dataesr/scientific-paragraphs-categorization huggingface dataset:
Feature Extraction In the feature extraction process, the characteristics of the selected rice crop were extracted from the images captured across all channels. An orthomosaic was then created from the 98 images captured from the five channels, as shown in Figure  3a. The orthomosaic was created using the geo-tagged ground-level markers depicted in Figure  2  to ensure that all channels were aligned when subsampling was performed in the plot. The channel data were combined and registered into two images for the purpose of visualizing the orthomosaic. The first orthomosaic, shown in Figure  3b , contains the red, green, and blue channels. The second orthomosaic, displayed in Figure  3c , includes the red, red-edge, and near-infrared channels. In the figures, each rice plot is subdivided into 40 subplots, each of which contains an average of one plant.
Estimation Models The ground-truth data for training these ML algorithms are specified in Tables  1  and 2. The data include the fresh weight, dry weight, percentage of water content, and measured SPAD values, which, through their adherence to a linear correlation, were directly correlated with the leaf-blade N concentrations. A number of plants that matched the previously mentioned plots were manually collected for destructive biomass testing. The samples taken from each subplot were weighed at the time of cutting for a fresh weight estimate, and again after drying to produce a dry-weight estimate in order to define the corresponding ground truth. The collected multispectral image database consisted of 11,800 images, resulting from the 59 rice plots and the 40 subplots in each rice plot, totaling 2360 images in each channel.
The goal is to recursively create binary splits that maximize the reduction in the variance of the target variable. The final model is a tree structure, where each leaf node contains a predicted value for the target variable  [46]. Tree regression can be prone to overfitting so techniques such as pruning or ensembling multiple trees can be used to improve its performance. MSE(t) = 1 N t ∑ i∈D t (y i -ȳt ) 2 (8) where MSE(t) is the mean square error at node t, N t is the total number of samples at node t, D t is the dataset at node t, y i is the target value of the i-th instance at node t, and ȳt is the average value of the responses at node t. Ensemble regression involves combining multiple models to make a more accurate prediction. One popular ensemble technique is random forest, which is an extension of the decision tree model.
In SVM regression, the goal is to find a hyperplane that maximizes the margin between the predicted values and the actual values. The model tries to fit a linear function to the data while minimizing the errors or deviations from the target variable  [49]. In cases where a linear function is inadequate for accurately fitting the data, a nonlinear kernel can be used to transform the data into a higher-dimensional space, where a linear function can better separate the data. minimize 1 2 ||w|| 2 + C N ∑ i=1 (ξ i + ξ * i ) (11) subject to y i -w T φ(x i ) -b ≤ + ξ i (12) w T φ(x i ) + b -y i ≤ + ξ * i ( 13 ) ξ i , ξ * i ≥ 0, i = 1,. , N (14) where w is the weight vector, φ(x i ) is a feature mapping function that maps the input vector x i to a higher-dimensional space, b is the bias term, C is a regularization parameter that controls the trade-off between maximizing the margin and minimizing the training error, ξ i and ξ * i are slack variables that account for prediction errors outside the ε-tube, y i is the target value for the i-th instance, and N is the number of instances in the dataset.
Although not all 400 genotypes were sampled, an analysis of the VIs was performed on the entire orthomosaic. Figure  6  shows the VIs with the most significant differences in their values. The alignment and orthorectification allowed for the VI statuses to be mapped for the different genotypes. Although the entire crop was rice, significant variations in the VIs were observed. These variations were not necessarily due to different biomass and nitrogen values but rather to the phenotypic expressions of the cultivated genotypes. For rice crops of the same genotype, exploring the VI maps can be useful since they can reveal anomalies in the crops. The phenotypic expression should be similar and vary only due to changes in biomass or nitrogen. VI calculations were performed on the segmented image dataset of the 59 sampled plots.
4 to 1. 8, with the outliers at 1. 1 for plot 13. This plot had a fresh-weight value of 940 g, a dry-weight value of 300 g, and a SPAD value of 41. The range for plot 31 was from 0. 9 to 1. 6, with a fresh-weight value of 900 g, a dry-weight value of 280 g, and a SPAD value of 38. 02. This genotype had an inversely proportional relationship with the SPAD values and a directly proportional relationship with the biomass values. The variation between the VIs was significant, as was the variation between the ground-truth values. Figure  9  shows a correlation matrix for the dataset used, where W-BM is the wet biomass in grams, D-BM is the dry biomass, N is the SPAD value, and He is the average height of the plants. The VIs referenced in Table  3 , the multispectral channels, and the GF segmentation values are also included.
It can be observed that the height was highly correlated with the biomass variables, a valuable observation as it is an estimation parameter that does not require destructive methods. As mentioned in Section 2, tests were conducted for five models. Each of the methods was optimized using Bayesian optimization, which can minimize a model's confidence interval by adjusting the hyperparameters. The five models selected for training were evaluated using the four feature matrices. This process was executed using a common machine learning technique known as k-fold cross-validation, specifically with five randomly formed folds. In k-fold cross-validation, the original sample is randomly partitioned into k equal-sized subsamples. Of the k subsamples, a single subsample is retained as the validation data for testing the model, and the remaining k-1 subsamples are used as training data.
In this case, the folds were created from 80% of the total 2360 rice subplots, resulting in 1888 samples. These samples were used to train the models, taking into account the variability of the different datasets. These datasets were distributed in a 70/30 split, where 70% was used for training and 30% for validation. This split helps prevent overfitting, which is a modeling error that occurs when a function is too closely aligned to a limited set of data points. In addition, 472 samples composed of 8 rice subplots of each genotype, were randomly set aside. These were not included in the model training and were reserved for the independent evaluation of the trained models. This is a necessary step to test how well the models generalize to unseen data. W -B M D -B M N I D p l o t I D G e n H e S R N D V I G N D V I C T V I S A V I D V I M S A V I E V I A R V I B L U E G R E E N R E D R E G N I R G F W-BM D-BM N IDplot In Tables  4 5 6 , the values of the selected metrics, R 2 , MAE, and RMSE, are presented.
The following figures show graphs of the estimations for the wet biomass, dry biomass, and nitrogen variables from the learning models with the highest correlation coefficients, with the estimated values in orange and the ground-truth values in blue. In Figure  10 , the estimation for the first variable, wet biomass, is displayed. The results are shown for the training set of 472 images for the ensemble regression, tree regression, and Gaussian process regression estimation models. The graphs present the wet biomass of the samples (Y-axis) versus the samples obtained (X-axis) from each of the characterized images. In general, Gaussian process regression performed the best, obtaining a correlation coefficient of 0. 987. This indicates that this model handles uncertainty much better for smaller datasets.
In Figure  11 , the estimations for the second variable, dry biomass, are displayed. In general, better performance was observed for Gaussian process regression, which obtained a correlation coefficient of 0. 998. This indicates that this model handles uncertainty much better for smaller datasets. In Figure  12 , the estimations for the third variable, SPAD, are displayed. Better performance was observed for Gaussian process regression, which obtained a correlation coefficient of 0. 999. This indicates that this model handles uncertainty much better for smaller datasets.
However, the computational cost of GPR, particularly for large datasets, is a significant drawback. As technological advances facilitate the collection of more and more data through increasingly sophisticated remote sensing technologies, the scalability of models becomes a critical concern. In this respect, the TR and ER methods may have an advantage. The ability of ER and TR to handle complex, nonlinear relationships using decision rules is a significant advantage, considering the complex interactions among environmental factors, plant physiology, and the resulting variables to be estimated (biomass, nitrogen content). However, the careful selection of the kernel function and fine-tuning of hyperparameters can be potential drawbacks, as these require additional computational resources and domain expertise.
Ensemble techniques, which combine multiple base regression models, can potentially offer robustness and improved predictive performance, which is a significant advantage. Additionally, in this study, the estimation models were calibrated and tested by including both time-independent imagery samples and time-dependent vegetation index dynamics throughout each phenological cycle, enabling the characterization of spatio-temporal variations in above-ground biomass and leaf nitrogen. However, these methods can also pose computational challenges, as multiple base models need to be trained and combined. Additionally, the interpretability of these models can be lost or reduced, which could be a disadvantage in scenarios where understanding the model's decision process is crucial. Selecting the most suitable method depends on factors such as the size of the dataset, the complexity of the relationship between the VIs and target variables, and the importance of uncertainty quantification in the predictions.
Through the use of VIs, characteristics can be identified that closely correlate with the results from in-the-field sampling, offering researchers a non-invasive alternative for measuring this parameter and potentially eliminating the use of destructive sampling methods. The correlation between the VIs and the parameters measured in the field will vary according to the genotype and its phenological expression. In this study, the correlation matrix for the dataset revealed strong relationships between the data gathered conventionally in the field and several VIs. In addition, the VIs that correlated the most with the groundtruth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. Moreover, the multispectral channels, specifically REG, NIR, and GFkuts segmentation, showed significant correlations with both wet and dry biomass, with an additional correlation observed with the RGB channels for dry biomass.
Funding: This work was funded by the  OMICAS program : " Optimización Multiescala In-silico de Cultivos Agrícolas Sostenibles (Infraestructura y validación en Arroz y Caña de Azúcar )", anchored at the  Pontificia Universidad Javeriana in Cali  and funded within the  Colombian Scientific Ecosystem  by  The World Bank ; the  Colombian Ministry of Science; Technology, and Innovation ; the  Colombian Ministry of Education ; the  Colombian Ministry of Industry and Tourism ; and  ICETEX  under grant  ID FP44842-217-2018  and  OMICAS  Award  ID 792-61187. Institutional Review Board Statement: Not applicable.
1. For many animals, social relationships are a key determinant of fitness. However, major gaps remain in our understanding of the adaptive function, ontogeny, evolution, and mechanistic underpinnings of social relationships. There is a vast and ever-accumulating amount of social behavioural data on individually recognised animals, an incredible resource to shed light onto the biological basis of social relationships. Yet, the full potential of such data lies in comparative research across taxa with distinct life histories and ecologies. Substantial challenges impede systematic comparisons, one of which is the lack of persistent, accessible, and standardised databases. 2. Here, we advocate for the creation of big-team collaborations and comparative databases to unlock the wealth of behavioural data for research on social relationships by introducing MacaqueNet (https://macaquenet.
, 2016; Schneider et al. , 2019). Comparative studies therefore typically rely on combining independent research on individual populations  (Lukas & Clutton-Brock, 2017). There is a vast and ever-accumulating amount of social behavioural data on individually recognised animals  (Sheldon et al. , 2022). This is an incredible resource for comparative research into the biological basis of social relationships, but it also comes with substantial challenges. The field of animal behaviour historically and currently still consists of many independent research groups collecting and managing data using similar yet slightly different methods (the 'long tail' of data: many small datasets that together represent the vast majority of existing data;  Wallis et al. , 2013). Choices regarding which behaviours to observe, how those behaviours are defined, and which methods are used to record them, result in a wide variation in behavioural data types and definitions.
This significantly complicates the creation of standardised databases on social behaviour, compared to existing databases pooling relatively consistently defined and quantified data such as life history, morphological and ecological traits, population sizes and geographic ranges, and measures of biodiversity (SPI-Birds:  Culina et al. , 2021; The Global Biodiversity Information Facility: Edwards et al. , 2000; panTHERIA: Jones et al. , 2009;  The Primate Life History Database (PLHD):  Strier et al. , 2010). Moreover, most comparative studies in animal behaviour typically consist of one-off comparative research projects, spearheaded by an individual research team, and focused on a specific question. The generated databases are usually not conceived for general use, making them difficult to find or repurpose for future studies  (O'Dea et al.
, 2021). As such, the painstaking effort of searching the literature for suitable data, acquiring such data, and standardising the data and metadata across datasets needs to be repeated for each new comparative study  (Poisot et al. , 2019). A far more sustainable approach is to create enduring and reusable databases, with well-defined protocols for (meta)data standardisation, archiving and accession  (Culina et al. , 2021). Clear and concise guidelines for such databases are provided by the FAIR guiding principles to ensure data are Findable, Accessible, Interoperable and Reusable ('FAIR',  Wilkinson et al. , 2016). The development of such databases has become a priority for many scientific fields and has led to the creation of bigteam science initiatives, bringing together scientists "across labs, institutions, disciplines, cultures, and continents"  (Forscher et al.
, 2022). Although complex, there is great potential in standardising existing data and making them usable for comparative research. The Animal Social Network Repository (ASNR) was the first significant effort to consolidate social behaviour data across different animal species using FAIR principles  (Sah et al. , 2019). Since its publication, the ASNR database has been used for several comparative studies across a broad taxonomic range (e. g. Collier et al. , 2022 and Gagliardi et al. , 2023) , demonstrating the value of such databases for the scientific community. However, the ASNR database's significant diversity also presents challenges for comparative research as it contains social data collected in various ways, with substantial variation in observation effort and capturing fundamentally distinct aspects of sociality.
A second, recently developed database is DomArchive, which focuses on dominance interactions within groups of various taxa  (Strauss et al. , 2022). Social dominance is conceptually similar and measured fairly consistently across taxa  (Strauss et al. , 2022) , and therefore lends itself well to comparative questions. However, a comprehensive understanding of social relationships necessitates data on both affiliative and agonistic social interactions. Both types of interactions play crucial but distinct roles in shaping the dynamics and complexity of social systems. In contrast to dominance interactions, affiliative interactions are typically more varied in terms of both the types of behaviours and methods used to measure them, making it more complex to build a database of truly comparable data  (Ellis et al.
2019). One approach to overcoming this challenge is to collate data for taxa where the observed behaviours and recording protocols are relatively uniform. These databases can then be integrated into an archive of databases, such as the EcoEvo data source catalogue (https://ckan-ecoevo. d4science. org/,  Culina et al. , 2018) , to ultimately create a comprehensive FAIR database of social behaviour. Here, we introduce MacaqueNet (https://macaquenet. github. io/), a global grass-roots collaboration that provides a platform for big team social behavioural research, and the MacaqueNet database, a cross-species collaborative FAIR database, essential for synthetic comparative research  (Borries et al. , 2016; Coles et al. , 2022). To the best of our knowledge, this is the first collaborative database on both affiliative and agonistic social behavioural data in any animal and can thus provide a functional example for future endeavours of this type.
Methods and Results MacaqueNet connects researchers studying the social behaviour of macaques in diverse settings. The aim of MacaqueNet is twofold: to encourage and facilitate collaboration between independent research teams and to provide a lasting central platform for data archiving, standardising, and accession. Here we describe the establishment of MacaqueNet, from starting a large-scale collective to the creation of a cross-species collaborative database and the implementation of data entry and retrieval protocols. First, however, we illustrate the exceptional suitability of macaques as a taxon to initiate the construction of a cross-species database of social behavioural data.
Why the genus Macaca lends itself to the creation of a comparative database of social behaviour With 25 currently recognised extant species, Macaca is the most widely geographically distributed non-human primate genus  (Roos et al. , 2019). Macaques are one of the most versatile and adaptable primates, exploiting very different environments, from the temperate, mountainous habitats of Morocco and Japan to the tropical forests of Southeast Asia  (Thierry, 2007). Throughout this range they live in areas of varying anthropogenic impact, from urban cities to natural habitats coming under increasing tourist and agricultural pressure  (Radhakrishna et al. , 2012). This variation in habitats and climates is reflected in the species' ecology, mating system and social structure  (Cords, 2012). All macaques are frugivorous, but their diet also includes other plant materials, including leaves, seeds and flowers, as well as small vertebrate and invertebrate animals.
Creating MacaqueNet MacaqueNet originated in 2017 as a small-scale collaboration proposal for one specific study. From there, MacaqueNet developed and expanded to become a global grass-roots network of macaque researchers, with further potential collaborators being identified through word of mouth and a literature search on Google Scholar. We aimed to mitigate the inherent bias towards researchers from Europe and the US, and to facilitate the involvement of scientists from macaque-range nations and historically underrepresented research communities. An initial email was sent to all first and last authors of papers that contained or pointed to the existence of social behavioural data on macaques, asking if they would be willing to contribute those data to a collaborative database. Of the 193 researchers contacted, 68 did not reply or declined, 28 had no suitable data and 97 agreed to participate.
The MacaqueNet Relational Database We brought together social behavioural data from independent research teams into a standardised cross-species database. The MacaqueNet database currently contains social interaction data from 1981 to the present for 14 of the 25 recognised species of macaques (Fig  1 ). At the time of publication, the data have been collected on 21 wild, 22 captive, and 18 free-ranging populations (some of which have been observed for several study periods) at a total of 61 field sites, zoos, and research centres (Fig 2 ), resulting in social behavioural data on 3972 individual macaques (Fig  1 ). The full extent of the data available can be explored using the search tool on the MacaqueNet website: https://macaquenet. github. io/database/. The MacaqueNet database currently contains data for 14 out of 25 recognized species.
Each dot represents the density for one (affiliative or agonistic) socio-metric matrix. In addition to the behavioural data, the database includes rich metadata on methodology, study populations and research teams. To ensure that the data and metadata are comparable across studies, we set up a standardisation pipeline through which all contributed data ('primary data') is run before it enters the database. Fig S2 in the supplementary material provides an overview of the workflow from primary data submission, over the data checks and standardisation pipeline to data requests. Both the primary data and the standardised database are currently stored and managed in a GitHub repository (https://github. com/MacaqueNet). The code for the data standardisation pipeline and the metadata on the available datasets, along with resources such as the Glossary, the workflow and the MacaqueNet Terms of Use, are openly accessible in the repository.
MacaqueNet is both a big-team science resource and a community MacaqueNet makes macaque data FAIR (Findable, Accessible, Interoperable and Reusable,  Wilkinson et al. , 2016) , in four main ways. First, by identifying, accessing, and bringing together data from multiple sources into one centralised place (https://macaquenet. github. io/database/). Second, by developing a common vocabulary for the data and metadata in the MacaqueNet database, with openly accessible definitions (see Glossary in the supplementary material,  Borries et al. , 2016). Third, by standardising data across studies and research sites together with the data contributors who understand the intricacies and subtleties of the data  (Schneider et al. , 2019). Fourth, by providing transparent and openly available annotation of how primary data were processed, so that the original data can always be recovered and processed in different ways if future researchers want to make other decisions  (Schneider et al.