dataesr/scientific-paragraphs-categorization huggingface dataset:
Sound analysis. Sound features of antennal rasps. Synchronized recordings of sounds (in. wav format) and videos were first analyzed to confirm sound production by each tagged spiny lobster. Then, each antennal rasp was extracted manually using the Audacity software (version 2. 1. 1  46  ). Antennal rasps were defined as pulse trains composed of at least several pulses separated by less than 20 ms from each other. Hence, any isolated pulses present in the recordings were not analyzed here. We performed sound analysis on a total of 10 antennal rasps per spiny lobster and per distance (80 sounds analyzed per individual in total). The same sounds were analyzed at the 8 different distances for each spiny lobster. All sequences were then processed using custom MATLAB scripts (version 9. 1; The MathWorks).
Ambient noise characterization. Recordings of ambient noise (10 min each) from the 8 hydrophones were first visualized to ensure the absence of antennal rasps. Because anthropogenic noise affected the ambient noise recordings during the experiment, sound sequences were both cut into 20 sequences of 30 seconds each, and we randomly selected 3 of 20 sequences from each recording. The sequences where anthropogenic noise (mainly shipping noise) was dominant were not taken into account in the analysis. We calculated the SPL rms of all selected 30 s long sequences. This SPL rms was calculated over the same frequency band as the antennal rasp SPL rms and SL rms (10-78 kHz), except for data from the Wildlife recorder, for which SPL rms was calculated over the 10-48 kHz frequency band. This provided a mean value for the ambient noise at each hydrophone placement, and was referred to the ambient noise level (ANL).
evaluation of transmission losses. The datasets of the averaged SPL rms for each individual and for each distance were fitted with nonlinear least-squares regressions using custom-made scripts in MATLAB. We used the following equation 42 : = - SPL SL TL (1) rms r ms where TL is the transmission loss (in dB). TL represents the loss of intensity due to the geometrical spreading of sounds in a physical medium  42  , and was calculated as the slope of the logarithmic regression between SPL rms and the distance from the spiny lobsters, which was expressed as: = × a TL log (r) (2) 10 where r is the distance between the spiny lobsters and the hydrophones (in meters), and a is the geometrical TL term. We obtained 19 different TL models using this method on the dataset generated by each animal (i. e.
known SPL rms , SL rms and r). The measurements from the 5 very small individuals were not included in this analysis because we did not have enough measurement points as they were only detectable between up to 5 and 10 m (see Results). Moreover, a global TL coefficient was also estimated using a global dataset obtained by merging the sounds from the 19 spiny lobsters. We compared this global TL with other theoretical models of TL commonly used in the bioacoustic literature  19. In theory, the spherical spreading loss ( = × TL 20 log (r) 10 ) prevails near the source where sound propagates uniformly in all directions. The cylindrical spreading loss ( = × TL 10 log (r) 10 ), prevails in shallow waters where sound cannot propagate as a spherical wave in all directions but only as a cylindrical wave bounded by the sea floor and the sea surface.
Acknowledgements Spiny lobsters were collected in this study for in situ experiments under the conditions of Permit no. 824/2019  issued by the  'Direction Interregionale de la Mer Nord Atlantique -Manche Ouest'. We thank the IUEM diving team for help collecting spiny lobsters. We are particularly grateful to  Erwan Amice ,  Thierry Le Bec  and  Pierre Poitevin  for their valuable help during the sound recording experiment. We also thank  Eric Dabas  for his technical support at the IUEM facilities. We deeply thank  Flore Samaran  for lending recording devices to build the linear array of hydrophones. We thank  Michel Legris  for discussions about sonar equation. We thank  Ian Thomas Jones  for proofreading our manuscript. This research was carried out as part of the PhD research project of  Youenn Jézéquel  for the  Université de Bretagne Occidentale (Brest)  with a grant from the  French Ministry of Higher Education and Research.
Assessing the Potential Impacts of NABS on Biodiversity and Ecosystem Services The exposure to novel ocean conditions in NABS might seriously challenge the viability of marine species currently inhabiting those areas, thus leading to substantial species turnover, changes in catch composition, and declines in potential fisheries production  (Cheung et al. , 2016b). To evaluate the risk that the emergence of NABS may pose to marine biodiversity and ecosystems services, we calculated the number of marine species and the annual volume of fisheries catches that will be exposed to NABS conditions. For marine exploited biodiversity, we collated the global gridded marine species richness dataset  (Gagné et al. , 2020)  and added several other exploited species distributions from the Sea Around Us 1 and species used in  Asch et al.
(2017). 1 www. seaaroundus. org This dataset included 1,105 species ranging from invertebrate to top predator. We extracted average annual total fisheries catch (average between 2001 and 2015) from the Sea Around Us database. Both datasets were in the 0. 5 • latitude × 0. 5 • longitude grid consistent with the ocean conditions and BGCPs data. We then computed the number of species and total annual catch in spatial cells characterized as NABS for each year between 2000 and 2100 across ESMs and RCPs.
FUNDING WC, GR, CW, and VL acknowledge funding support from the Nippon Foundation-the University of British Columbia Nereus Program. WC acknowledges support from the Natural Sciences and Engineering Research Council of Canada. OM acknowledges the support of the French ANR project CIGOEF (grant ANR-17-CE32-0008-01). TF thanks the Swiss National Science Foundation (PP00P2_170687) and the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 820989 (project COMFORT, Our common future ocean in the Earth system -quantifying coupled cycles of carbon, oxygen, and nutrients for determining and achieving safe operating spaces with respect to tipping points) for financial support.
Next, the divider was removed again, and the pikeperch larva was allowed 20 min to forage on this live prey. All behaviours were video recorded with a digital camera (Sony Handycam, DCR-SR72) positioned 80 cm above the device. The water in the device was the same as that in the aquarium and renewed between each test. Videos were analysed with The Observer XT10® software (Noldus, Netherlands). The analysis focused on the following variables: (i) attack, which was characterized by rapid movement of the pikeperch larva towards the zebrafish larvae, with its mouth open. This behaviour could easily be identified: just before the attack, the larva either stopped and took on an 'S' shape  (Houde, 2001; Turesson et al. , 2002)  or just changed the orientation of its caudal fin without stopping; (ii) capture, which corresponded to the biting of prey by pikeperch;  (iii)  the distance between the prey and the pikeperch larva before the attack (when the 'S' shape was clearly observed), measured from the mouth of the predator to the middle of the body of the prey on video screenshot with ImageJ® after the calibration of scale; (iv) the swimming activity of pikeperch larvae in the presence of zebrafish larvae, defined as the displacement during 3 min of the larva of more than its body length in less than 1 second; (v) the effectiveness of pikeperch larvae in attacking zebrafish larvae calculated by the number of captures relative to the total number of attacks directed by pikeperch larvae that attacked at least once.
Morphological traits and growth measurements In order to correlate the onset of piscivory with morphometric larval changes, pikeperch larvae were euthanized with an overdose (240 mg. L -1 ) of tricaine methanesulfonate (MS-222) anaesthetic after completion of the behavioural tests, and measured using a binocular microscope (Optika equipped with a Sony camera, Microvision, Lw1235C-GTI, Japan). Body morphometrics were taken from digital images using the image analysis software Archimed® (Microvision Instrument, France) and ImageJ®. Nine different morphometric characters associated with locomotion, vision and feeding were measured (Fig. 1 ): Total body Length  (TL)  is the distance between the snout to the tip of the tail; Eye Diameter (ED) is the average of the maximum and minimum diameters of the eye orbit; Head Length (HL) is the distance between the tip of the snout and the pre-opercula edge; Head Height (HH) is the greatest height of the head measured perpendicularly to the mid-section of the eye; Tail Length (TaL) is the distance between the anus and the base of the caudal fin; Tail Height (TH) is the distance perpendicular to the axis of the tail between the dorsal and caudal fins; Head Width (HW) is the greatest width of the head behind the eyes; Mouth Perimeter (MP) is the distance between the eyes following the superior jaw; and Mouth Width (MW) is the distance between the eyes and parallel to HW.
Statistical analyses Firstly, we tested whether the time of day when tests were conducted could influence larval behaviours (attack and capture) by means of a Generalized Linear Model [glm, package 'lme4'  (Bates et al. , 2014) ]. According to the peculiarity of the studied variables, that were the numbers of attacks and captures, i. e. counts, the distribution used in GLM was Poisson (corresponding natural link function: log). The time of day was separated into four time periods (period 1: from 8:30 to 11:30, period 2: from 11:30 to 14:30, period 3: from 14:30 to 17:30, and period 4: from 17:30 to 20:30). The time of day did not affect the number of captures (χ 2 = 3. 4, d. f. = 3, P = 0. 34) and affect the number of attacks (χ 2 = 32. 5, d. f. = 3, P < 0. 0001) with period 1 different from period 4 (z = 4.
test (R Core Team, 2017)) and the homogeneity of variance was tested with Levene test (leveneTest package 'car'  (Fox and Weisberg, 2016) ). When data did not fit with normality or homogeneity, we used a nonparametric test. For comparison between age groups, we used a Kruskall-Wallis test due to the non-homogeneity of data. Secondly, we compared the distances of attack and the swimming activity levels between four age groups (the 23 dph-group was also excluded) to evaluate pikeperch larval predatory abilities. For attack distances, pikeperch larvae were divided into three groups for each studied age group: (i) a larva could make a successful attack with prey capture (AC), (ii) unsuccessful attacks, but larva had already captured zebrafish larvae previously (AnC1) or (iii) no successful attacks throughout the whole duration of the test (AnC2).
We tested the effect of the age and of a previous success (AC vs. AnC1) of capture on the distance of attack, by using a Wald χ 2 test applied on a Linear Mixed Model [lmer, package 'lme4'  (Bates et al. , 2014)]  including the age as covariate, the previous success as fixed factor, and the interaction between age and experience, and individual as random factor. To compare AnC1 and AnC2, the effect of the age and the previous success of capture on the distance was tested using an ANCOVA [Anova, 'package 'car'  (Fox and Weisberg, 2016) ] including the age as covariate, the previous success as fixed factor and the interaction between age and experience. Swimming activity was tested using F-tests (Anova, package 'car'  (Fox and Weisberg, 2016 )) applied on a Linear Model [lm, package 'lme4'  (Bates et al.
, 2014) ] with age as a fixed factor and number of attacks (transformation square root) as a covariate. Then, a Tukey method was used as post-hoc test with P-value adjustment (fdr) considering the mean of attacks [contrast, package 'emmeans'  (Lenth et al. , 2019) ]. Thirdly, growth during development was described with regressions estimated from each morphometric parameter divided by the total length (TL) according to the allometric growth model described by Fuiman  (Fuiman, 1983). In this allometric model, the inflexion points designated the value of the body characters where the regression slopes (allometric growth coefficient) changed. To initiate the model, a Principal Component Analysis (PCA) (vegan 2. 0-9 packages, R software version 3. 2. 4) was carried out using the covariance matrix of the measured characters divided by TL.
Finally, to analyse enzyme activities, interaction between status (piscivore or nonpiscivore) and age (30 or 52 dph) was tested by an analysis of variance (ANOVA) using the lm function (R Core Team, 2017) with status and age as fixed effects [model = lm (enzyme~status*age)] and enzyme activity as a dependent variable (trypsin, α-amylase or pepsin). For ANOVA validation, residuals were tested for homogeneity and normality using residual vs. fitted value and sample vs. theoretical quantile (Q-Q) plots, respectively [plot (R Core Team, 2017)] followed by Shapiro-Wilk test for normality and Levene test for homogeneity of variance. As the data met the ANOVA assumptions, an ANOVA Type I was performed to calculate F-tests [ANOVA (  R Core Team, 2017) ]. When interactions between status and age were not significant, enzyme quantities between piscivorous and non-piscivorous larvae were compared with Student t-test for each age group tested (30 and 52 dph).
Furthermore, a correlation between the number of attacks and enzyme quantities was tested at 30 and 52 dph with Pearson correlation test. All statistical analyses were performed using the free software R version 3. 6. 2 (R Core Team, 2017) except for χ 2 tests, which were performed with StatView software (version 5. 0). For model validations, residuals were tested for homogeneity and normality using residual vs. fitted value and sample vs. theoretical quantile (Q-Q) plots, respectively [plotresid, package 'RVAideMemoire'  (Hervé, 2017) ]. The level of significance used in all tests was P < 0. 05.
Acknowledgements We would like to thank  D. Żarski , and  J. Roche  for supplying pikeperch larvae,  Y. Ledoré  for taking good care of them,  M. Hervé  for his statistical help, and finally  C. Merlin  ( ABC Translation, Ferrières, France ) for her help to improve the English of this paper. This work received funding from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration (KBBE-2013-07 single stage, GA 603121, Diversify). It was also supported by the  University of Lorraine  through a grant to AD.
05 m higher than the sample of measured available cavities The IButton data were extracted with OneWireViewer software (1-Wire drivers X86, version 4. 03). The humidity data were corrected following the sensor manufacturer's recommendations  (Maxim Integrated Products Inc, 2011: p. 53 ). For each nest and each day, we calculated: (a) the maximum, minimum and mean interior temperature and humidity rate, (b) the daily amplitude of the interior temperature and humidity (i. e. , the difference between the minimum and maximum daily temperature/humidity rate inside the nest), (c) the daily mean and maximum absolute delta between the interior and exterior temperature (i. e. , the mean and the maximum over 24h of the absolute values of hourly differences between exterior ambient temperature and interior nest temperature).
Data availability. The satellite products used in this study are the CALIPSO 5 km aerosol profile product  (Vaughan et al. , 2019)  publicly available at the AERIS/ICARE database (ICARE data and services center, 2019); the MODIS Corrected Reflectance (True Color) images  (Gumley et al. , 2010)  publicly available from NASA Worldview (NASA Worldview snapshots application center, 2019). The HALO-DLR aircraft lidar observations (level-2 data of depolarization and water vapour mixing ratio profiles) used in this study are available via the HALO database (https://halo-db. pa. op. dlr. de/, last access: 15 July 2020) (DLR, 2020). The AERONET version 3 data are available at https://aeronet. gsfc. nasa. gov/ (last access: 15 July 2020) (NASA, 2020). The pre-calculated AERONET kernels used in this work are publicly available at https://code.
grasp-open. com/open/spheroid-package (last access: 15 July 2020)  (GRASP OPEN, 2020). All datasets created during the calculation of the scattering properties of near-spherical and Chebyshev particles can be accessed through the ReACT-NOA database upon request to the corresponding author. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-20-14005-2020-supplement. Author contributions. VA, AT and AG conceived the presented idea. VA and AT supported AG on the analysis, manuscript preparation and figure design. AT guided and supervised AG on the scattering model calculations and results interpretation. RC and LP performed the analysis on fractal aggregates scattering properties and provided AG with the results (not shown in the final paper).
AG, AK and SS analysed the Microwave Limb Sounder (MLS) water vapour data and performed FLEXible PARTicle dispersion model (FLEXPART) runs to support the dispersion of the smoke and volcanic plumes (not shown in the paper). EM, MT and IT prepared the CALIPSO data and figures. MH, HB and AA collected and analysed Leipzig lidar measurements. TL, AL and OD supported AG to confirm T-matrix results for near-spherical particles. SG and MW performed the DLR HALO airborne lidar measurements and the corresponding analysis. DB advised AG on the interpretation of the results of this study. All authors provided critical feedback and helped shape the research, analysis and manuscript. Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "EARLINET aerosol profiling: contributions to atmospheric and climate research".
It does not belong to a conference. Acknowledgements. The authors would like to thank Michael Mishchenko for making the T-matrix codes available (https://www. giss. nasa. gov/staff/mmishchenko/t_matrix. html, last access: 15 July 2020). We are grateful to EARLINET (https://www. earlinet. org/, last access: 15 July 2020) and ACTRIS (https://www. actris. eu, last access: 15 July 2020) for the data collection, calibration, processing and dissemination. We are grateful to the AERIS/ICARE Data and Services Center for providing access to the CALIPSO data used and their computational centre (http://www. icare. univ-lille1. fr/, last access: 15 July 2020). We thank the NASA/LaRC/ASDC for making available the CALIPSO products. The authors are grateful to the NASA EOS Aura MLS team for providing free access to the MLS water vapour data (https://mls.
jpl. nasa. gov/, last access: 15 July 2020). We acknowledge the use of imagery from the Worldview Snapshots application (https://wvs. earthdata. nasa. gov/, last access: 15 July 2020), part of the Earth Observing System Data and Information System (EOS-DIS). We acknowledge the PANhellenic GEophysical observatory of Antikythera (PANGEA) data centre for supporting all computations for the development of datasets for the calculation of the scattering properties of near-spherical and Chebyshev particles. Financial support. The research leading to these results was supported through the European Research Council (ERC) under the European Community's Horizon 2020 research and innovation framework programme -ERC grant agreement no. 725698 (D-TECT). Anna Gialitaki acknowledges support of this work by the project "PANhellenic infrastructure for Atmospheric Composition and climatE chAnge" (MIS 5021516), which is implemented under the action "Reinforcement of the Research and Innovation Infrastructure", funded by the operational programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and cofinanced by Greece and the European Union (European Regional Development Fund).
Enabling data reuse and knowledge discovery is increasingly critical in modern science, and requires an effort towards standardising data publication practices. This is particularly challenging in the plant phenotyping domain, due to its complexity and heterogeneity. We have produced the MIAPPE 1. 1 release, which enhances the existing MIAPPE standard in coverage, to support perennial plants, in structure, through an explicit data model, and in clarity, through definitions and examples. We evaluated MIAPPE 1. 1 by using it to express several heterogeneous phenotyping experiments in a range of different formats, to demonstrate its applicability and the interoperability between the various implementations. Furthermore, the extended coverage is demonstrated by the fact that one of the datasets could not have been described under MIAPPE 1.
, 2017;  Cwiek-Kupczy nska, 2018). On the one hand, the development of automated high-throughput and high-resolution technologies has contributed to a scale-up in the number, complexity and size of plant phenotyping datasets. This has been amplified by the increasing number of long-term, highly multilocal phenotyping networks aiming to decipher the interaction between genotype and environment  (Millet et al. , 2019). Conversely, the reuse and meta-analyses of phenotyping data are particularly challenging due to the heterogeneity of this domain that encompasses many types of experimental sites (field, glasshouse, controlled environment), plants (crops, forest trees), collected data (images, physical measurements, chemical assays, molecular biology assays), and experimental designs (factors being tested, timing, field layouts, etc.
). Furthermore, plant phenotype hinges not only on the interaction between genotype and environment, but also developmental stage and epigenome status  (King et al. , 2010) , which raises the challenges of integrating genotypic and phenotypic data  (Pommier et al. , 2019b). A successful example of data reuse in this domain is the study by  Hurtado-Lopez et al. (2015) , who reused field trial datasets and integrated them with quantitative trait locus (QTL) data to yield novel insights into genotype by environment (GxE) interactions in potato. Because the original experimental data followed no standardisation guidelines, the authors had to manually assemble detailed metadata during the preprocessing of the data from descriptions in unstructured text. To facilitate such studies, so that they may become the norm rather than the exception, it is essential that the scientific community adopt good data management and publication practices  (Zamir, 2013).
The requirements for data reuse in science have been formalised in the FAIR data principles  (Wilkinson et al. , 2016). They state the criteria that scientific data must fulfil to be findable, accessible, interoperable and reusable by both humans and machines, which hinge on having rich, harmonised, machinereadable, high-quality metadata describing the data as explicitly and objectively as possible. Four key components are needed from research communities to meet these requirements: metadata standards which list the fields required for interpreting the data from a given experimental domain; machine-readable (meta)data exchange formats in which to express and share the (meta)data; ontologies or controlled vocabularies to describe (meta)data values and ensure that they are objective, consistent and unambiguous across datasets; and searchable data repositories with a well established protocol for machine access.