dataesr/scientific-paragraphs-categorization huggingface dataset:
3 and onwards). Indeed, only one of the seven proteomics studies listed in Table 2  (Kwasiborski et al. , 2008)  flags myoglobin as a significant contributor to fresh pork color. A possible inference from this is that the various factors affecting paleness of pork via the effect of post-mortem muscle metabolism and temperature, and the partial denaturation of sarcoplasmic proteins (mostly enzymes from the energy metabolism pathways) and myofibrillar proteins would play major roles in the determination of fresh pork color. Proteomics seemed as a useful tool to decipher such mechanisms and to broaden our knowledge on the underlying mechanisms. However, the large amount of data generated by meat research proteomics can be overwhelming. The integration of the proteomics findings and/or datasets through data-, text-mining and computational biology methods (bioinformatics) is a promising way to better understand the complex biological systems and refine the list of candidate biomarkers  (Vaudel et al.
, 2016). In fact, data reuse is a cutting-edge, active, and evolving field  (Griss, Perez-Riverol, Hermjakob, & Vizcaíno, 2015) , recently successfully applied to meat research to rediscover and reshape the publicly available proteomics data to decipher the unknowns of meat quality determination  (Gagaoua et al. , 2020; Gagaoua et al. , 2021). Therefore, the integration of fresh pork color proteomics studies and the related proteins in a unique database (comprehensive data repository) is more than needed to have a critical overview on the conducted studies. Furthermore, several attempts can be achived through such integrative approach (Fig. 2 ), likely i) to provide, in the frame of knowledge discovery, a more holistic understanding of the biological processes and their interactions in underpinning pork color determination and variation; ii) to perform a cross-study analysis with the aim of identifying the consistent and commonly reported proteins related to pork color traits, and accordingly develop an integromics meta-analysis approach to reveal new and robust features and/or generic molecular signatures of pork color beyond the scope of individual studies; iii) to perform a systems biology and network analysis with the aim of creating more comprehensive and accurate models of biological processes governing pork color determination; and (iv) to shortlist and propose, in the frame of biomarker discovery, potential candidate biomarkers related/correlated to pork color traits.
Therefore, the discovery of biomarkers enables, for example, the design of protein chips to predict meat quality  (Gagaoua, Bonnet, De Koning, & Picard, 2018; Picard et al. , 2018). Thus, one of the lofty goals is to explain and/or predict meat quality, by comparing its molecular signatures with those of reference groups gathered in databases and constituted on the basis of a targeted quality trait using specific thresholds. In pork, it was also successfully applied to study most of the farm-to-fork factors discussed above standing from productive (breed, diet, stress, …) to technological (ageing, cooking, …) factors, including the assessment of multiple quality traits and quality defects of both fresh and processed pork products (for review:  (Lopez-Pedrouso et al. , 2020; Zhou, Pan, Cao, & Zhou, 2021) ).
Proteomics as an emerging tool in meat (pork) color research The application of proteomics in meat color research is very recent  (Gagaoua et al. , 2020; Joseph, Suman, Rentfrow, Li, & Beach, 2012 ;   Main objectives of integrating proteomics datasets through data mining and bioinformatics as a promising way to better understand complex biological systems and refine the list of pork meat quality (color) biomarkers. Suman et al. , 2023; della Malva et al. , 2023)  although the first studies on pork color that evaluated its potential to study color stability or determination were conducted few years before those on beef color (Table  S1 ). Based on the seven studies available in the literature and gathered in Table  S1  (details are given later) that have used proteomics to correlate muscle proteome with pork color traits  (D΄Alessandro, Marrocco, Zolla, D΄Andrea, & Zolla, 2011; Hwang, Park, Kim, Cho, & Lee, 2005; Kim, Jeong, Yang, & Hur, 2019; Kwasiborski et al.
Current list of biomarkers and molecular functions related to pork color determination The data-and text-mining on the seven eligible studies permitted creating a database of 46 putative protein biomarkers of pork color (Table  2 ), which were identified by their gene names retrieved from UniProt (https://www. uniprot. org/). The database was subjected to bioinformatics analyses  (Kiyimba, Gagaoua, Suman, Mafi, & Ramanathan, 2022)  for protein-protein interactions using STRING database (https://string-db. org/), for Gene Ontology (GO) enrichment using the webservice tools Metascape® (https://metascape. org/) or ProteINSIDE (http://www. proteinside. org/) and for Voronoi treemap classification using Proteomaps (https://bionic-vis. biologie. uni-greifswald. de/). The proteins (given by their gene names and detailed in Table  2 ) had the highest number (n = 38) of significantly correlated proteins with lightness (L*) (Fig.
Lessons from the integromics database on the major proteins and molecular signatures involved in pork color determination From a pork color molecular signatures perspective and in line with the earlier integromics study on beef color  (Gagaoua et al. , 2020) , the top two dominant biochemical pathways in pork color were catalytic and energy metabolism (half of which are glycolytic enzymes) and contractile and muscle structure, followed by protein folding and response to oxidative stress, binding and transport proteins, signaling, apoptosis and proteolysis (Fig. 5 ). Referring to the frequently identified putative protein biomarkers among the 7 studies irrespective of the color traits, 10 of them were identified in more than one study (≥ 2 times): GAPDH, LDH, GPD1, CKM and PGM1 (energy metabolism), HSPB1 and HSPA2 (heat shock proteins), MLC1 and TNNT2 (muscle structure) and HBB (heme binding and oxygen transport).
TNNT2 seemed to be a negative biomarker for a* and b*, while it is in both directions for L*. GPD1 seemed to be a negative biomarker of L* and b*, and in both directions for a* (Fig. 4 ). The inversion in the direction of the correlations could occur when the biochemical mechanism underlying such correlation has a major contribution to the phenomenon under study  (Terlouw et al. , 2021) , but other factors are also involved, such as animal age, breed, muscle type, husbandry factors, etc. (For review:  (Gagaoua et al. , 2020) ). The multifactorial and interdependent nature of the influence of post-mortem metabolism and oxidative properties observed in this database on meat color is well known  (Gagaoua, Terlouw, & Picard, 2021; Suman et al. , 2023). This may partially explain the inversion in the direction of the correlations observed in this pork color biomarkers database (Table  2  and Fig.
Moreover, the in-depth analysis of the myriad factors at interplay revealed that lightness is the major parameter that is impacted compared to redness and yellowness coordinates. This has been confirmed by the integromics meta-analysis, which identify that most of the candidate protein biomarkers (83%) are related to lightness compared to redness (37%) and yellowness (28%). Pork color determination results from muscle biology and metabolism, muscle proteome profile, but to less extent myoglobin chemistry. The interactions of these Fig. 5. A STRING functional interaction network linking the 46 proteins identified by proteomic studies to be related to porcine meat color traits (Table  2 ). The interaction map was generated from a web-based search of the STRING database (http://string-db. org/).
Default settings of confidence of 0. 5 and 4 criteria for linkage: Co-occurrence, experimental evidences, existing databases and text mining were used. Considering the limitation of the GO annotation of genes in porcine, we converted their Uniprot access IDs to orthologous human EntrezGene IDs using BioMart (http://www. ensembl. org/biomart/). multiple factors affect the structure and state of muscle proteomes, and involve sophisticated interconnected molecular signatures as underlying mechanisms in the development of fresh pork color. Thanks to the integrative proteomics approach we applied in this paper, we extended our knowledge on the interplay of multiple molecular and biochemical processes governing fresh pork color. First, it revealed a certain level of disparity among studies and suggest only 10 proteins candidates from the created repertoire for future evaluation following the pipeline of biomarkers discovery  (Gagaoua, 2021) , by using for instance, targeted proteomics  (Wu et al.
, 2018). However, the impact of stressor intensity (dose and duration) on the inter-individual variability in learning performance was not analysed. We therefore explored these effects by analysing the raw datasets from 23 studies that assessed bee cognition applying olfactory and visual learning protocols in either an appetitive or aversive context. Although a decreased cognitive performance was expected in stressed bees, we predicted that the effect of stressors on the variability would depend on the stressor intensity, which would help estimate the hazardous nature of a given stressor.
| Search and selection of datasets The search for datasets in scientific publications falling within the scope of our research question was performed in July 2020 using the PubMed database. The words used for the search were ('Stressor' OR 'Pesticide' OR 'Parasite') AND ('Cognition' OR 'Learning') AND ('Bees'). This search was not restricted to any section of the manuscripts and automatically extended to similar terms intended under the MeSH hierarchy of the database. A total of 240 studies were found, of which 18 met our inclusion criteria regarding the cognitive task and the type of stressor (see below). The search terms under which each study was found are available in Table  S1  in Supporting Information. Five datasets belonging to the authors of this study were also included as they filled the inclusion criteria.
| Dataset organization and normalization of variables All but three raw datasets were available online with the published material. Those three datasets were kindly provided by their authors, that is, Dara Stanley and Ken Tan. The raw data were downloaded and saved as. csv files. A new dataset was created, which combined information on the species, the cognitive task studied, the type of stressor, the type of exposure (acute/chronic), and, in the case of pesticide studies, the dose (μg/bee) or concentration (ppb). The dose (acute exposure) and concentration (chronic exposure) were normalized as the percentage of the LD50. When learning performance was measured as a binary response (e. g. success vs. failure) across multiple trials, the raw data were used to calculate a learning score for each individual corresponding to the number of successful trials.
This was required because the variance in binary variables can be mathematically predicted from the mean and sample size and does not reflect biological variance (see Figure  S1  in Supporting Information). Each study provided individual cognitive scores for at least one experimental treatment and control group. There was a total of 73 experimental treatments across the 23 studies. To compare the mean cognitive performance and the cognitive variability across studies, we used a standardized method for the meta-analysis of variation  (Nakagawa et al. , 2015; Senior et al. , 2020). This method controls for the mean-variance linear relationship that may exist in a dataset by using unbiased effect size statistics of the mean and variability, i. e. the natural logarithm of the ratio between the means (lnRR) and the natural logarithm of the ratio between the coefficients of variation (lnCVR) of treated and control groups, respectively.
Changes in lnCVR are not an indirect consequence of changes in lnRR, as would have been the case had we analysed the variance and the mean, but they rather reflect changes in variability per se. The two prerequisites for this method are (i) to use log scale data and (ii) to observe a mean-variance linear relationship. Studies for which negative cognitive scores were present were transformed to log-scale data by adding the minimum score to all individuals. The mean and standard deviation of the cognitive scores, as well as sample sizes, were calculated for each experimental treatment and control group. A linear relationship and positive correlation were found between the log sample mean and standard deviation in our dataset (see Figure  S2  in Supporting Information). All prerequisites being met, we then calculated the lnRR and lnCVR for each experimental treatment and control group (i.
| Data analyses All analyses were conducted in R Studio v. 1. 2. 5033  (RStudio Team, 2015). The code is available. The package metafor  (Viechtbauer, 2010)  was used to compute multilevel meta-analytic models (MLMA), multilevel meta-regression models (MLMR) and to generate forest plots. The study and experiment identifier were always set as nested random effects. For MLMR and depending on the question, the type of stressor, the genus, the type of task, the exposure duration and/or the percentage of LD50 were defined as fixed effects. The restricted maximum likelihood approach (REML) was used to estimate the parameters of the meta-analysis models. Forest plots were used to show the effect size estimates lnRR and lnCVR and their 95% confidence interval (CI). In this approach, positive effect sizes reflect higher means (lnRR) or coefficients of variation (lnCVR) in the treated group compared with controls.
| Variability in cognitive performance increased with stressor intensity To investigate whether stressor intensity modulates the effects of stressors on the variability of the cognitive performance, we focused our analyses on the 14 pesticide studies of our dataset for which the percentage of LD50 was known (Figure  3 ). Pesticide studies were the most abundant in the literature and present the advantage that a normalization of stressor intensity across drugs F I G U R E 1 Stressors decreased the mean cognitive performance and increased cognitive variability among bees. Forest plots showing the effect size estimates for the mean (lnRR; left panel) and variability (lnCVR; right panel) across the 23 studies. The 95% confidence intervals are displayed for each experiment. Intervals not crossing the zero line reflect a significant effect of the stressors.
| DISCUSS ION Many environmental stressors affect the behaviour and cognition of animals  (Killen et al. , 2013; Klein et al. , 2017; Siviter et al. , 2018 Siviter et al. , , 2021)). While studies reporting such sublethal effects have typically focused on mean phenotypic traits, all individuals in a population are not similarly affected by stressors, and the resulting phenotypic variability may be critical for stress resilience. Here we tested this hypothesis by analysing raw datasets of 23 bee studies. We showed different effects on the cognitive mean and inter-individual variability of insects exposed to stressors, depending on stress level and exposure mode, thus raising the importance of examining variability in addition to mean phenotypic traits in ecotoxicological studies. Focusing on pesticides revealed the mean cognitive performance of bees was altered by both chronic and acute exposures, with strongest effects observed after a chronic exposure.
These intriguing effects of stress on cognitive traits demonstrate the importance of considering inter-individual phenotypic variability in analyses of the impact of environmental stressors on animals. We hope such an approach can be generalized to assess more thoroughly the hazardous nature of the stressors and identify the modes of exposure that might be less damaging for wild populations. An effort should be made to apply the analysis of phenotypic variability to studies on bee pathogens and parasites as they are strongly associated with population decline and yet represented only three experiments in our dataset  (Dainat et al. , 2012; Fürst et al. , 2014). Future investigations should also consider the possible interaction between agrochemicals, which have synergistic effects on bee mortality, but antagonistic effects on behaviour when looking at the mean only  (Siviter et al.
DATA AVA I L A B I L I T Y S TAT E M E N T Data available from Dryad Digital Repository: https://doi. org/10. 5061/dryad. 63xsj 3v72  (Cabirol et al. , 2023b). The R code used for the analyses is available on Zenodo: https://doi. org/ 10. 5281/ zenodo. 7794106  (Cabirol et al. , 2023a).
658, df = 1, p = 0. 056, lnCVR: QM = 3. 069, df = 1, p = 0. 080). This means pesticides were on average more detrimental to cognitive performance at high doses, yet inducing a greater variability, irrespective of exposure duration. These opposite effects of stressor intensity on lnRR and lnCVR triggered an indirect negative correlation between the two estimates in the whole dataset (Pearson's correlation test, r = -0. 667, p < 0. 001). F I G U R E 2 Effect of stressors on the cognitive mean and variability were consistent across experimental designs. Forest plots showing the mean effects size estimates lnRR (left) and lnCVR (right) for (a) different types of stressors, (b) different bee genera and, (c) different cognitive tasks. The 95% confidence intervals are displayed and indicate a significant effect of the stressor when not crossing the zero line.
O R CO NTR I B UTI O N S Amélie Cabirol, Tamara Gómez-Moracho, Cristian Pasquaretta and Mathieu Lihoreau designed the study. Amélie Cabirol, Tamara Gómez-Moracho and Coline Monchanin collected the data. Tamara Gómez-Moracho processed the data and prepared dataset. Amélie Cabirol, Tamara Gómez-Moracho, Coline Monchanin and Cristian Pasquaretta analysed the data. Amélie Cabirol wrote the first draft of the manuscript. All authors substantially contributed to revisions.
A consistent dataset of lake surface water temperature, ice cover, water-leaving reflectance, water level and extent is presented. the collection constitutes the Lakes Essential Climate Variable (ECV) for inland waters. The data span combined satellite observations from 1992 to 2020 inclusive and quantifies over 2000 relatively large lakes, which represent a small fraction of the number of lakes worldwide but a significant fraction of global freshwater surface. Visible and near-infrared optical imagery, thermal imagery and microwave radar data from satellites have been exploited. all observations are provided in a common grid at 1/120° latitude-longitude resolution, jointly in daily files. The data/algorithms have been validated against in situ measurements where possible. Consistency analysis between the variables has guided the development of the joint dataset.
Many studies identified the essential lake response variables that act as indicators of the effects of climate change on both the lake and the catchment  8. Lakes are therefore important carriers of climate-related signals. The Global Climate Observing System (GCOS)  9  defines "Lakes" as an Essential Climate Variable (ECV) with six linked quantities characterising their physical state, namely, lake surface temperature, water level and extent, ice cover and thickness, and lake water leaving reflectance (colour). All interact and contribute to the lake physical response to climate change. Monitoring these variables closely is a starting point for understanding the complex lake environment and its variations in time and space, a need to which this dataset responds. For convenience, we continue to refer to these variables as "lake" observations also when they are included for water reservoirs, sections of rivers, or lagoons.
However, in situ data are indispensable, being direct measurements, including for their use in the validation of remote sensing measurements. Satellite systems offer globally consistent observations including measurements for lakes for which in situ measurements are not available. Differing technologies lead to a variety of sampling patterns, resolution, uncertainty and revisit periods. Often, such characteristics have improved upon over the period addressed by the dataset as technology advances. Optical and thermal imagery may be limited by cloud cover, which is not the case for active and passive microwave observations. The European Space Agency Climate Change Initiative (ESA CCI) is generating multi-decadal satellite-based products to serve the climate modelling and climate user community, including the lakes dataset described herein.
The dataset addresses all the thematic variables except lake ice thickenss, namely lake surface water temperature, lake ice cover, lake colour (as lake water-leaving reflectance), lake water level and lake water extent spanning the period 1992-2020. The variables are derived from measurements by instruments including radar altimeters, radiometers, spectrometers, and multispectral scanner systems on satellites such as Sentinel-3, Sentinel-2, Terra, Aqua, Envisat, ERS-2, MetOp, Landsat, Topex/Poseidon, Jason, GFO, Saral/AltiKa and Cryosat. The observations of the five thematic variables derived from data of multiple sensors and satellites consequently span different temporal and spatial resolutions. The observations for each of the thematic variables derived from the different sensors have been regridded to create a multi-variable dataset with a common spatio-temporal representation.
The thematic variables have been individually validated against in situ measurements (when available, otherwise with other appropriate methods) and the level of consistency among these variables has been explored. Exploring the physical processes occurring in the lakes have highlighted some issues especially in the thematic variables for which the retrieval is less mature and more complex. This particularly benefits the optical retrieval of the lake water leaving reflectance during periods of sub-pixel or thin ice cover, and biogeochemical variables derived from the lake water leaving reflectance (turbidity and the phytoplankton pigment chlorophyll-a). At the time of this study, the dataset presented in this paper is the longest and most complete collection of satellite observations of the Lakes ECV, it is 'analysis-ready' and it responds to the GCOS ECV monitoring requirements.
Methods The dataset contains data products from multiple thematic variables of the Lakes ECV consistently stored in a regular spatial and temporal grid. The collection of satellite observations includes the following five physical variables which capture specific climate responses of global inland water bodies: • Lake Water Level (LWL): fundamental for understanding the balance between water inputs and water loss. • Lake Water Extent (LWE): elucidates lake expansion (e. g. glacial regions) and the effects of drought (e. g. arid environments). It also determines the locally cooling effect of water bodies. • Lake Surface Water Temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. • Lake Ice Cover (LIC): freeze-up in autumn/winter and break-up in spring are proxies for gradually changing climate patterns and seasonality.