dataesr/scientific-paragraphs-categorization huggingface dataset:
Software implementation We simulated the cattle breeding programme using AlphaSimR R package  [22]. We fitted the model in Eq. (  6 ) using the BLUPF90 family of programs  [19] , while all post-processing was done in R  [15]. To compute and summarise the partitions, we used the AlphaPart R package  [10] , to prepare data and present results, we used the collection of tidyverse R packages  [24]  and patchwork R package  [25]. The simulation and analysis code is fully available at the GitHub repository https://github. com/HighlanderLab/ toliveira_alphapart_variance.
This shows that nevertheless the female path contributed to the genetic gain, as we will analyse further below. We now turn attention to the summary of the partitions from Fig. 3  with a mean and variance shown in Fig. 4 , focusing on the medium-accuracy scenario. Means of partitions followed the centre of distributions shown in Fig. 3. In contrast, partitioning variances indicated a smaller variation for the male path than for the female Fig. 1  Flowchart representing a possible algorithm to evaluate contributions of paths to genetic mean and variance using samples of breeding values with the AlphaPart R package in a multi-trait case Fig. 2  Simulation scheme illustrating an overview of the medium-and high-accuracy scenarios Fig. 3  Distribution of breeding value partitions by sex and by sex and selection status [selected males (M(S)), non-selected males (M(N)), and females (F)] over generations for medium-accuracy scenario path, in line with only male selection in our example.
Thus, the participants should respond with the hand that they feel on the same side of the dot sticker. It is noteworthy that in each experiment, we used two response mappings manipulated across participants. In the first one, the blue dot sticker was on the right and the orange dot sticker was on the left and conversely for the second mapping. The script and the material used as well as the raw data and the analyses are all available online (https://osf. io/4yrfq/?view_only=f4477c728218428bbdc62c9e2fcdb650). In accordance with the journal's guidelines regarding the use of artificial intelligence, we employed a Large Language Model (LLM) known as ChatGPT to enhance the grammatical structure and rectify spelling errors in our manuscript. When encountering sentences or paragraphs with linguistic uncertainties, we used ChatGPT to make corrections while preserving the original content.
In this analysis, we combined the data from each experiment (Nfull dataset = 90, NExperiment 1 = 30, NExperiment 2 = 30, and NExperiment3 = 30). For all three experiments, we used the same data filtering procedure than in the previous independent analyses. In order to compare experiments, we directly analyzed the Simon effect of each participant in each experiment, which corresponds to the following formula: incompatible trials (mean IT of right response/left stimulus and left response/right stimulus) -compatible trials (mean IT of right response/right stimulus and left response/left stimulus). Note that the response here corresponds to the participants' real hands, not the ones they saw on screen. In this case, a positive Simon effect suggests that it depended on tactile/proprioceptive effects and a negative Simon effect suggests that it depended on visual effects.
Specifically, we compared three conditions. A condition where the visual effects of the hands were reversed along the horizontal axis compared to tactile/proprioceptive effects (i. e. , multimodal discrepancy), and the participants' task emphasized the relevance of visual effects (Experiment 1). A second condition where the same discrepancy was induced, but this time the task highlighted the relevance of tactile/proprioceptive effects (Experiment 2). In the last condition, the visual effects were no longer reversed (Experiment 3). For each condition, we assessed the nature (i. e. , normal or reversed), and the magnitude of the Simon effect. The data we gathered provided various contributions discussed below. A modulation of tactile/proprioceptive effects according to the task at-hand A major finding of our study is that Experiment 2 (tactile/proprioceptive-reversed group) revealed a Simon effect that depended on tactile/proprioceptive effects, while this effect disappeared in Experiment 1 (visual-reversed group).
e. , positive Simon effect), and another group according to visual effects (i. e. , negative Simon effect), although a clear segregation into two distinct groups is not evident in our dataset. A second level of influence may occur at the trial level. It is possible that participants coded their responses based on tactile/proprioceptive effects in some trials while relying on visual effects in other trials. This possibility cannot be ruled out, especially considering that some participants in our sample exhibited a null effect at an individual level (Figure  6 ). This possibility is supported by the results of our bin analysis, which revealed that the Simon effect becomes increasingly negative as ITs lengthen (until it becomes statistically significant for the last bin). This pattern suggests a critical between-trial variability that could explain the smaller magnitude of the Simon effect.
While remotely-sensed chlorophyll-a and salinity could be studied, satellite observations of sea surface temperature (SST) provide the most easily-accessible and longest input dataset for front detection  23, 24. Additionally thermal fronts are usually highly spatially correlated to density and chlorophyll-a fronts as well  11, 25, 26. Subsequently, SST observations are well-suited to Eulerian front diagnostics and to study front-marine life interactions. Histogram-based front detection, such as the Cayulla and Cornillon algorithm 17 which was further improved by Nieto et al. 18  , is the most widely used method to detect persistent front edges on large spatial scales from satellite SST observations  11, 12, 23. However, gradient-based methods present significant advantages to the study of regional fronts dynamics compared to histogram-based methods 6.
The present dataset offers the synergy of both Eulerian and Lagrangian perspectives by providing a multi-decadal record of both thermal gradient and backward FSLE fields at high resolution. Thermal gradients (resp. backward FSLEs) fields are computed on a 1 km (resp. 1. 56 km) grid, covering the period 2003-2020 (resp. 1994-2020), for two oceanic regions characterized by high biodiversity under conservation concerns: the Mediterranean Sea (MedSea) and the Southwest Indian Ocean (SWIO). Thermal gradients (resp. backward FSLEs) lead to thermal (resp. dynamical) fronts after region-appropriate thresholding, as illustrated in Fig. 1. Other data products informing on ocean fronts have been developed. Belkin et al. 23  produced a 9 km-resolution persistent front database for major Large Marine Ecosystems  43.
Additionally, daily backward FSLEs  44  (accessed on Jan 20th 2023) have been computed from altimetry derived daily velocities on a 4 km global grid since 1994 following the method of d'Ovidio et al. 32. Finally, the BOA has been used by the National Oceanic and Atmospheric Administration (NOAA) since 2013 to produce near real-time chlorophyll-a frontal maps (accessed on Jan 20th, 2023) for the U. S. coastal areas only. Those existing datasets target analyses of much larger spatial scales, their resolution being 2. 5 to 9 time coarser than the dataset presented here, and/or do not cover the present geographical domains of interest. Our front dataset differs to those existing by being specifically designed for multidisciplinary research, especially topics linking ocean weather and marine ecology  45.
It is dedicated to researchers who investigate front dynamics at space and time scales that relate to the highly patchy distribution of biological production and the rapidly evolving behavior (for foraging, mating, socializing, migrating, etc. ) of marine species, especially top marine predators. Additionally, the greater length of the provided time series allows for analyzing long-term variability and trends. Our ocean front dataset for the Mediterranean Sea and the Southwest Indian Ocean aims to Fig. 1  Illustration showing how NOMAD  51  users can go from thermal gradients (resp. backward FSLEs) to thermal (resp. dynamical) fronts via region-specific thresholding, with example of fields before and after thresholding. All fields were taken on July 1st, 2020. The colormap is from cmocean  80  and with the same range as in Fig.
Methods thermal gradients. We use the BOA  16  , and more specifically Benjamin Galuardi's pseudo-code written in R (available on https://github. com/galuardi/boaR, accessed on May 19th, 2022) to compute thermal gradient magnitudes. We apply the BOA on daily satellite Sea Surface Temperatures (SST) from the Multi-scale Ultrahigh Resolution sea-surface temperature analysis (MUR SST, accessed on Jan 10th, 2023)  46  , the resulting field is a thermal gradient expressed in °C/km. The resulting Southwestern Indian Ocean (SWIO) thermal gradient dataset covers latitudes between 5°S and 35°S and longitudes between 30°E to 80°E, and the Mediterranean Sea (MedSea) dataset covers latitudes between 30°N and 46°N and longitudes between -6°E to 37°E. The outputs for both geographical domains are produced daily from 2003 to 2020 and at the same spatial resolution of 1/100° which is about 1 km.
0384 °C. The choice of threshold is based on a preliminary comparison between MUR SST standard deviation and our thermal gradient dataset. Finite-size Lyapunov exponents. In this work, backward-in-time FSLEs in the SWIO are computed from remotely-sensed Sea Surface Height (SSH) at 1/4° spatial resolution from Global Total Surface and 15 m Current (CLS, 2018, COPERNICUS-GLOBCURRENT, accessed on Jan 10th, 2023) from Altimetric absolute geostrophic velocities and Modeled Ekman Current Reprocessing  47. In the MedSea, backward FSLEs are computed from daily absolute geostrophic surface currents derived from Sea Level Anomalies (SLA) at 1/8° spatial resolution from a SSALTO/DUACS multimission altimeter regional L4 product released in 2016 by AVISO + and based on regional mean dynamic topography  47  (https://data.
δ 0 corresponds to the spatial resolution of the FSLE grid, which is here 1/64° or approximately 1. 56 km. δ f is the final distance between particles, here it is set so that δ f = 10δ 0. τ is the minimum time (among the 4 particle pairs) that it takes for the particles to reach the distance δ f. FSLEs are thus expressed in day -1. Similarly to the thermal front dataset, the SWIO FSLE dataset covers latitudes between 5°S and 35°S and longitude between 30°E to 80°E, and the MedSea dataset covers latitudes between 30°N and 46°N and longitudes between -6°E to 37°E. Both geographical domains have a spatial resolution of 1/64° which is about 1. 56 km, and the output is produced daily from 1994 to 2020. Each daily FSLE dataset includes a flag variable indicating whether pixels are located on land (flag = 2), or if they are "suitable" (flag = 0) or "unsuitable" (flag = 1) for front analysis.
Moreover, it is very likely that marine organisms aggregate actively along the most adequate fronts for foraging not based on their absolute characteristics but rather on their relative properties (i. e. targeting the most advantageous fronts as compared to all those found in the surroundings). As such, and to adapt our dataset to users interested in the interaction between fronts and marine life at both basin and eco-region scales, we provide thresholds for the MedSea and the SWIO basins but also for each Longhurst's provinces  48, 49  and Spalding's ecoregions  50  falling into each geographical domain (Fig. 3 ). For both levels of analysis, we set the regional and subregional thresholds at the value of the 70 th percentile computed over all the "suitable" pixels in the multidecadal dataset.
Data Records The oceaN frOnt dataset for the Mediterranean seA and southwest inDian ocean (NOMAD)  51  can be accessed through Ifremer online data repository. Figure  2  is a sample snapshot of the thermal gradient and backward-in-time FSLE field in the MedSea and the SWIO. The front dataset is 940 Gb in total, and is separated into 4 folders as detailed in Table  1. Each folder contains yearly subfolders, consisting of daily netcdf files. Each netcdf file provides: thermal gradient or backward FSLE values, pixel quality flags, but also front threshold values specific to each Longhurst's province and Spalding's ecoregion found in the MedSea or SWIO, as well as the corresponding percentage of "suitable" pixels found in those same ecologically-relevant subregions of the MedSea and the SWIO. Each netcdf file contains variables as shown below: Thermal gradient netcdf files (TG_*region*_*date*.
The integration and interpolation of those different satellite products is done by the Multi-Resolution Variational Analysis (MRVA) method, which uses scale-dependent time windows to adjust for various spatial resolutions  46. MUR analysis thus provides improved spatial coverage and resolution of its SST product compared to conventional L3 data. However, such interpolation procedure also tends to blur oceanic features of interest such as eddies and fronts. It is thus necessary to find a way to identify pixels which have been blurred beyond a certain limit. The MUR SST product comes with a standard deviation of the estimation error (in °C) at each grid point  46. We use this estimate of MUR analysis uncertainty to identify a suitable threshold above which the multidecadal dataset is considered too blurred for front analysis.
The MUR analysis product also provides the time lag to most recent 1 km data, which would help the filtering, but the variable is only included after July 2016. Figure  8  of Sudre et al. work  27  further illustrates the relationship between thermal front detection and the two quality flags available in the MUR dataset. We finally opted for using only the standard deviation of the estimation error to provide a uniform and consistent multidecadal database. Visual comparisons between MUR standard deviation of the estimation error and our thermal gradient dataset were performed to find an arbitrary threshold that would return well-defined frontal structures without too much gaps. A too high estimation error limit would Fig. 4  Example of thermal gradient magnitude from the BOA (a) and corresponding MUR analysed SST value (b) for a section at 26°S in the SWIO, for July 1st, 2020.
However, the SST CCI analysis is offered on a 1/20° grid (about 5 km), which is 5 times coarser than that of MUR SST. When pairing with biological observations such as GPS tracks, aerial surveys or sightings, the SST CCI may return spurious and artificial correlations. In designing NOMAD  51  , the authors favored the grid resolution over the climate readiness of the final product to better fit typical scales of marine animal trajectories in the SWIO  [54] [55] [56]  , hence the final choice of SST MUR over the SST CCI analysis. The comparison between both MUR SST and SST CCI analysis is illustrated in Fig. 5  over the front-rich Mozambique Channel  27. The larger thermal gradient magnitudes from SST CCI could be explained by the contextual median filtering step in the BOA  16. We hope future ocean front datasets will be able to combine the advantage of both fine-grid resolution and climate-readiness without much compromise.
about dynamical fronts. NOMAD 51 provides a multidecadal record of backward-in-time FSLEs, which can be interpreted as dynamical fronts through region-specific thresholding (Fig. 1 ). In the past 20 years, FSLEs have been recognized by the ocean science community as a reliable way to identify Lagrangian coherent structures  32, 34, [57] [58] [59]. Indeed, FSLEs tend to be better-suited to transport barrier detection than other diagnostics such as the Okubo-Weiss parameter and finite-time Lyapunov exponents  33, 59, 60. Additionally, FSLEs perform well in the presence of noise and unresolved scales  34. Finally FSLEs pairs well with tracer-based front indicators, such as sea-surface temperature  37, 61  and phytoplankton communities  8, 42. Choice of velocity data. The choice of suitable velocity products for the MedSea and the SWIO was motivated by their spatial and temporal resolutions but also the extent of the datasets.
Although both velocity datasets perform well at the mesoscale (typical horizontal scales of 100 km), it is worth noting that they are not climate-ready. The choice of a uniform global climate-ready alternative, such as DUACS geostrophy-only currents distributed by the Copernicus Climate Change Service (C3S) (https://data. marine. copernicus. eu, accessed on Feb 10th, 2023), would have been uniform between both domains but would also have resulted in the loss of the Ekman component in the SWIO and led to a spatial resolution downgrade from 1/8° to 1/4° in the MedSea. The comparison between FSLEs derived from Globcurrent total velocities  47  and FSLEs derived from geostrophy-only currents from the C3S  63  in the SWIO is illustrated in Fig. 6. The difference between FSLEs computed from total and geostrophy-only currents reveals small changes in their magnitude and overall pattern, but also show some variations in the positions of the LCS (in the approximate range of 0.
5-1°or 50-100 km). These displacements are especially strong in front-rich regions such as the Mozambique Channel and South East of Madagascar. We thus chose to let go of uniformity and climate-readiness to ensure the best rendering of front structures with available Added-value of spatial resolution. The NOMAD  51  dataset distinguishes itself from existing front databases by its higher spatial and temporal resolutions. A great advantage of FSLEs is that they are robust to small changes in the velocity field, and they also exhibit typical multifractal properties  34. These properties ensure that FSLEs produced on a finer-grid than the original velocity grid are indeed meaningful and not artificial. They cannot resolve all the small scales processes, but they render well the dynamics driven by the coarser velocity field.
We argue that spatial resolution is critical when investigating fronts-marine life interactions. To illustrate this statement, we compare NOMAD  51  backward FSLE dataset to the AVISO FSLE product  44  in the SWIO. The AVISO FSLE field is computed from 1/4° SSALTO/Duacs delayed-time global ocean absolute geostrophic currents (DUACS2018 DT MADT UV products) on a 4 km grid. The AVISO FSLE product  44  differs from NOMAD  51  FSLEs in several parameters including velocity field, spatial resolution, times of integration, δ 0 and δ f. We have already contrasted the difference between total and geostrophy-only currents in FSLE computations in the previous paragraph. Additionally, we assume that the AVISO FSLE product time of integration, δ 0 and δ f have been optimized for the flow field. We will thus only focus here on why we think the higher spatial resolution of the NOMAD  51  FSLE field is a significant added-value for biological use.
e. when the flag is equal to 0, and TND is the total number of days in the whole dataset. The FSD for thermal gradients reveals which regions are more prone to cloudiness between 2003 and 2020 (Fig. 9a, c ). In the MedSea, most of the open ocean presents a FSD above 85% thanks to the dominant clear sky typical of Mediterranean climate. The Mediterranean regions most covered by clouds are the western Alboran Sea, the northern Aegean Sea and more generally coastal areas. In the SWIO, most of the open ocean in the Mozambique Channel and east of Madagascar has a FSD above 80%. Continental shelves, Islands (the Comoros archipelago, Mayotte, La Réunion and Mauricius islands) and northeast of the South Equatorial Current are marked by a reduced FSD, but rarely fall below 50%. The FSD for the backward FSLE dataset reveals regions most accessible to advected ocean particles between 1994 and 2020 (Fig.
9b, d ). In the MedSea, the FSD is lowest on continental shelves and western Alboran Sea but above 90% for most of the open ocean. In the SWIO, the FSD is above 90% in the Mozambique Channel and most of the western Indian Ocean. Lesser FSD can be found along the path of the South Equatorial Current, along the west Madagascar continental shelves and more generally at the border of the geographical domain. Overall both datasets and both geographical domains present a very good ratio of suitable pixel for front analysis. Mean fields. Time average maps have been produced for both thermal gradients (Fig. 10a, c ) and backward FSLE fields (Fig. 10b, d ) in both geographical domains over their respective time period (2003-2020 for thermal gradients and 1994-2020 for backward FSLEs). Only the "suitable" pixels were counted when time-averaging.
Time-averaged backward FSLE values are also stronger in the path of the NEMC and SEMC, but also at the side edges of the eddy train that characterizes the Mozambique Channel circulation  66, 67  , and towards the Agulhas current  68. These results are consistent with previous works on time-averaged thermal gradients  58  and FSLEs in the MedSea  32, 69  , but also in the SWIO  11, 27  , thus validating our dataset for large time scales. Front frequency. Another helpful metric to assess our dataset is the front frequency (FF), i. e. frequency of front days (amongst suitable days only) at a position (x, y), computed as follow: Where SNFD (x, y) is the number of front days (amongst "suitable" days only) at (x, y), and TNSD (x, y) is the total number of "suitable" days at the same position. "Front days" correspond to days when thermal gradient of FSLE values at (x, y) are above a pre-determined 70 th percentile threshold.
Usage Notes The oceaN frOnt for the Mediterranean seA and southwest inDian ocean dataset (NOMAD  51  ) is a multi-decadal record of thermal gradients and backward FSLEs in the Southwest Indian Ocean and the Mediterranean Sea. The NOMAD  51  distinguishes itself from existing alternatives  23, 44  by its higher temporal and spatial resolution and extent. NOMAD  51  is suitable for a broad variety of applications where the detection of thermal fronts and attracting Lagrangian coherent structures in the MedSea and the SWIO is needed. These include the identification of spatial and temporal dynamics of thermal and dynamic fronts, along with multidecadal variability analyses. Additionally, the release of our front dataset makes the investigation of the relationship between fronts and marine life possible thanks to a high temporal and spatial resolution.
The provided dataset is divided into 4 folders as described in Table  1. Each folder contains yearly subfolders with daily netcdf files. The netcdf files can be read as is, as they directly provide direct thermal gradient or backward-in-time FSLE fields. We suggest to use Python language software with xarray and dask packages, which are best suited to handle large datasets and High Power Computing. NOMAD  51  will be updated on a yearly basis. NoMaD visualization. The authors suggest using the quality flag provided in each dataset to select "suitable" pixels (i. e. flag = 0) before further front analysis. Then front detection is a simple thresholding of the field of interest as illustrated in Fig. 1. Subregion-specific thresholds are added as attributes of each netcdf file, but custom thresholding is also possible.