dataesr/scientific-paragraphs-categorization huggingface dataset:
Results PassivePy and CoLA's human-coded labels agreed on 97. 3% of labels (Cohen's κ =. 85, SE =. 03). Of the 951 active-voice sentences, PassivePy labeled 925 sentences as active and 26 sentences as passive, a false-positive rate of 2. 7%. Similarly, of the total 92 passive-voice sentences, PassivePy labeled 2 as active and 90 as passive (i. e. , a false-negative rate of 2. 2%). This evidence suggests that against a dataset of human-coded texts, PassivePy identifies grammatical voice accurately and efficiently. Our first study provides an initial validation of PassivePy's aims and its computational procedure in a published dataset  (Warstadt & Bowman, 2019). However, the texts from the CoLA dataset are from a uniform and specialized setting (e. g. , sentences from theoretical linguistics publications).
Coding Procedure One of the authors and a graduate assistant trained in academic writing coded each sentence in the dataset independently, identifying if they were active (coded as 0) or passive (coded as 1). Both coders received additional training on grammatical voice before this task to ensure coding was performed as accurately as possible. The two coders' initial rate of agreement was 90. 2% (Cohen's κ =. 63). The disagreements (113 out of 1150) were resolved via discussion. A description of the final sample with human-coded labels is summarized in Table  4. We named this dataset as the "CloudResearch dataset" and have published it online and on the PassivePy's site (https://github. com/mitramir55/PassivePy). Together, in a second study using more naturally-produced and content-diverse texts, PassivePy correctly identified grammatical voice and remains a valid measure.
LIWC's auxiliary verbs' measure reached accuracy rates of 56% (CoLA dataset) and 51% (CloudResearch dataset). Similarly, SpaCy's auxiliary verbs' measure reached accuracy rates of 60% (CoLA dataset) and 53% (CloudResearch dataset). Compared to PassivePy, auxiliary verbs perform poorly in identifying passive voice. Benchmarking #2: Writing Clarity Calculator. Next, we benchmarked PassivePy against the "Writing Clarity" tool and website (http://writingclaritycalculator. com/) created by  Warren et al. (2021). This tool uses a pattern matching procedure searching for forms of the verb "to be" followed by a past participle, which is often susceptible to both false positive and false negative errors. For example, the sentences "I was exhausted" and "the resource was exhausted" will be treated similarly by this approach even though the former is not in passive voice and the latter is.
Similarly, this approach will fail to detect passive structures that use forms such as "get + past participle" or "become + past participle. " We randomly chose 50 sentences from each dataset used in our validation studies and manually entered each sentence into the "Writing Clarity" website and recorded the coding output by the tool. We then compared the coding by "Writing Clarity" and that of PassivePy with the human-coding (i. e. , ground truth). The "Writing Clarity" tool achieved a human-coding agreement rate of 86% in the CoLA dataset and 76% in the CloudResearch dataset. On the same samples, PassivePy achieved agreement rates of 94% and 100%, respectively. The accuracy of PassivePy outperforms the "Writing Clarity" tool.
DISCUSSION In this paper, we offer and validate a transparent, replicable, and reliable text analysis Moreover, companies and practitioners can also use PassivePy in their customer management and content management processes. For example, companies can use PassivePy to analyze reviews and complaints from their customers to identify those who have had a negative experience and those likely to escalate the complaint. As our linguistic records increase in offline and online spaces, it is critical to develop tools that can identify a range of language dimensions in a seamless, efficient, and reliable manner. Researchers are now using accessible, large-scale text corpora to address meaningful research questions with field data  (Boyd & Schwartz, 2021) , such as how people respond to trauma and emotional upheavals in psychology  (Bohner, 2001; Markowitz, 2021; Seraj et al.
PassivePy enables researchers active in these domains to automatically code for passive voice and use it as a moderator or a covariate to their focal effects. We offer PassivePy to revive the academic interest in grammatical voice, as its application to a range of subfields (e. g. , psychological distance, consumer psychology) suggests its potential as an important marker of social and psychological dynamics. We hope other scholars will take the base package from PassivePy and build on its capabilities for even greater functionality. PassivePy was created to improve our understanding of social and consumer psychology theory. This tool now opens new theoretical doors into the understanding of social and psychological phenomena through grammatical voice and word patterns. To this end, in the online documentation of PassivePy, we outline how first-time users (even with no coding expertise) can use the tool.
Limitations and Future Directions We have created a readymade website (https://passivepy. streamlit. app/) for PassivePy that offers the same functionality as the Python package but is limited in its analysis capacity. This website can analyze pasted text or uploaded spreadsheet files, producing an output display and a readable file ready for download and further analyses. For more advanced and computationally intensive applications, we suggest using the Python package. PassivePy, in its code form, requires some baseline level of computational knowledge. With our clear documentation and feedback from testers, however, we hope to have relieved this burden for most users. Researchers with some familiarity with Python or coding languages might perceive PassivePy as more approachable than those without coding experience.
Most social scientists in the natural language processing field are at least familiar with Python; the barrier to entry for this tool should therefore be relatively minor even for those without a coding background. Our validation studies attempted to recruit passive and active voice sentences from a range of settings. The results in this paper and associated accuracies between computation and human coding are germane to the texts analyzed, not all possible texts and topics. Therefore, we encourage future research to identify possible blind-spots of PassivePy to improve its functionality and improve accuracy. Still, we took great care to ensure the tool is counting grammatical voice accurately (e. g. , through part-of-speech tagging, dependency parsing, and rule-based matching). Moreover, in our benchmarking analyses, PassivePy outperforms all three tools and measures for passive voice.
As computational methods for natural language processing continue to improve, our package and its accuracies will improve in kind. Finally, our focus in this piece was on passive voice, but its counterpart -active voice -deserves treatment in future research. That is, if passive voice is linked to negativity as others describe, it stands to reason that active voice may be linked to positivity. Future studies should examine this contention directly, performing both field studies and experiments to test differences in passive and active voice across various consumer psychology contexts.
#3: Coh-Matrix. Finally, we benchmarked PassivePy against Coh-Matrix (Graesser et al. , 2011; McNamara et al. , 2014) , a common automated text analysis program that evaluates agentless passive voice. We analyzed all texts in both validation datasets using Coh-Matrix and recorded the agreement rates between the coding provided by Coh-Matrix and that of human coders. Coh-Matrix achieved an agreement rate of 96% in the CoLA dataset and 95% in the CloudResearch dataset. In comparison, these agreement rates for PassivePy were 97% and 98% across the two datasets, respectively. While Coh-Metrix provides accuracies akin to PassivePy, how the tool counts agentless passive voice is opaque and the tool is difficult to scale. Our slight improvement in accuracy, openness, and transparency are nontrivial benefits compared to Coh-Metrix.
Methods This first phase involved a quantitative analysis through the collection of data on publications, comments and followers that made it possible to know the rate of involvement, the rate of activism and the rate of reactivity. As underlined by the researchers Mazza and Palermo  (Sobry & Cernaianu, 2022) , the set of information collected is analysed according to a quantitative approach, by means of the construction of special indices to measure the degree of participation, engagement and conversation rates (PTAT). In particular, the Engagement rate was calculated, an indicator that allows us to understand the rate of user involvement. Knowing this index makes it possible to understand whether the communication strategies implemented adequately involve the users of the page. The Engagement rate was calculated using the following formula: PTAT/fans*100.
Types of content posted In this table dedicated to Facebook, it can be seen that january is a relatively calm month, in which no relevant data emerges for redBull, Alpine, and Alpha Tauri, for example. Almost half of the teams therefore do not initiate a communication from the outset, but the situation changes as early as February where values begin to be representative, and then in March, when the championship resumes, there is good engagement. On average, the rate is considered positive on Facebook when it is between 2% and 3% (Adobe, 2022). So we can say that although the start was slow, the teams in the first quarter are within the average. In Table  3  dedicated to Instagram, the situation is different. The Engagement rate has values that, although low, are well within the European average.
The unit of analysis was the news item. A news item was defined as a set of contiguous verbal, and, if applicable, audio and/or visual elements that refer to the same event/issue/person. All current news content in the sampled outlets was coded on those days, excluding opinion articles, reviews, and stories not produced by the journalists of that news outlet, e. g. , wire service stories. *in Qatar, two radio stations were originally included, but they had to be eliminated because it was found after data collection to be more closely aligned with a talk show than a news show, which violated our sampling criteria. Our sample consisted of a total of 148,474 news stories from 102 newspapers, 96 TV newscasts, 74 radio news programs, and 93 news websites.
Data Collection The sample search process, as well as the news item coding was done by native speakers in each country. National teams received extensive training during 2019 and 2020 to ensure a good understanding of the codebook that included operational definitions for each variable. Coders in each country coded each story directly into an online interface designed for that purpose or entered the data manually into an SPSS file. Based on Krippendorff's alpha (Ka), the final global intercoder reliability was. 79. The variation of intercoder reliability across roles ranged from. 76 to. 86, while the variation across countries ranged from. 72 to. 91. The content analysis also included data related to the type of media platform in which the news story was published, the specific outlet, the item's topic, and other potential influential factors at the individual, organizational, and societal levels.
SSA uses the mathematical properties of descending order diagonal matrices (Hankel, Toeplitz matrices, e. g. ,  [63] ) and their orthogonalization by singular value decomposition (SVD, e. g. ,  [64] ). In Figure  3a , we superimpose the SSA trends of declination in CLF in red, sea level at Brest in blue, and polar motion in black. We dealt with the sea-level series in Le Mouël et al. (  [65] ), in which we compared variations of the sea-level trends with the variations of Markowitz-Stoyko polar drift. We studied many aspects of polar motion in  [49, 66, 67]. The time derivatives of the three trends are shown in Figure  3b. We also used the long dataset of Stepheson and Morrison (  [68] ) and  Gross ([69] ) for lod and compare its trend with the first derivative of magnetic declination (Figure  3c ).
As is the case for all derivative operators, it amplifies high-frequency components. Le Mouël et al. (  [77] ) showed that the 11 yr component is a major component of the length of day, whereas Lopes et al. (  [66] ) showed that it is a minor one of polar motion. In order to retain the homogeneity of our datasets and their temporal resolution, we integrated the 11 yr quasi-cycle extracted from the length of day by SSA (Figure  10 , black curve, bottom two rows). In the top row of Figure  10 , we superimposed the 11 yr component from sunspots (pink) with that of aa. The two curves appear to be in quadrature: this is checked by offsetting the Schwabe cycle forward by exactly the 11/4 yr (second row). The explanation for this observation is the following: the torque exerted by Jupiter acts directly on sunspots, while the aa index is the difference between two antipodal observatories.
Data Availability Statement: The used data are freely available at the following address: IERS: https: //www. iers. org/IERS/EN/DataProducts/EarthOrientationData/eop. html (accessed on 12 March 2023); Permanent Service of Mean Sea Level: https://www. psmsl. org/data/obtaining/complete. php accessed on 12 March 2023; Geomagnetism Data Portal: https://wdc. bgs. ac. uk/dataportal/ accessed on 12 March 2023.
Quantifying the Effect of SUN3 and 4 OverExpression on ER Architecture SUN3 and 4 have been implicated in maintaining ER morphology  [18]. To further explore the potential function of SUN3 and 4 as ER morphogens, we compared ER morphology of control cells expressing GFP-HDEL alone (Figure  2A ) with N-and C-terminally tagged SUN3 and SUN4 (Figure  2B-E ) using the ER structure quantification software AnalyzER  [22]. An initial analysis by MANOVA revealed significant changes in ER structure on overexpression of both SUN proteins (Pillias trace, p-value = 9. 31 × 10 -12 and Roy's largest root, p-value = 1. 90 × 10 -22 ). Plotting the first two discriminate canonical variables (Figure  2F ) and a dendrogram plot of group means (Figure  2G ) indicated that SUN3 and SUN4 overexpression results in different modifications to ER structure.
According to the Orthologous Matrix (OMA) and EggNOG databases  [29, 30] , SUN3 and maMYB have a large number of orthologues across plant species, although the mid-SUN domain was already present in unicellular green algae while maMYB appears later in mosses (Supplementary Figure  S2 ). Interestingly, the transmembrane domains of maMYB and SUN3 as predicted by TMHMM-2. 0  [31]  are well conserved in all orthologues. Altogether, the localization and interaction between maMYB and SUN3 and the conservation of transmembrane domains during evolution suggest that the two proteins may have an important function in the ER and potentially the outer nuclear membrane.
Accessions Numbers, T-DNA Mutants and 35S::MaMYB-vYFP Line Accession numbers for this study include SUN1 (AT5G04990), SUN2 (AT3G10730), SUN3 (AT1G22882), SUN4 (AT1G71360), SUN5 (AT4G24950), and maMYB (AT5G45420). The sun3 insertion T-DNA line Flag_374A03 (sun3-1) from the Salk collection was obtained from the European Arabidopsis Stock Centre. As Flag_374A03 line was in the Ws (Wassilewskija) background  [13] , three successive generations of outcross with wild-type Col0 plants were used to generate a near isogenic Flag_374A03 Col0 line. The T-DNA insertion was genotyped by PCR. A line overexpressing maMYB fused upstream to venus YFP under the 35S cauliflower promoter was provided by Prof F Brandizzi (maMYB-vYFP)  [28].
benthamiana transiently expressing fluorescently tagged proteins of interest. Analysis of ER structure was performed using the AnalyzER software, as described in  [22]. In brief, confocal images were collected and imported into AnalyzER. Imaging conditions, including pixel size and channel order, were input into the software, together with estimated minimum and maximum tubule width. Images were upsampled to increase pixel density and to prevent pixelation error later in the analysis. A region of background outside the cell was measured, with the intensity of this region subtracted from the overall images. Image intensity was then enhanced by deploying a guided filter, to increase the intensity of the ER network. The area of analysis was masked out using thresholding, and within this region cisternae were identified using an opening function, followed by active contouring, to map the outline of cisternae onto the underlying image intensity.
Conservation of MaMYB and mid-SUN3 and Phylogenetic Reconstruction maMYB and SUN3 protein orthologues were selected in 16 species from green algae, basal land plants and angiosperms: Arabidopsis thaliana, Arabidopsis lyrata, Brassica oleracea, Populus trichocarpa, Theobroma cacao, Prunus persica, Glycine max, Oryza sativa sub sp indica, Musa acuminata, Zea mays, Amborella trichopoda, Picea glauca, Selaginella moelledorffii, Physcomitrella patens, Chlamydomonas reinhardtii, Ostreococcus lucimarinus. Orthologues were collected using OMA and EggNOG databases  [29, 30]. Graphic representation was performed using ITOL  [47]. Signal peptide were predicted by SignalP-5. 0 [48]  and Phobius [49], transmembrane domain by TMHMM-2. 0  [32]. The accession numbers of all orthologues are provided in Supplementary Table  S4.
Cultivation conditions of Phaeodactylum tricornutum, nitrate starvation and resupply and growth monitoring Triplicate non agitated cultures of P. tricornutum ecotypes Pt1 and Pt4 (respectively CCMP 2561 and UTEX 646) were conducted. Cells were grown in 500 mL Erlenmeyer flasks containing 200 mL of autoclaved f/2 medium  (Guillard and Ryther, 1962). Cultures were inoculated at a cellular density of approximately 10 5 cells/mL. Temperature was maintained at 19°C and light intensity was provided at 55 μmol/m²/s with daylight fluorescent tubes (LuxLine Plus T8 F58W/865 G13, Sylvania Lighting) following a light-dark cycle of 16h-8h. This growth intensity was chosen because it allowed to drive similar relative electron transport rates in the different strains studied here as determined by tracing Photosynthesis-Irradiance (PI) curves using a Dual-PAM 100 (Walz, Germany) operated with the default routine (Supplementary Data S1).
Confocal image analysis and 3D modelling of plastid and lipid droplets Raw images from confocal acquisitions were processed through deconvolution with the software Huygens professional 4. 5. 1 (Scientific Volume Imaging, Hilversum, Netherlands). Deconvoluted images were further processed using Fiji freeware (ImageJ) to adjust brightness and contrast of each channel and produce maximum intensity projections on the z axis. Deconvoluted images were also used as input for 3D modelling of LDs and of the area where chlorophyll fluorescence was detected (i. e. thylakoids) with IMARIS 9. 0 (Oxford Instruments, United Kingdom). Cells were individually selected when images contained multiple cells. Then, for each cell, 3D volumes were created for each LD and for thylakoids using values of the "smooth" and "background" parameters specific for thylakoids (respectively 0.
Bright field microscopy for the determination of cell volumes Images of P. tricornutum populations were acquired with a Zeiss Cell Discoverer 7 inverted microscope (Carl Zeiss France, Rueil-Malmaison, France). Cells from each sample were deposited in a well of a 24 well glass-bottom microplate and covered by a glass coverslip. Images were acquired with a ×50 water-immersion objective and a tube lens magnification of 0. 5. Two mosaics of 5×5 images were collected for each sample. Image analysis was automated with a homemade macro due to the large number of cells in the images. ImageJ plugin "Trainable Weka Segmentation"  (Arganda-Carreras et al. , 2017)  was used to separate cells from the background in each image by machine learning algorithms. For the training phase, an image was randomly selected from the dataset.
The R package "ggplot2" (https://ggplot2. tidyverse. org/) was used for tracing violin plots and density maps. For box and whiskers plots, the delimitations were those of "ggplot2": the line inside the box is the median, the ends of the box are the first and third quartile while the ends of the whiskers are the lowest (respectively highest) value between maximum (minimum) and Q3 + 1. 5 × [interquartile range] (Q1 -1. 5 × [interquartile range]).
Pt1 and Pt4 cells accumulate lipid droplets on distinct modes but degrade them similarly CLSM data also provided insights into how lipids are distributed between LDs inside each cell. Indeed, it was previously described that Pt4 cells accumulate their lipids in two large LDs while Pt1 cells would form a higher number of smaller LDs  (Leyland et al. , 2020b; Scarsini et al. , 2021). To assess these accumulation patterns, we defined the proportion of total LD volume comprised in the two largest LDs as an indicator for characterizing the inequality of lipid distribution between LDs in a cell. This indicator would tend to 100% when the majority of lipids is concentrated in only two LDs (very inequalitarian) while a lower value would indicate a repartition between a higher number of LDs (Figure  7 ).
Asterisks denote a difference between ecotypes (t-test: *0. 01< p-value< 0. 05; **0. 001< p-value< 0. 01; ***p-value< 0. 001), letters show differences between means at each time of sampling [ANOVA, p-value<0. 05, LSD post-hoc test a>b>c (Pt1) and a'>b'>c'>d' (Pt4)]. Box and whiskers plot: values plotted as given by R package ggplot2, the red diamond inside the plot is the mean. appeared, progressively in Pt1 and immediately in Pt4. Finally, after the N resupply, the second population of LDs in both ecotypes almost completely vanished at h24 and totally at h72, reaching a distribution similar to that of D0.
05, LSD post-hoc test a>b>c (Pt1) and a'>b'>c' (Pt4)]. Box and whiskers plot: values plotted as given by R package ggplot2, the red diamond inside the plot is the mean. morphotypes  (Ovide et al. , 2018; Song et al. , 2020; Galas et al. , 2021). In the culture conditions used in this study, the major morphotype observed for both Pt1 and Pt4 is the fusiform one. As already described for fusiform cells  (Flori et al. , 2017; Broddrick et al. , 2019) , they both contain a unique plastid located at the center of the cell and displaying a bilobed shape (Figures 2, 3 and S1). Both strains also share the same major biochemical compounds including chlorophyll a and c, fucoxanthin as a major carotenoid, the b1,3 glucan called chrysolaminarin as a storage carbohydrate and C16:0, C16:1 n-7 and C20:5 n-3 as major fatty acids in their total lipids fatty acid profiles  (Abida et al.
Acknowledgments The authors would like to thank  Carole Burel  from the  GlycoMEV UR4358 lab  and  Damien Schapman  from  HeRacLeS-PRIMACEN, University of Rouen Normandie  for technical help. The authors are also grateful to  Pr H. Hu  ( Key Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan China ) for kindly providing the Pt1_Wuhan strain.
| Multi-speciesdataset We used a data set of 20 reef fish species co-occurring in four populations of the WIO. Species were chosen to maximize the representation of the ecological traits spectrum, so that this multi-species framework is particularly suitable to compare SGDC patterns in species with different life history traits, and thus understand why SGDC studies come up with such different results  (Lamy et al. , 2017). We used double-digest restriction-site associated DNA sequencing (ddRADseq) data on 20 tropical reef fish species. To expand the data set of  Donati et al. (2021) , we added ddRADseq data for Parapercis hexophtalma and Naso brevirostris. We removed Zanclus cornutus from the analysis as there are no other species in its family (Zanclidae), which precludes the possibility to estimate within-family SD.