dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018). Egg retention was randomly inspected daily on one unit of spawning nests along the five lines, hence there were five replicates for each type of artificial spawning ground. And the exact same relative position was sampled in 1 day in the spawning grounds composed of three materials, so as to ensure the comparability of the data of artificial spawning substrates made of different materials. Three layers of circular spawning nests from top to bottom were counted, respectively. The specific method of egg counting is to take a fixed number of pieces or filaments spawning substrates out of the water surface and count the attachment of eggs on the spot. To verify field data and reduce counting errors, digital images of sampled substrates on every surface were taken, and the viable and dead eggs were counted with an imaging software Image J (version 1.
Water temperature monitoring To study the relationship between fish spawning behavior and water temperature, water temperature of artificial spawning A B FIGURE 2 The structure of artificial spawning substrates (A) and arrangement of artificial spawning grounds (B). PAS, palm sheet; PEF, polyethylene filament; PEN, polyethylene net. 2. 6. Data analyses 2. 6. 1. Egg deposition and mortality Egg deposition was determined by counting the eggs attached to the artificial substrate and measuring the surface are of artificial substrate. Each artificial spawning substrate had five replicates in each water layer (upper, middle, and lower). Egg density on artificial spawning substrate was calculated by the following equation: Egg density attached eggs surface area of substrat ind m. - ( ) = 2 n e e.
The hatching rate (the probability of eggs hatching successfully), 7-day survival rate (the probability of hatched fry surviving for 7 days) and the total survival rate (the probability of eggs hatching into fry and surviving for 7 days) could be calculated as follow: Spawning fish were enumerated and measured from DIDSON data analyzed using ARISFish (version 2. 6. 2, Sound Metrics, 2019) software. Fish observed by the DIDSON were measured and counted from 6:30 AM on April 9 to 6:30 AM on April 10 when the spawning peaks occurred. Hatching rate % [ ]= ×.
Statistics Statistical analyses were performed using SPSS (version 19. 0, Statistical Package for Social Sciences (SPSS), 2010), R (version 3. 5. 2,  R Core Team, 2018 ), and Origin Pro (version 2017C, Origin Lab Corporation, 2017). For egg density and egg mortality, pairwise comparisons between values of all substrate materials (PAS, PEF, PEN) of same water layers were conducted with the pairwise Mann-Whitney U-test (nonparametric method chosen due to nonnormal distribution of data). The same pairwise comparisons were conducted between values of all water layers (Lower, Middle, Upper) of same substrate materials. For hatching rate and survival rate of eggs, pairwise Mann-Whitney U-test were conducted to compare the difference of substrate materials. Paired test was applied to eliminate the effect of different sampling batches (different sampling data and locations in spawning ground).
Funding: Portuguese science funding agency, Fundação para a Ciência e Tecnologia, FCT: PhD fellowship to EL (SFRH/BD/52171/2013), and research grants to PB (PTDC/BIA-EVF/0017/2014, PTDC/BIA-EVL/0321/2021). French research funding agency, Agence Nationale de la Recherche, ANR: Laboratory of Excellence TULIP, ANR-10-LABX-41 and ANR-11-IDEX-0002-02 (support for DD) and French research centre, Centre National de la Recherche Scientifique, CNRS: International Associated Laboratory, LIA BEEG-B (support for DD), and the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme  (FP7/2007 (FP7/ -2013)) , through the PRESTIGE programme coordinated by Campus France (support for DD).
Fly stocks and rearing conditions We analyzed adult female flies from different genotypes for the project's different tasks. For the characterization of pigmentation variation, we used genotypes from the Drosophila Genetic Reference Panel (DGRP)  (Mackay et al. 2012; Huang et al. 2014 ), which we obtained from the Bloomington Stock Center. We obtained data on flies reared at 17°C and 28°C from 191 DGRP genotypes, and data for only 17°C for three additional DGRP genotypes (for which survival was low at 28°C). For a subset of 75 DGRP lines at 17°C and 85 DGRP lines at 28°C, we generated two or three replicate sets. For the functional validations, we used: i) mixtures of selected DGRP genotypes, which we derived as described below (section "Functional analysis of candidate SNPs by Mendelian Randomization"), ii) loss-of-function mutants of selected pigmentation genes (ebony, yellow, and bric-a-brac), which we obtained from the Bloomington Stock Center (reference codes 1658, 169, and 37298, respectively), and iii) a standard control "wildtype" laboratory strain, Canton-S, which we obtained from CK Mirth lab.
Statistical analyses of G and T effects on pigmentation All statistical analyses were conducted in R v 3. 6. 2 (Team 2019) (R Development Core Team 2017) and Rstudio (RCore Team 2018; RStudio Team 2016). The tidyverse R package suite  (Wickham et al. 2019 ) was used for preparing all analyses and plots. The packages lme4  (Bates et al. 2014)  and lmerTest  (Kuznetsova et al. 2017)   We checked assumptions of parametric tests by visualizing QQplots and residuals' plots and by using Shapiro test (for normality) and Bartlett test (for homoscedasticity). For each body part and pigmentation trait, we used linear models to test for differences across DGRP genotypes (in R syntax: lm(Trait~Genotype)) or for the interaction between genotype and temperature (lm(Trait~Genotype + Temperature + Genotype:Temperature)).
The Orion Bar The Orion Bar, a strongly UV-irradiated PDR, is an escarpment of the Orion molecular cloud (OMC), the closest site of ongoing massive star formation. The bar is illuminated by the O7-type star θ 1  Ori C, the most massive and luminous member of the Trapezium cluster at the heart of the Orion Nebula (e. g. , O'Dell 2001). The Trapezium cluster creates a blister H II region that is eating its way into the parental cloud. A large cavity has been carved out of the molecular gas and the inner concave structure tilts to form the Orion Bar  (Wen & O'dell 1995; O'Dell 2001). Weilbacher et al. (2015)  presented Multi Unit Spectroscopic Explorer (MUSE)/Very Large Telescope (VLT) integral spectroscopic data of the central part of the Orion Nebula with an angular resolution of ∼0. 7-1. 2 ′′.
Observations and data reduction We integrated 750 s on-source for each filter. The observing strategy was successful although it was slightly challenging because of the variable nebular background on the tip-tilt star. Indeed, the nebular background can significantly increase the effective magnitude of the tip-tilt star, which in turn can degrade the AO correction. In an environment such as the Orion Bar, with its complex background, the automatic AO acquisition sequence may need to be modified to manually take a background for the tip-tilt sensor away from the nebular region. Such manual background acquisition reduces the efficiency of the AO observations. We took images in the H 2 v = 1-0 and Kcont filters with V377 Ori as the tip-tilt star. However, due to the high nebular background, we used θ 2 Ori A as the tip-tilt star for the [FeII]/Hcont and Br-gamma/Kcont sequences.
Data reduction The basic data reduction of the imaging frames (i. e. , dark correction, flat field, sky substraction) were performed using standard procedures for NIR imaging with a modified version of the reduction pipeline from the UCLA Galactic Center group based on Pyraf  (Lu et al. 2009 ). The set of frames slightly offset in telescope position were realigned and co-added to produce the final image. We calculated star centroids and offsets from one frame to another using the IRAF task IMALIGN in order to line up the images. Thus, the images were co-added by weighted median which suppresses the ghosts and the diffraction spike due to the bright θ 2 Ori A star. The final images are of dimension of 38. 64 ′′ × 38. 88 ′′ or 0. 08 pc × 0. 08 pc at 414 pc. In order to overcome the telescope pointing error (which is of the order of 0.
5 ′′ ) and determine the proper World Coordinate System (WCS), we matched the stars in our NIRC2 image to Gaia coordinates. We used the Gaia catalog early data release 3 (EDR3 3 ). For the flux calibration, we determined the conversion factor between the detector counts and physical units using previous observations of the H 2 1-0 S(1) line obtained with NTT/SOFI  (Walmsley et al. 2000). We convolved the continuum-subtracted 3 https://www. cosmos. esa. int/web/gaia Keck/NIRC2 H 2 line emission to the beam of the NTT/SOFI observation (seeing limited ∼1 ′′ ). We estimated the flux conversion factor in regions of the maps with high S/N, and the reduced science maps (in analog digital units per second) were multiplied by this conversion factor to get the flux-calibrated science maps in erg cm -2 s -1 sr -1.
Acknowledgements. We thank our referee,  Robert ODell  , for the care and quality of his comments and suggestions that improved the clarity of this paper. The data presented herein were obtained at the  W. M. Keck Observatory , which is operated as a scientific partnership among the  California Institute of Technology , the  University of California  and the  National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the  W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. This research has made use of the  Keck Observatory Archive  (KOA), which is operated by the  W.
M. Keck Observatory  and the  NASA Exoplanet Science Institute (NExScI) , under contract with the  National Aeronautics and Space Administration. Mark G. Wolfire  acknowledges partial support from a  NASA -Keck  data analysis grant (contract  1653861 ). Javier R. Goicoechea  thanks the  Spanish MCIYU  for funding support under grant  PID2019-106110GB-I00.
Preference for natural statistics in abstract visual patterns Our second experiment tested whether the patterns we generated for the targets in the first experiment also varied in their attractiveness. In a two-alternative forced-choice task (2-AFC), we assessed participants' preferences for the targets used in the detection task. The targets in this experiment were presented in pairs against a uniform grey background (examples in Figure  2A ). We calculated a preference score for each target using the Élő rating method, an iterative algorithm that performs pairwise comparisons and is commonly used to measure preference (see e. g. (28, 29) ). Targets with differing Fourier slopes elicited markedly different preferences (Figure  2 ). The resulting data can be fitted with a simple quadratic model (ranking~slope + slope²), which predicts a preference peak for a target slope of -2.
36 (fit adjusted R²: 0. 9136). This preference peak matches the preferred peak value reported in previous studies (see e. g. (30) ). The most widely accepted explanation for this value is that it matches the Fourier slope that is dominant in the natural visual environment (between -1. 81 and -2. 4 depending on the study, as reported by  (31). In order to confirm that the slope with the peak preference score in our second experiment corresponds with the dominant slope of natural scenes, we used our code to estimate the slope of 4,319 images of the Landscape Pictures dataset  (32)  which contains images of different types of landscapes (e. g. mountain, beach, desert). Methods of Fourier slope calculation vary across the literature, thus any comparison of slope values across datasets must be based on the same calculation.
We found an average slope of -2. 39 for the images in the Landscape Pictures dataset. This is very similar to the preference peak inferred from our experiment on a grey background (-2. 36), further corroborating the general finding that people prefer the Fourier slope of natural scenes. In comparison, human faces have an average slope of around -3. 35 (+/-0. 08) as tested on the Chicago (CFD and CFD-MR,  (33)  and the FACES (34) datasets and close to what is reported in the literature  (35, 36)  and human-made objects such as furniture also have steeper slopes, around -3. 89 (+/-0. 27), as tested on the Bonn dataset (37) and also similar to what was previously reported for this kind of objects  (38). This result, combined with those from the detection task, validate the use of the Fourier slope to manipulate both the camouflage effectiveness and the attractiveness of stimuli.
Online experiment and participant recruitment: The experiment took place online on a website hosted by the Montpellier Bioinformatics Biodiversity platform (University of Montpellier, France) and is still available for demonstration purposes: http://isemsurvey. mbb. univ-montp2. fr/pattern/. The experiment was programmed using a custom HTML script (including JavaScript, CSS, and PHP) and jsPsych, a JavaScript library for creating online experiments  (55) , and is available to play on all standard internet browsers. The experiment must be conducted with a desktop or laptop computer, excluding smartphones and tablets. Participants were recruited through advertisements on dedicated mailing lists and on social networks. Participants were not paid, and they had to be at least 16 years old. Approval from the French (deemed exempt, CNIL number 2-21044)) and American (deemed exempt, UMBC IRB #522) ethics committees was obtained prior to the start of the experiment.
Measure of pattern preference Preference scores for a target with a specific slope value are expressed as a ranking, computed via the Élő score (using the EloChoice package in R, v0. 29. 4; (56)), an iterative algorithm traditionally used in chess games and that performs pairwise comparisons. It is now frequently used as a measure of aesthetic value and is well suited for unbalanced design as the case of our study (see  Tribot et al. , 2018; Clark et al. , 2018  for similar use; Goodspeed, 2017 for a comparison of different pairwise ranking systems). As in  Clark et al. (2018) , we used the elochoice function with initial Élő scores set to 0, and k set to 100. We ran 1000 simulations to make sure our ratings were stable. We ran the analysis image-wise, that is by considering each of five versions of a stimulus as an independent condition to account for potential variability between stimulus versions.
Note that running the same code but by considering all five versions of a stimulus as the same condition did not yield different results. We estimated the minimum number of participants required to achieve stability in ratings using the raterprog function of the EloChoice package with the ratershuffle argument set to 10 to avoid a participant order effect. We found that using the data from 378 participants for images with a grey background (reliability index = 0. 57, Figure  S1 ) and from 263 participants was sufficient to reach stable ratings (reliability index = 0. 59, Figure  S2 ) for images with a patterned background.
Statistical analyses We removed participants who did not finish the experiment and participants who took part in the experiment more than once (IP address verification procedure). In total, we kept the data of 1757 participants out of the 2567 participation events that were recorded (68. 6%). For details on sociodemographic data, see supplementary material. For the detection task, we removed trials where responses were faster than neurophysiological limits (i. e. below 150ms, see e. g. Thorpe et al. , 1996). For the two alternative forced-choice tasks, we found no lateral bias in participants' responses: the left target was chosen in 50. 5 to 51% of the trials. We fitted our data with linear mixed-effects models (lme4 package,  (59) ) with REML set to false. We averaged the participants' Élő scores to obtain one score per image and used it as our dependent variable.
In a second step, this GAM, which represents natural conditions, is applied to predict natural streamflow, i. e. streamflow that would be expected in the absence of the reservoir, for the regulated period. The difference between the observed regulated streamflow signal and the predicted natural baseline should correspond to the reservoir operation signal. We apply this approach to reconstruct the seasonality of reservoir regulation, i. e. information on when water is stored in and released from a reservoir, from a dataset of 74 catchments in the central Alps with a known reservoir construction date (i. e. date when the reservoir went into operation). We group these reconstructed regulation seasonalities using functional clustering to identify groups of catchments with similar reservoir operation strategies.
, 2021). Our approach can be used to reconstruct reservoir operation signals in catchments where streamflow and climate data are available for a period before and after a known date of reservoir construction. Such information is more widely available than reservoir in-and outflow measurements, which means that the approach is applicable in different regions around the globe where streamflow observations and information on reservoir construction dates are available. Here, we use the proposed approach to shed light on spatial variations in reservoir regulation signals and their relationship to catchment characteristics. To do so, we apply the approach to extract reservoir signals from observed time series of 74 catchments in the central Alps (Sect. 2. 2). From this database of 74 extracted signals, we identify groups of catchments with similar reservoir operation strategies using functional data clustering  (Sect.
Dataset The central Alps are an interesting region for studying different reservoir regulation patterns because this region is characterized by diverse hydro-climatic regimes  (Bard et al. , 2015)  which are often heavily influenced by reservoirs  (Lehner et al. , 2005; Brunner et al. , 2019a). Therefore, we identify a large sample of 74 regulated catchments in the headwater regions of the four major European rivers originating in the central Alps, namely the Rhine, Rhône, Danube, and Po, for which the date of reservoir construction, i. e. the date when a reservoir went into operation, is known and for which observed daily streamflow data are available for both a period before and a period after reservoir construction (Fig. 1 ). The observed streamflow time series were obtained from national agencies in Switzerland (Federal Office for the Environment, FOEN), Austria (Austrian Ministry of Sustainability and Tourism), and eastern France (Banque HYDRO) and regional agencies in southern Germany (regions of Bavaria, Bavarian State Office for the Environment, and Baden-Württemberg, State Institute for the Environment Baden-Württemberg).
The streamflow records of the different catchments do not necessarily cover the same time period; however, each catchment has streamflow data for at least 10 years before and after reservoir construction (see Fig. 2 , for an example time series in the Swiss Alps). Northern Italy was excluded from the analysis because streamflow records provided by the regional agencies did not cover the pre-reservoir construction period. In addition to streamflow, we derive daily meteorological time series (precipitation and temperature) for each catchment from the gridded observational E-OBS dataset at 25 km spatial resolution for the period 1950-2020  (Cornes et al. , 2018)  by averaging over all grid cells within a catchment.
This balance between non-parametric modelling and a simple additive link facilitates the interpretation of the contribution of each ex-planatory variable. Still, other regression techniques (neural networks, random forest, and other machine learning algorithms) could replace our GAM approach in the scheme displayed in Fig. 3. Keeping in mind that our training period can be short (a few decades) at some locations, this lack of a large training dataset may also limit the application of fully data-driven machine learning techniques. The model covariates include the following three climatological drivers: (1) smoothed daily temperatures, (2) smoothed daily precipitation, and (3) day of the year (seasonality) and interpolated daily glacier mass balance changes (Fig. 3a ; for details on datasets, see Sect.
If present, missing values in the time series of all variables are replaced by the daily mean over the natural period for the natural data and over the regulated period for the regulated data. Temperature and precipitation time series are smoothed over a moving time window of 5 d to remove noise because smoothing improves model performance. Furthermore, data on reservoir locations and construction dates are also obtained from national agencies (FOEN in Switzerland, the Austrian Ministry of Sustainability and Tourism in Austria, and the Comité Fran¿ais des Barrages et Réservoirs in France; https://www. barrages-cfbr. eu/, last access: 1 March 2022) and open-source databases (for Germany; see Speckhann et al. , 2021). To account for changes in glacier melt contributions over time, we compute annual glacier.
Acknowledgements. We acknowledge the E-OBS dataset, from the  EU-FP6  project  UERRA  (https://www. uerra. eu,last access:  1 March 2022 ) and the  Copernicus Climate Change Service , and the data providers in the European Climate Assessment & Dataset (ECA & D) project; https://www. ecad. eu,last access:  1 March 2022 ). We thank  Loris Compagno , for providing the simulated glacier mass balance time series. In addition, we thank the two anonymous reviewers, for their constructive feedback, which helped to greatly improve the presentation of the results. Financial support. This research has been supported by the  Deutsche Forschungsgemeinschaft  (grant no. 2100371301 ), the  French national programmes (FRAISE-LEFE/INSU  and  80 PRIME CNRS-INSU ), and the  European H2020 XAIDA  (grant no. 101003469 ).
Philippe Naveau also received support of the  French Agence Nationale de la Recherche (ANR ; grant no. ANR-20-CE40-0025-01 ;  T-REX  project )  and the  ANR-Melody  (grant no. ANR-19-CE46-0011 ). This open-access publication was funded by the  University of Freiburg. Review statement. This paper was edited by  Micha Werner  and reviewed by  Charles Rougé  and one anonymous referee.
These sediments had been deposited continuously with an average sedimentation rate of 1. 9 cm kyr -1  [57]  and show uniform lithologies. The royalsocietypublishing. org/journal/rspb Proc. R. Soc. B 290: 20221994 palaeodepth in the study area exceeded 200 m for both MIS 20 and MIS 19, but became slightly shallower during MIS 18, as indicated by the greater abundance of goby otoliths  [11]. The sediment samples were diluted in water, sieved with a 250-µm mesh sieve and then dried in an oven. The sagittal otoliths (henceforth referred to simply as 'otoliths') were handpicked from the residues and identified based on the morphological characteristics  [45]  and through comparison with modern and fossil material from the Mediterranean  [11, 12, 58, 59]. Each otolith was photographed, and its length and width were measured (electronic supplementary material, figure  S1 ;  [60] ) using the microscope ZEISS SteREO Discovery V20 and the software ZEN (ZEISS Efficient Navigation).