dataesr/scientific-paragraphs-categorization huggingface dataset:
Coordinated Acquisition of Massive Data Sets The increasing rate of acquisition of plankton images (Figure  3 ) has been fueled by the recognition that they can provide biological information at an unprecedented scale, for research and monitoring purposes, and by the wide distribution of commercial instruments  (Lombard et al. 2019 , Stemmann et al. 2012 ). This acquisition is quickly growing beyond the capacities of most plankton ecology laboratories, even for classic stand-alone studies: When each deployment yields millions of images to classify and terabytes of raw data to store, personal computers are not enough; data centers become necessary. Section 3 highlighted that few data sets cover long periods or large spatial scales. With the deployment of imaging sensors on automated moorings  (Orenstein et al.
2020b) , on autonomous instruments  (Ohman et al. 2019) , or as part of large-scale programs such as the Global Ocean Ship-Based Hydrographic Investigations Program (GO-SHIP)  (Lombard et al. 2019) , such data sets should become easier to acquire, and this would be a boon for understanding plankton dynamics at those scales. However, those data sets would be even larger than those of the typical ecological study and will require adequate infrastructure. Another way of accessing information over long/large scales, or to investigate local processes but at several times/places, is by aggregating individual data sets. For this to succeed, data sets need to be interoperable, consistent, and shared. For plankton and marine snow image data sets, interoperability probably starts with a common taxonomy.
2021). Beyond taxa names, other qualifiers are often added to identifications (such as life stage), and some are specific to in situ images (such as whether the organism is completee. g. , an appendicularian body, or just its mucus house, or both). The vocabularies of the British Oceanographic Data Centre (BODC 2021), used by the Ocean Biodiversity Information System, and of the Video Annotation and Reference System knowledge base (MBARI 2019), geared toward deep-sea images, provide standard terms for some but not all of these cases. Those vocabularies also cover the documentation of other essential metadata, including not only the longitude, latitude, and time but also the volume sampled, instrument name, pixel size of the image, and so on, which are essential to aggregate data sets in a meaningful way.
Customized Tools for Quantitative Imaging Most hurdles between us and a worldwide network of plankton image curation seem to be in process and mentalities, not in technology. Indeed, many instruments and machine learning techniques are mature and already in use. It is the connections between them, the overall data flow, and the final release of data that are often missing. Software tools that ease the storage, management, annotation, and sharing of images can help. Gomes-Pereira et al. (  2016 ) reviewed 23 such tools, most of which are geared toward benthic images, although SQUIDLE+ (https://squidle. org), for example, is also used for plankton. Tools dedicated to plankton images have also been developed by independent teams; for example, Plankton Identifier  (Gorsky et al. 2010)  and Zoo/PhytoImage  (Grosjean et al.
2018 ) can be used to build training sets and ease access to classic machine learning methods, and the IFCB dashboard (https://ifcb-data. whoi. edu) enables users to store and browse data sets from this instrument. Recently, the EcoTaxa web application  (Picheral et al. 2017 ) has gained traction and now hosts ∼150 million images, from ∼10 different instruments, generated by over 1,000 users from more than 350 organizations worldwide (Figure  3 ). It integrates image and metadata storage, data browsing and subsetting, classic and deep learning, and data export. Its main goal is to provide an interface that accelerates the work of human curators by using machine learning. Yet, in its current form, it will not scale to the billions of plankton images collected worldwide, it only conforms to a few metadata standards, it has no tools dedicated to quality control, and its machine learning back end could be improved.
ACKNOWLEDGMENTS The authors wish to thank their hiring institutions for financial support through their salaries. Their work and reflections on the issues discussed in this article were also fostered by the following projects: the  World Wide Web of Plankton Image Curation, Belmont Forum  ( J. -O. I. grant  ANR-18-BELM-003 , D. J. L. grant  18076935 ); the  Working Group on Machine Learning in Marine Sciences, International Council for the Exploration of the Sea  ( J. -O. I. );  Blue Cloud, European Union  ( J. -O. I. );  JERICO-S3 ,  European Union  ( J. -O. I. , L. S. ); FORMAL (From Observing to  Modeling Ocean Life ),  Institut des Sciences du Calcul   et des Données, Sorbonne Université  ( J. -O. I. , S. -D. A. , L. S. );  ARTIFACTZ ,  Université Côte d'Azur  (S. -D. A. , J. -O. I. );  KAKENHI ,  Japan Society for the Promotion of Science  (D.
The results of the application of the MCR-ALS method can be used for the discovery of the main driving factors (latent https://doi. org/10. 5194/egusphere-2022-117 Preprint. Discussion started: 20 April 2022 c Author(s) 2022. CC BY 4. 0 License. variables) responsible of the observed data variance, in this case, of the observed changes in the measured chemical pollutants. The present study is focused on promoting and extending the use of multivariate curve resolution alternating least squares method, including trilinear and quadrilinear constraints, for the investigation of NO2, O3 and PM10 air pollution. In addition, this study aims at providing different strategies to deal and estimate missing data also using the MCR-ALS methodology (Multivariate Curve Resolution of incomplete data multisets | Elsevier Enhanced Reader, 2022).
Air quality data The experimental data used in this work consisted on O3, NO2 and PM10 concentrations recorded from eight air quality monitoring stations operated by the Department of Environment of the Catalan Autonomous Government. The selected air quality monitoring stations consisted in three urban  (Gràcia, Vall d'Hebron and Granollers) , one semi-urban (Manlleu) and one control site (Observatori Fabra), all of them located in the province of Barcelona, and three rural: Juneda and Bellver de Cerdanya in the province of Lleida, and Begur (Costa Brava, NE Catalonia), in the province of Girona. More detailed information about the characteristics of the stations is provided in a previous air quality monitoring study by the authors  (Gorrochategui et al. , 2021). NO2 concentrations were measured by means of chemiluminescence according to the UNE method 77212:1993, using automatically operated MCV 30QL analyzers.
Experimental data multisets In this study, two experimental data multisets were analyzed (see Fig. 1 ). Both of them contained hourly concentrations of NO2, O3 and PM10 measured in the eight air quality monitoring stations but for distinct Decreto 463/2020de 14 de marzo, por el que se declara el estado de alarma para la gestión de la situación de crisis sanitaria ocasionada por el COVID-19. , 2020)) in the different stations. The second data set contained air quality data recorded in the same 8 stations but during a longer period of time: from January 1 st to December 31 st of 2018, 2019 and 2020. The latter multiset was built in order to evaluate annual trends of air pollution; especially interesting in 2020, an extraordinary year due to the coronavirus pandemic. As observed in Fig. 1 , both data sets contained some missing data blocks, which were not included in the MCR-ALS analyses of individual contaminants, a part from some spot values, which were further estimated to undergo chemometric analysis.
Data sets arrangement In this study, the two data multisets were separately arranged to further undergo MCR-ALS individual analyses of the complete experimental data sets (Fig. 1 ). To conduct the analysis of the month of April, data matrices for NO2, O3 and PM10 were separately arranged in a first step. For each contaminant, a total of 24 data matrices, one per year (three years) and per monitoring station (eight stations), of size 30 x 24 (month days' x hourly measurements), were obtained. As observed in Fig. 1a  these 24 data matrices were labeled as Dstation-year; with the name of the corresponding air quality station (V: Vall d'Hebron, Gn: Ganollers, M: Manlleu, J: Juneda, Bl: Bellver, Ga: Gràcia, Bg: Begur and O: Observatori Fabra) and the two last digits of the year  (2018, 2019 and 2020).
1a ). To conduct the analysis of the entire three years, data matrices for NO2, O3 and PM10 were also separately arranged in a second step. For each contaminant, a total of 24 data matrices, one per year and per monitoring station, of size 365 x 24 (year days x hourly measurements), were obtained. These 24 data matrices were then arranged using a column-wise arrangement, obtaining three augmented data matrices: Dcaug-allyear-NO2, Dcaug-allyear-O3 and Dcaug-allyear-PM10 (Fig. 1b ). In this case, for the three contaminants, some data were missing (white gaps in the figure). For NO2 and O3, some data from Observatori Fabra station was missing. Thus, the resulting augmented matrices, Dcaug-allyear-NO2, Dcaugallyear- O3, contained information of the whole year, seven stations and the three years (365 x 3 x 7), resulting in (7655 x 24) matrices, as shown in the figure.
MCR-ALS analysis of the complete experimental data sets using trilinear and quadrilinear constraints Solving Eq. (  3 ) using bilinear MCR-ALS does not take into account the temporal and spatial structure of the data in the vertical concatenated mode which includes the information of the day, year and station. This data structure can be considered in the trilinear and specially in the quadrilinear extensions of the bilinear models described in Eq. (1-3). The factor decomposition model given before can be extended to a three-way dataset, D, or to a four-way data set, D, expressed individually for every data value as given by Eq. (  4 ) and (  5 ). d i,j,kl = ∑ x i,n y j,n z kl,n N n=1 + e i,j,kl (trilinear model) (4) Therefore, the proposed trilinear and quadrilinear models take advantage of the natural structure of the analyzed data sets, especially in relation to their different temporal modes (i.
MCR-ALS simultaneous analysis of incomplete multiblock experimental data The simultaneous analysis of the NO2, O3 and PM10 experimental data can be done one step forward using a data fusion multiblock strategy. This would imply building a single MCR model for the whole multiset and Y T PM. In addition, if the trilinearity/quadrilinearity constraints are applied to the columns of the resolved factor matrix Xcaug as described above in Sect. 2. 6 using matrix decompositions of Eq. (  6 ) and (  7 ), the common day, year and station profiles will be separately recovered and analyzed. However as previously described, April and the whole year individual data sets were not obtained for all stations, years and pollutants, and therefore they together could not be fitted in a rectangular superaugmented data matrix containing all the data for all the years and stations as shown in Eq.
(  8 ) for Dcraug. Some of the individual data sets were missing (see Sect. 2. 3 and 2. 4). In particular, in the case of April, two different data blocks could be arranged. First, the NO2, O3 and PM10 concentrations data for 3 years and 6 stations were arranged in the complete row-and column-wise augmented April data block, DA1craug, with 540 rows (30 days x 3 years x 6 stations) and 72 columns (24 hours for NO2 + 24 hours for O3 + 24 hours for PM10). Secondly, the additional NO2 and O3 concentration data for 3 years and 2 stations were arranged This new incomplete data set DA12craug is built using the two data blocks previously defined, DA1craug and DA2craug, both concatenated vertically and filling the empty data block corresponding to the unknown concentrations of PM10 for two missing stations with the NaN notation.
The application of MCR-ALS to this incomplete dataset is decomposed using a bilinear model (see in Fig. 3 ), giving the two factor matrices XA12caug and YA12 T raug. XA12caug factor matrix has the column-wise augmented day x year x station profiles in its columns and YA12 T raug factor matrix has the row-wise augmented hour profiles for NO2 (YNO2), O3 (YO3) and PM10 (YPM10) in its rows. As previously, the trilinear/quadrilinear constraints can be applied during the ALS factor decomposition to the XA12caug factor matrix and allow the separate recovery of the day, year and station profiles, a part of the hour profiles for NO2, O3 and PM10 obtained in YA12 T raug. Analogous equations can be described for the NO2, O3 and PM10 experimental data measured not only in April but during the whole year.
Software MATLAB 9. 10. 0 R2021a (The MathWorks, Inc. , Natick, MA, USA) was used as the development platform for data analysis and visualization. The new graphical interface MCR-ALS GUI 2. 0  (Malik and Tauler, 2013) , freely available as a toolbox at the web address http://www. mcrals. info/. , was used for bilinear and trilinear data sets. Statistics Toolbox TM for MATLAB and PLS Toolbox 8. 9. 1 (Eigenvector Research Inc. , Wenatchee, WA, USA) were also used in this work. New specific MCR-ALS command line code for incomplete multiblock data sets is under final development and it can be requested to one of the authors (RT, email:roma. tauler@idaea. csic. es). variance overlap (also given in Table  1 ) in every case can be obtained subtracting the sum of the individual variances with the variance obtained with all the components simultaneously.
This difference is again larger in the case of NO2. In Table  1  also, the variances obtained when the trilinearity constraint was applied, instead of the quadrilinearity constrain, are also given, with similar results to those obtained by both multilinear models. In the case of MCR-ALS of all-year data of the Dcaug-allyear-NO2, Dcaug-allyear-O3 and Dcaugallyear-PM10 data matrices, rather similar results to those from April were obtained in terms of explained variances for all three type of models (see Table  1 ), reflecting again the higher complexity of the NO2 data over the years and stations compared to O3 data, and the intermediate behavior of PM10 data, although the later more similar to the NO2 data. Possible correlations between NO2, O3 and PM10 data sets during the month of April of 2018, 2019 and 2020 and in the eight stations were investigated using the incomplete data arrangement described in Sect.
2. 7 and Fig. S1. MCR-ALS analysis of DA12craug with five components and with only negativity constraints gave a total explained variance of 96. 2% (Table  1 ). When the quadrilinearity constraint was applied, the total explained variance decreased down to 90. 7% (91. 2% for the MCR-ALS trilinear). Such decrease between bilinear and quadrilinear MCR-ALS models of only 5 % indicated a good quadrilinear behavior of the whole system in April. The explained variances of each component individually are given below with the corresponding Figures of the resolved profiles. In the case of the simultaneous study of NO2, O3 and PM10 profiles along all the three years (i. e. , 2018, 2019 and 2020) in the seven stations using the incomplete data arrangement described in Sect. 2. 7 and Fig. S3. Results using five components indicated also a rather good quadrilinear behavior of the system.
Ozone concentrations were measured by means of UV photometry according to ISO FDIS 139464:1998, automatically operated with MCV 48 AUV analyzers. PM10 concentrations were measured by means of gravimetric determination, using manually operated high volume samplers MCV CAV-A/MS. The generated databases with all the concentrations measured were compiled by the Department of Air Monitoring and Control Service of the Generalitat de Catalunya (Xarxa de Vigilància i Previsió de la Contaminació Atmosfèrica (XVPCA).
These values indicate the higher complexity of the NO2 data compared to O3 data as will be shown also below. When the quadrilinear constraint was applied these values decreased to 78. 4%, 92. 9% and 78. 4% respectively, confirming again the less complex and more regular changes of ozone concentrations in the three years at the different monitoring stations. The simultaneous analysis of the incomplete multiblock data sets allowed the exploration of the potential correlations among the three contaminants, which was easily interpretable with the representation of overlapped NO2, O3 and PM10 hourly profiles. Interestingly, both in the study of the month of April and the study of the entire years, the simultaneous analysis of the three contaminants evidenced a correlation between NO2 and PM10, due to their common pollution sources (i.
Such effect was more evident in inner rural areas and in the control site (i. e. , Begur and Observatori Fabra), where the amount of NOx necessary to react with ozone and to produce its suppression is lower compared to urban areas due to the smaller traffic density and industrial activity. Overall, this work contributes to the better knowledge of the evolution of NO2, O3 and PM10 contamination in eight rural and urban areas of Catalonia during the two years before the  COVID-19 (i. e. , 2018 and 2019)  and the year itself of the pandemic (i. e. , 2020). The work also highlights: (a) the capacity of MCR-ALS with quadrilinearity constraints to perform simultaneous analysis of different contamination sources to study potential correlations among them and (b) the good performance of this approach in the analysis of complex four-way environmental data sets containing missing data blocks, providing concise and easy interpretable results.
, 2016). Data on individual sexual swelling cycle were collected by zookeepers on a specific data sheet, where they indicated whether a female had the swelling cycle (from minimum to maximum size turgidity) or not (menopause: one female; lactation: one female; contraceptive: one female; juveniles females: two). Yawning bouts were collected via the all occurrences sampling method (Altman, 1974) in absence of external perturbing events (e. g. , aggression, food distribution; 595 yawns collected in total). The yawning pattern involved mouth opening, with inhalation and a more rapid closing and exhalation  (Baenninger, 1997). No yawn was vocalized (via the use of vocal folds). For each yawn (triggering yawn) emitted by a subject, we recorded: (i) identity, sex and age of the yawner (trigger); (ii) identity, sex and age of all the subjects that were visible to the human observer and that could see the triggering yawn (potential responders); (iii) time of the triggering yawn (time of last consecutive yawn if more yawns were emitted in a row); (iv) distance between each potential responder and the last trigger (≤1 m, 1 < distance ≤ 10 m, > 10 m).
, 2021). When more than one yawning response occurred from different subjects, the first responder would become a trigger and we noted whether-for each responder-the triggering stimulus came from one or multiple triggers within the fixed time window. For each minute within the 3-min time window, we recorded the time of each yawning response (if any), so as to determine-a posteriori -the minute in which such response occurred (first/second/third) and the identity of the responder. We did not include in the yawning response dataset the subjects (i. e. , the potential responders) that had their head rotated by 180°with respect to the trigger or when a physical, sight-blocking obstacle prevented them from seeing the trigger.
Based on the method of calculation of the Corrected Conciliatory Tendency  (de Waal & Yoshihara, 1983; Veenema et al. , 1994)  for post-conflict management, we calculated the Individual Contagion Tendencies (ICTs) as follows: (APs -DPs)/(APs + DPs + NPs). The interobserver reliability between the two data collectors (M. C. and G. D. M. ) was calculated via Cohen's k on 10% of the yawning events which they recorded concurrently and independently. Cohen's k was calculated for all the variables considered (yawner identity, possible responder identity, detection condition of the possible responder, F I G U R E 1 Yawn contagion sequence. Yuli (7yo female) emits a yawn and Lokoro (6yo male) responds after 18 s. Lokoro is sitting within 1 m from Yuli and can see the triggering yawn distance, yawning response and minute) and was always higher than 0.
In GLMM 1 (N cases = 344), the following fixed factors were included: (i) triggering yawn number (factor; 1 = one yawn; 2 = two yawns; 3 = more than three yawns); (ii) distance (factor: 1 = individuals within 1 m; 2 = from 1 to 10 m; 3 = more than 10 m); (iii) sex of the trigger and potential responder (factor; M = male; F = female); (iv) age of the trigger and potential responder (numeric; years); (v) trigger and responder rank (numeric; NDS); (vi) affiliation levels (numeric; hourly frequencies with data normalized over the observation time). In GLMM 2 (N cases = 133), run on female dyads only-to check whether the swelling status of trigger and/or responder would affect yawn contagion-the following fixed factors were included: trigger reproductive state and responder swelling status (factor; 0 = without sexual swelling; 1 = with sexual swelling).
In both GLMM 1 and GLMM 2 the combination between trigger and potential responder's identity (dyad) was included as a random factor. The GLMMs were fitted in R (R Core Team, 2018; version 3. 5. 3) by using the function "glmer" of the R-package "lme4"  (Bates et al. , 2015). As a first step we verified if the full model significantly differed from the null model that included the random factors only  (Forstmeier & Schielzeth, 2011). The likelihood ratio test  (Dobson & Barnett, 2018)  was used to test this significance (analysis of variance with argument "Chisq"). Subsequently, by using the R-function "drop1," the p values for the individual predictors based on likelihood ratio tests between the full and the null model were calculated  (Barr et al. , 2013). As the target variables were binomial, a binomial error distribution was used.
For significant multinomial predictors, we performed all pairwise comparisons with the Tukey test  (Bretz et al. , 2010 ) using a multiple contrast package (multcomp). We reported the Bonferroni-adjusted p values, estimate (Est), standard error (SE), and Z values. We obtained the variance inflation factor (VIF) for the numeric variables of GLMM 1 via the "vif" function in R. All VIF values were between 1 and 2 (min-max range: 1. 29-1. 48), thus indicating no collinearity. We calculated the effect size via the package "effectsize," function effectsize which returns the best effect-size measure for the provided input GLMM.
The opposite cases of bonobos  (Demuru & Palagi, 2012; Tan et al. , 2017; present study) and orangutans (van Berlo et al. , 2020)  converge in indicating that the familiarity bias may be related to interindividual cohesion (proximate level) and type of sociality (ultimate level). In contrast with previous reports  (Joly-Mascheroni et al. , 2008; Romero et al. , 2013; Silva et al. , 2012) , a meta-analysis showed that familiarity seems not to affect interspecific yawn contagion between dogs and humans  (Neilands et al. , 2020). A similar approach could help disentangle the familiarity issue in bonobos, especially if by including data collected with the same methodologies on different colonies. Once again-owing to the differences observed across study groups and sites-we stress the importance of expanding the dataset on yawn contagion to account for intergroup differences and clarify what factors can modulate the phenomenon at the population level.
ACKNOWLEDGEMENTS We thank the staff of  La Vallée des Singes (Romagne, France) , and particularly the bonobo keepers  Carole Michelet ,  Franck Alexieff ,  Marielle Brun  and the veterinarian  Bertille Markuet  for support. Funding (University of Torino, DBIOS): research funds to IN;  NOR-I_RILO_19_01 ; PhD grant to MC ( 879705 ), and Erasmus traineeship mobility grant to GDM ( 886095 ).