologies By the numBers We classified the meaning of 47 325 spider species and subspecies names, of which 44063 had a single meaning and 3262 multiple meanings. Most etymologies referred to morphology (41%, N = 20 702), geography (27%, N = 13 880) or were dedicated to people (19%, N = 9881) (Fig. 1A ). The most frequently used names across the database mostly belonged to these three categories (Fig. 2 ) as well. The least used etymologies belonged to the category Other (4%, N = 2128). Over 90% of names attributed to species had a single meaning (i. e. we classified them into a single subcategory). However, there were names with multiple meanings -typically two, although there were etymologies with up to four meanings. The average length of species epithets was eight letters (Box 1).
Indeed, the categories of morphology and geography were overwhelmingly represented across the database (Fig. 1B ). However, naming practice has changed: names based on morphology, as well as those based on ecology and behaviour, have both declined substantially since the late-19 th century. Interestingly, the same trend is seen in species etymologies in the plant genus Aloë L. (Figueiredo & Smith, 2010) : names from morphology have fallen by about half over the 20 th century, while names for geography and people have increased. Figueiredo and Smith speculate that this may reflect either decreasing knowledge of technical Latin or a saturation effect where simple morphological names have increasingly been used up. In spiders, at least, we suspect the latter is more likely. However, it is difficult to distinguish this hypothesis from an increasing receptivity to more creative naming, perhaps reflecting broader acceptance that science is, like all human enterprises, culturally situated.
To us, the creativity of taxonomists in devising new species names is inspirational and can foster reflection about the way we communicate science. Third, our database can be a starting point to explore important topics related to academic life, especially the timely debate around inequalities in the scientific community. An illustrative example comes from a recent work by  Trisos et al. (2021). They mapped Latin names of birds globally, showing that most of the species occurring outside Europe in formerly colonized countries are named after European surnames rather than indigenous ones. In our case, a subset of spider etymologies dedicated to people could be used to confirm the generality of this pattern beyond birds. Also, one could test whether there is a gender bias in the way taxonomists assign species names and if gender ratios in names are changing through time [as studied for plants by  Lindon et al.
(2015) ]. We believe the latter would be a timely endeavour, given that reflections around gender issues are gaining ground in the arachnological community  (Holmquist & Gillespie, 2022; Marusik & Sherwood, 2022 ; see also the multiple initiatives by the group SWA -Support Women in Arachnology; @SwaWomen). We leave the answers to these and other questions for future work, while welcoming anyone to explore and re-use the database uploaded alongside this submission. For now, we simply highlight two recent examples: Grymeus dharmapriyai  Ranasinghe & Benjamin, 2018, and Tmarus manojkaushalyai Ileperuma Arachchi & Benjamin, 2019 , both named by a young Sri Lankan woman as part of her taxonomic training. She named them for her husband  (Ranasinghe & Benjamin, 2018; Ileperuma Arachchi & Benjamin, 2019).
ACKNOWLEDGEMENTS We are grateful to the World Spider Catalog's team (https://wsc. nmbe. ch/boards) for their immense contribution to spider taxonomy. Special thanks to people who helped us with inferring etymologies in specific languages, namely  Malin Teräväinen  (Swedish) and  Svetlana Pétillon  (Russian). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  882221.
Results and discussion Synthesized field evidence for external cycling A comprehensive dataset obtained from an aircraft measurement campaign provided excellent insight into the external cycling of NO x and its perturbation on the oxidative capacity of the atmosphere  4, 32. Nineteen research flights were conducted onboard NSF/NCAR C-130 to collect measurements of NO x , HONO, nitric acid (HNO 3 ), particulate nitrate (pNO 3 ), some alkyl nitrates, PAN, radicals (OH, HO 2 , RO 2 ), ozone (O 3 ), volatile organic compounds (VOCs), aerosol size distributions, photolysis frequencies, and other meteorological parameters, mostly in various locations in the low-NO x troposphere (Methods), i. e. , from the pristine terrestrial and marine boundary layer (BL) to the FT (Fig. S1  and Table  S1 ). Herein, we defined low-NO x and high-NO x regimes with a NO x concentration threshold of 500 pptv, given that 500 pptv represented the upper limit concentration of NO x in the remote troposphere.
Furthermore, 500 pptv appeared to be a turning point for the external cycling to be key sources of HONO and NO x (see below). The data were collected from nineteen research flights without further screening and offered a more global representativity and atmospheric variability of the external cycling and therefore supported our analysis strategy for directly exploring the external cycling of NO x across high-to low-NO x atmospheres. In our previous publications, we exploited data from a limited number of research flights in forested areas (i. e. , RF4-5, 11, 17-18) and clean marine boundary layers (i. e. , RF14, 16) and discussed the budget of HONO and specific mechanism of the external cycling  4, 32. Several studies of this kind based on aircraft observations had generally employed the budget analysis methodology for HONO to conduct case studies in clean marine air or fire plumes  21, 34, 38.
The tropospheric chemistry mechanism was described in Parrella et al. (2012)  66. No further updates, including external cycling, were made. Emissions of atmospheric components included anthropogenic emissions from the Emissions Database for Global Atmospheric Research (https://edgar. jrc. ec. europa. eu), biogenic emissions of volatile organic carbons from the Model of Emissions of Gases and Aerosols from Nature inventory  67  , fire emissions from the Global Fire Emissions Database 68 , soil NO x emissions  69  , and lightning NO x sources  70. The dry deposition scheme used a resistance in-series model based on Wesely (1989)   71. The wet deposition scheme included the scavenging of soluble tracers in convective updrafts as well as the rainout and washout of soluble tracers  72. Simulation results corresponding to the research flight track during the same period were extracted for measurement comparison.
Acknowledgements This analysis and model work is supported by the  National Natural Science Foundation of China  (grants  41875151 , C. Y. ). We acknowledge the  U. S. National Natural Science Foundation  (grants  AGS-1216166  and  AGS -1826989 , X. Z. ) and members of the SAS project for supporting and providing the field measurements. We really appreciate  Xueling Meng  for her kind help in preparing Fig. 1.
Dlk2 regulates zebrafish neurogenesis Dlk2 encodes a unique transcript in the zebrafish genome. To evaluate the transcriptional landscape of Dlk2 through zebrafish brain development, publicly available single-cell RNA seq datasets derived from zebrafish heads harvested from embryonic to larval stages  11  were analyzed (Fig. 1a , Supplementary Table  1 ). Dlk2 was detected in both mesoderm and ectoderm derived tissues (Fig. 1a , Supplementary Table  1 ). Within the brain, the strongest expression was located in the forebrain, specifically in the telencephalon, diencephalon, hypothalamus, and habenulae. Gene expression was found in neuronal sub-populations, including GABAergic and glutamatergic neurons, progenitors, and radial glial cells (Supplementary Table  1 ). Dlk2 is expressed in the eye (cornea) from 20 hours post-fertilization (hpf) and in the retina (RGC) from 2 days post-fertilization (dpf) (Fig.
Dlk2 interacts with Numb The Dlk2 mutant phenotype mimics a Numb/Numblike knockout 3 and alters Notch signaling, both of which are known to play important roles in tumorigenesis. Consistent with this, Dlk2 was found to be differentially expressed in brain tumors of patients (Extended data Fig. 4a ). Furthermore, in both low-grade gliomas and glioblastomas, Dlk2 was significantly decreased. Since Notch signaling is upregulated in these cancers, we specifically compared the expression of Dlk2 to Notch receptors and canonical ligands in glioblastoma (Extended data Fig. 4b ). Strikingly, Dlk2 was significantly down-regulated. Jagged 2 was the only other significantly down-regulated gene. (Extended data Fig. 4b ). Survival rates of patients with low (below the median value) and high expression levels of Dlk2 with low-grade glioma from the Cancer Genome Atlas (TGCA) database were compared, and we found that a lower expression correlates with poorer survival (p=0.
001) (Extended data Fig. 4c ). As Dlk2 is generally lowly expressed in these tumors, another tumor model was investigated to assess its potential interaction with Numb. Significant research on Numb in breast cancer has also shown it to be deregulated in these tumors, with a downstream effect on Notch signaling  22- 24. To gain initial insight into whether Dlk2 is likely to play a functional role in breast cancer progression in patients, publicly available datasets were queried. Analysis of gene expression data from TCGA showed that both Dlk2 and Numb expressions were significantly decreased in tumor tissue relative to normal breast tissue GTEx data (Fig. 3a ). Assessment of expression levels relative to patient survival suggested that patients with a lower Dlk2 expression have a shortened survival compared to the high expression group (Fig.
4e ). These data suggest that Dlk2 and Numb are part of the same complex. In order to assess whether these proteins interact directly or indirectly, we gathered affinity and binding kinetics using surface plasmon resonance-based assay (SPR). We observed no specific interactions between Dlk2 and different negative controls (Fig. 3g ; Extended data Fig. 5a-d ). However, high-affinity interactions were observed between Dlk2 (Dlk2 extracellular domain (Dlk2EC)) and Numb with a KD value of »2. 4 nM, which was in a similar range to the interaction with the positive control Notch1 intracellular domain (NICD) (residues 2280-2550) and full-length Numb (KD »14. 3 nM) (Fig. 3h, i ). The full-length Dlk2 bound to Numb with similar affinity to the Dlk2EC (data not included). A known target for Numb as a tumor suppressor is the E3 ligase Mdm2, which, upon interaction, is unable to bind p53  25.
RNA isolation and RT-qPCR RNA was purified with the miRNeasy Micro Kit (Qiagen) according to the manufacturer's instructions. Reverse transcription was done using SuperScript III Reverse Transcriptase (Thermo Fisher Scientific) with Random Primers following the manufacturer's instructions. RT-qPCR reactions were performed using Brilliant III Ultra-Fast SYBR Green qPCR Master mix (Agilent Technologies) with the relevant primers (Sigma-Aldrich). Actbn2 (F-GGGCCTACCTCACTTTGAGC; R-CAACCAGTGCGGGAATTTCA), Mobk13 (F-AAGACTCCCAAAGAGTGCCC; R-CCCAGTTTGGCCACAGATGA), Dlk2 (F-GCCGATGTGTGACGACTGTG; R-GCTGTCTGGAGCACACGTAA). Data acquisition was done using the LightCycler480 machine (Roche) and QuantStudio 5 (Fisher Thermo Scientific) instruments. Analysis was performed by the delta-Ct method.
PCR and Kompetitive Allele Specific PCR (KASP) genotyping assay For rapid DNA extraction of zebrafish embryos/adult tail fin, samples were incubated in 25 μl of lysis buffer (25 mM NaOH with 0. 2 mM EDTA) for 30 min at 95°C. Following rapid centrifugation, 25 μl of neutralization buffer (40mM Tris-HCl) was added to the samples, and after brief vortexing, DNA was used directly for PCR or genotyping assay or stored at -20°C. For PCR, a DNA loading buffer was added to the DNA samples. Agarose gel electrophoresis was done using gels made with TBE (Thermo Scientific), with DNA visualized under UV. Kompetitive Allele Specific PCR (KASP) (LGC group) genotyping assay was done using predesigned primers for the mutated sequence (A/T) of zebrafish Dlk2 (LGC group). Data acquisition was done using the LightCycler480 machine (Roche) and Quant Studio 5 (Fisher Thermo Scientific) instruments.
Zebrafish husbandry and behavioral assays For Embryos were obtained from natural crosses and staged according to Kimmel et al  45. Zebrafish lines were maintained under standard procedures in tanks with circulating system water at 28°C under 14: Upon media change, deyolking buffer was added, and repeated pipetting followed by centrifugation was performed to remove the yolk. Disaggregation of zebrafish cells was done using the liberase enzyme (Roche). Incubation at room temperature for 20 min, including repeated pipetting, was concluded by the addition of fetal bovine serum (FBS) to the samples. Following centrifugation, cells were filtered through a 30-mm cell strainer, resuspended in ice-cold PBS, and labeled with DAPI to exclude dead cells. Data acquisition was performed using the Attune Flow cytometer (Thermo Fisher Scientific).
Biacore Reanalysis of the single-cell RNA-Seq data from Raj et al, 2020  11  was performed using R (v4. 2. 1) and R-Studio (v2022. 12. +353). From the Gene Expression Omnibus (GEO) data accession number GSE158142; the Robject files were downloaded for each time point and loaded into Seurat 46 (v4. 3. 0). The sample sheet containing metadata (RDS, Stage, Units, ClusteringResolution) for the Robjects was also downloaded in order to match the published annotations and resolutions for the dimensionality reduction. UMAP plots at the published resolution were plotted for specific genes of interest using FeaturePlot(). Average expression for published clusters and resolutions were extracted using AverageExpression() and written to XLS format files.
In silico analysis using TCGA patients datasets RNA expression levels and survival data were downloaded from the UALCAN web-portal  44  as well as http://gepia2. cancer-pku. cn and www. oncolnc. org web-portals to assess the Cancer Genome Atlas/Genotype-Tissue Expression (TCGA/GTEx) database. Data with the accession number GSE4290 were analyzed from the TCGA database https://portal. gdc. cancer. gov. shell scripts, and a Rscript download and recreated the gene-specific analyses via GitHub; We also provide gene-specific data and plot files.
Acknowledgments We thank  A. Flemming ,  A. Ashcroft ,  M. Keegan  for early discussions. S. Patel ,  A. D. Hay , and all the members of the  Ferguson-Smith laboratory  for helpful discussions and for excellent suggestions;  B. Buddenberg  and  J. Dagget  from  Zantiks Ltd , for their support with the zebrafish behavior tracking experiments. Biological Services Unit  staff for assistance and animal maintenance in the zebrafish facility ( University of Cambridge , PDN). This work was funded by the  UK Medical Research Council.
****P ≤ 0. 0001 by unpaired Student's t-test Dlk2 expression is downregulated in gliomas a, Expression level analysis of Dlk2 and Numb using TCGA and GTEX datasets (Gepia2 database) in normal brain tissue (n=30) compared to astrocytoma grade III (n=19), oligodendroglioma grade II (n=38), oligodendroglioma grade III (n=12), and glioblastoma grade IV (n=81). ****P ≤ 0. 0001 by unpaired Student's t-test. b, Fold change expression of notch signaling (receptors and ligands) in glioblastoma grade IV (n=81) TCGA datasets displayed on (a). c, Survival analysis by Kapan-Meier curves and log-rank (Mantel-Cox) test in low-grade glioma on the basis of the TCGA database http://www. oncolnc. org, comparing patients' survival in subgroups high and low for Dlk2 expression, **P = 0. 001. d, Coimmunoprecipitation (co-IP) experiment MDA-MB-231 cell lysates that do not express Dlk2.
2022). There, we highlight the affinity of each of the four techniques with a prominent method of analysis associated in turn with an identifiable school of thought in sociology or anthropology, using data from a study on Eastern European populism. Those results are summarised here in Sect. An application. The contribution of the present paper consists in (1) a complete consideration of how and why each technique for network simplification supports prominent methods in sociology and anthropology, and (2) a quantitative analysis of how the techniques in question perform on datasets constructed from three different ethnographic studies. We proceed as follows. After discussing work related to our own (Sect. Related work), we introduce the codes co-occurrence network, which is the network to be simplified (Sect.
Related work The turn towards big data, fuelled by improvements in computing power, has led to renewed faith in the ability of quantitative work to provide more generalisable and yet valid knowledge (that is, knowledge that preserves some of the richness of case-derived insights) than that obtainable by qualitative studies or quantitative projects relying on smaller numbers of cases  (Beaulieu and Leonelli 2021). This has led to undeniable progress. At the same time, however, it has highlighted a need for methodological robustness. As scientific work based on large datasets becomes methodologically innovative, more steps are needed to move from raw data 1 to final result. As a consequence, the methods themselves may become harder to check against the insights derived from intimate familiarity with specific cases.
All this leads to researchers obtaining divergent results depending on ostensibly innocent choices about data cleanup prior to analysis  (Decuyper et al. 2016). Even controlled experiments with different researchers working with the same datasets on the same research questions have led to spectacularly divergent results, for reasons that are not yet entirely clear  (Silberzahn et al. 2018; Breznau et al. 2021). Qualitative sociological and anthropological research is not expected to be replicable; rather, its claim to generating reliable knowledge comes from the rigour and accountability of the methods applied systematically and self-consciously to a specific case or a small range of cases in well specified spatial and temporal contexts. Therefore, careful, transparent choices about one's method are necessary at every step of the way, even more so when research applies mixed methods  (Beaulieu and Leonelli 2021).
Construction and interpretation Consider an ethnographic corpus. In what follows, we call any text data encoding the point of view of one informant (interview transcript, field notes, post on an online forum and so on) a contribution. Contributions are then coded by one or more ethnographers. Coding consists of associating snippets of the contribution's text to keywords or keyphrases, called codes. The set of all codes in a study constitutes an ontology of the key concepts emerging from the community being observed and pertinent to that study's research questions 2. We can think of such an annotated corpus as a two-mode network. Nodes are of two types, contributions and codes; the same code can be used to annotate multiple contributions, and to annotate the same contribution multiple times.
2017 ; https:// tulip. labri. fr) for all graph processing and proceed as follows: first, from each dataset we induce the relative CCN. The resulting CCNs are too large and dense for visual analysis (Table  2 ). Second, we apply to each CCN different techniques for network simplification. The techniques and the rationale for choosing them are the subject of the next section. All techniques considered apply a simplification algorithm, the effects of which can be calibrated using one or two tuning parameters. For each corpus and each technique, we then observe how varying the value of the tuning parameter influences the resulting simplified network. We attempt to find interpretations for choosing specific values of the tuning parameter. Next, for each corpus and each technique we compute the maximal interpretable simplified network.