dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding The salary of Marjolein Heymans and Sara Wellens was funded by the European Commission within the framework of Marie Sklodowska-Curie Innovative Training Networks: BtRAIN (H2020-MSCA-ITN-2015, no 675619) and in3 (H2020-MSCA-ITN-2016, no 721975). CRediT authorship contribution statementElena Reale: Code, validation, formal analysis, Writing -Original draft; Jenny Sandström: Investigation, writingreview & editing; Maxime Culot: Methodology, investigation, writingreview & editing; Julie Hechon: Investigation, writingreview & editing; Sara Wellens: Investigation, writingreview & editing; Marjolein Heymans: Investigation, writingreview & editing; Florianne Tschudi-Monnet: Resources, supervision, writingreview & editing; David Vernez: Conceptualization, methodology; Nancy B. Hopf: Conceptualisation, methodology, formal analysis, writingreview & editing.
Estimating the welfare status at an individual level on the farm is a current issue to improve livestock animal monitoring. New technologies showed opportunities to analyze livestock behavior with machine learning and sensors. The aim of the study was to estimate some components of the welfare status of gestating sows based on machine learning methods and behavioral data. The dataset used was a combination of individual and group measures of behavior (activity, social and feeding behaviors). A clustering method was used to estimate the welfare status of 69 sows (housed in four groups) during different periods (sum of 2 days per week) of gestation (between 6 and 10 periods, depending on the group). Three clusters were identified and labelled (scapegoat, gentle and aggressive). Environmental conditions and the sows' health influenced the proportion of sows in each cluster, contrary to the characteristics of the sow (age, body weight or body condition).
1 ). The clustering results suggest grouping the gestating sows with similar behaviors into 3 subgroups (called clusters). This clustering task was performed on a dataset with a total of 388 individual observations from 69 sows, i. e. , one sow per period (sum of 2 days per week: Tuesday and Wednesday) on a total of 6 to 10 periods per sows (depending on the group, Fig. 1 ). These periods corresponded to control periods (i. e. , baseline behavior) or event periods with induced perturbations of the environment during a few days (3-5 days)  in the week to induce changes in behaviors and welfare status. The idea of these perturbations was to increase the behavior variability between sows. The method is also composed, during the offline learning process, of the exploration of the behavioral dataset, which aims to produce an interpretable model, i.
e. , a decision tree. For online forecasting, given some new data on a sow (feeder and automatic video analysis data), the already learned decision tree classifies the sow into one of the welfare groups. For the offline learning process, the relation of the clustering results with welfare status was interpreted using individual characteristics of the sows (age, body weight, back fat thickness, health status) and the experimental setup (control vs. events). This clustering step enables data annotation, paving the way for the application of a supervised and interpretable machine learning algorithm. Once the data were annotated with the cluster labels, a decision tree was learned on sensor data, feeder and automatic video analysis. The performance of this classification was evaluated using the labels obtained by the clustering step.
It's a way of classifying sows into different clusters using other data, i. e. automatically recorded data (feeder and cameras). For the online forecasting process, the decision tree is used for inference on new live data from the sows (feeder and automatic video analysis data) and provides the predicted welfare class label of the sow. One of the major interests of the decision tree is its interpretability, which makes it a valuable tool for understanding the algorithm decision rules behind predictions. A total of 69 crossbred sows (Landrace × Large White), housed in four gestating rooms with one pen inside (groups A, B, C and D), were studied throughout their gestation. The parity of the sows ranged from 1 to 10, included 16 primiparous (4 per group). All sows were confirmed to be pregnant (by ultrasound) 30 days after insemination.
Data collection Every Monday, animal-based measures (number of skin lesions, cleanliness of the sow, and identification of potential health problems) were evaluated based on the Welfare Quality ® assessment 3 with the same trained observer. A sow was categorized as "unhealthy" if, during the measure, a health problem was detected (bursitis, lameness, tail biting, vulvar lesion or abscesses); otherwise, the sow was categorized as "healthy". The electronic feeders automatically recorded every visit of each sow (time of the day, duration of the visit, feed intake). Extracted data were preprocessed to filter the outliers (i. e. , visit duration over 6 h, 0. 007% of all the dataset) and aggregated at an individual scale for the 7 variables described in Table  1. The feeder order, i. e. the order in which the sows had access to the feeder, was also recorded and represented a proxy of the hierarchical order  31  (i.
e. , rank 1 for the most dominant sows). Manual analysis of videos was carried out in continuous by trained observers to monitor individual behavior (social and activity) for two moments (23:00 h-04:00 h and 13:30 h-18:30 h) of 2 days per period. The detailed ethogram is available in Durand et al. 28  , and raw data are available in a datapaper  32. From the video analysis, 3 indexes were calculated to obtain a limited number of variables gathering the intensity and the valence of the level of activity and social interactions (Table  1 ). For example, when the "Index_activity" has a negative value, the sow spent more time passive than active, and when the "Index_giving" has a negative value, the sow gave more negative interactions than positive ones. An automatic analysis of videos was also achieved at a group level using a convolutional neural network algorithm (Dilepix, France)  33.
Then, the algorithm iteratively assigns data points to one of the clusters based on a similarity or distance measure and updates the cluster centers until convergence. When applying a clustering algorithm, the parametrization requires selecting appropriate values for various parameters that affect the clustering approach. These parameters may include the number of clusters to generate, the distance metric used and the convergence criteria for stopping the iterative process. Choosing optimal values for these parameters is crucial because it impacts the quality of the clustering results  34. K-medoid clustering was performed on the dataset composed of three variables extracted from the manual analysis video ('ratio_activity' , 'ratio_receiving' , 'ratio_giving') and normalized before clustering (values between -1 and 1).
Decision tree, supervised classification method based on clustering label The supervised classification method chosen was a decision tree applied to the data labeled by the classes issued from the previous clustering step. A decision tree is a popular machine learning algorithm that learns from input data and uses a tree-like structure to make decisions or predictions. In a decision tree, each internal node represents a combination of feature values, and each branch corresponds to a possible value or outcome. Decision trees are commonly used for classification tasks and provide interpretability and ease of understanding due to their graphical representation  36. The dataset composed of feeder data and automatic video analysis data was randomly split into a training dataset (70% of the original dataset) and a test dataset (30%), with care taken to ensure that there were a sufficient number of observations for each cluster.
Statistical analyses and implementation of algorithms The effect of factors (week, group, event) on the clusters was evaluated using Cochran's Q test, which was designed for paired data with more than two groups for comparison. Friedman tests and post hoc tests were used to assess the effects of continuous data (body weight, age, backfat thickness) on the clusters. The threshold for statistical significance was set at P < 0. 05 and trend-level significance between 0. 05 < P < 0. 10. The implementation was realized in Python using the scikit-learn library  35  (version 1. 2. 1. ) for the clustering (K-medoid) and the classification (decision tree) tasks. Statistical analyses were performed using the Python 'statsmodels' , 'scipy' and 'scikit_posthocs' libraries.
Automatic classification of welfare status The decision tree classification results with the feeder and the automatic video analysis data had accuracies of 80% and 72% (for the training and testing datasets respectively, using the labels from the clustering step) and F1-scores of 0. 80 and 0. 72. The performance results by clusters showed better results to predict 'gentle' and 'aggressive' sows (F1-score = 0. 86 and 0. 80, respectively) than 'scapegoat' (F1-score = 0. 70). The decision tree with the feeder data only had accuracies of 67% and 64% for the training and testing datasets, respectively, and F1-scores of 0. 61 and 0. 56. The performances to predict 'scapegoat' were better with feeder data only than with feeder and automatic video analysis data together, while this combination of measurements gave higher performances for 'aggressive' and 'gentle' sows (Table  3 ).
However, one of the drawbacks of data mining for cluster interpretation is that the clusters generated may not always have straightforward and easily interpretable meanings. To validate this new and upgradeable first approach, its application on additional datasets will be necessary to improve the studied link between welfare and behavior. To establish a more robust method, it should be testing with respect to other identified welfare indicators in the literature, such as stereotypies (abnormal behaviors linked to boredom)  21  , which can also be measured through video analysis. The three clusters identified in this study demonstrated a correlation with health status, particularly for 'aggressive' behavior. Animals' health status was considered a part of welfare status  39  , and behavioral changes could be potential signs of illness  40.
The questionnaire was distributed in 23 Frenchspeaking groups from February 9, 2021, to March 14, 2021, which allowed us to collect a total of 3217 responses. The dataset was deposited in the Mendeley repository (https://data. mende ley. com/ datas ets/ht5p5 pg7b7/ 1; DOI: 10. 17632/ht5p5pg7b7. 1).
| Factor analysis of personality structure To ensure that the personality traits of the cats were reliably described by the owners, we removed from the analyses the surveys indicating that the owners did not spend any time with their cat (n = 10) and those with comments that prevented their use to study the personality traits (e. g. , respondent not the cat owner, recently adopted cat, cat with a major health issue; n = 21). Like all animal species, cats go through different stages of development, and in juveniles, personality and predatory behavior are not yet stable  (Lowe & Bradshaw, 2001). Following  Litchfield et al. (2017) , we conservatively excluded cats aged under 1 year (n = 614) from the dataset. Finally, the responses with missing data in the set of personality adjectives were also removed (n = 64).
The final dataset included 2508 responses. We performed exploratory factor analysis on the personality variables (15 adjectives evaluated by the owners) to first determine the number of personality traits to be extracted and then estimate the values of each trait for each cat  (Hutcheson & Sofroniou, 1999). We initially ensured that the data were suitable for factor analysis using Bartlett's test of sphericity, which was significant (p <. 01), and the Kaiser-Meyer-Olkin (KMO) criterion, which had an overall value of 0. 722 (depending on the authors of the statistical tests, a value above 0. 5 or 0. 6 is considered to mean that the sampling is adequate). Both indicators thus show that the data were suitable for factor analysis  (Hutcheson & Sofroniou, 1999; Kaiser, 1974). To determine the number of factors -here, personality traits -to extract, we used the empirical Kaiser criterion (see  Braeken & Van Assen, 2017  for details) and parallel analysis with principal component analysis (PCA) (comparing the eigenvalues obtained to those generated from a Monte-Carlo simulated matrix), which indicated that four factors should be retained.
| Breed and personality We tested the link between breed and personality traits by computing the Euclidian distance between personality profiles for each pair of cats to produce a resemblance matrix from which we conducted a nonparametric (permutational) analysis of variance (permanova; package vegan;  Oksanen et al. , 2007)  using 999 permutations to test whether personality profiles differed according to breed. We then performed discriminant analysis using a non-parametric version of Pillai's test to evaluate the significance of the eigenvalues (package ade4;  Dray & Dufour, 2007). To ensure to have enough statistical power, we removed five breeds with <60 individuals (Chartreux, n = 28; Savanah, n = 20; Sphinx, n = 57, Siamese, n = 49, Turkish Angora, n = 21), leading to a dataset of 2162 responses.
| Factors influencing owner-reported frequency of prey brought home To run the subsequent analyses regarding predation, additional responses were excluded from the previously described dataset: cats living in apartments with minimal outdoor access as well as cats living in houses but without daily outdoor access (n = 1217), owners of four or more cats  (Cordonnier et al. , 2022)  who would supposedly have difficulty determining which cat brought home which prey (n = 188), incomplete responses (n = 2), and cats belonging to the Bengal breed (n = 36) because when the survey was posted on a Bengal cat Facebook group, several people suggested in the comments that participants give false answers to questions relating to predation. The final dataset included 719 responses. Since the response variables for reported predation frequencies were ordered (0: never, 1: 1-5 times a year, 2: 5-10 times a year, 3: 1-3 times a month; 4: once a week or more), two cumulative logit models (CLMs) were adjusted  (McCullagh, 1980) , with the reported frequencies of predation taken as response variables: CLM1: birds, CLM2: mammals.
For both models, we used a stepwise selection by sequential replacement to identify the subset of variables in the dataset resulting in the best performing model with the lowest prediction error  (Hegyi & Laczi, 2015; Venables & Ripley, 2013). Wald tests were performed on the predictor variables. The quality of the model estimates was monitored using Pearson residuals (package sure;  Greenwell et al. , 2018). For the qualitative variables, post hoc tests (including a Holm correction) were performed using a self-designed contrast matrix (package lsmeans;  Lenth & Hervé, 2015).
The impulsiveness factor was expected to emerge from the adjectives impulsive, predictable (negative loading), and distractible. However, in our dataset, impulsive was strongly loaded on the dominance factor, whereas predictable and distractible were moderately loaded on the agreeableness factor (Table  1a ). Litchfield et al. (2017)  analyzed survey data from New Zealand and Australian owners. When the two data sets were examined separately in the initial analysis, the scree plot of both datasets supported retaining only four factors as in the case of our dataset. Furthermore, in this initial separate analysis of the two data sets, the impulsive adjective was strongly and positively loaded on the impulsivity factor in the Australian data set but negatively in the New Zealand data set. It is therefore possible that cat impulsivity is perceived differently in different countries.
, , 2022)). Here we compiled sources related to specific ethical considerations and welfare regulations pertaining to biomedical (neuroscientific) research with NHPs in multiple countries worldwide. We also propose key ingredients to build a collaborative framework that can offer solutions and information to support global collaboration in research involving NHP models, considering the variability in local, national, and international regulation. This resource initially focuses on macaques and marmosets, as the more common laboratory NHP models. For 13 countries national information is aggregated into a tabulated resource and maintained online (see PRIMatE Resource Exchange, https:// prime-re. github. io/; Messinger et al. , 2021). This resource is intended to be regularly updated by the research community to reflect changes in animal research regulation.
International resource on non-human primate neuroscience regulation One major aim of this paper is the aggregation and sharing of animal welfare and regulatory guidelines. A comparative assessment of current national regulations is depicted in Fig. 1. This information is summarized in Tables  1  and 2. For an online version of the tables that will be updated visit: https://prime-re. github. io/hardware_and_protocols/glo bal_collab. html). One consideration to note when examining these individual nations' regulations is that although many European countries are bound to the EU Directive 2010/63/EU or, in North America, follow the guidelines set by the United States of America (USA) Office of Laboratory Animal Welfare (OLAW; the enforcing body for the federal research agency, National Institutes of Health (NIH); https://olaw.
In contrast, for China, Japan, Mexico and the USA, animals can be re-used on successive research projects, although the re-use of an animal must be scientifically justified and authorized (see Tables  1 2 ; https://prime-re. github. io/hardware_a nd_protocols/global_collab. html). Another similarity noted across countries is a minimum weaning age for macaques and marmosets (Table  2  Sections F-G). Further, in all countries surveyed, except in India and the USA, the use of wild-caught NHPs for neuroscience research purposes is banned. Although the use of wild-caught NHPs in neuroscience research is uncommon at academic institutions in the USA, concerns regarding illegal trade (www. nytimes. com/2022/11/16/us/cambodia-monkey-smuggling-ring. html) highlight at least two important considerations.
To navigate this space with regard to international collaborations, being aware of the common understanding and where it diverges requires sharing of information. The capacity for finding common ground is contingent upon the resources and availability of an accessible database. In addition to digital data (e. g. , neuroimaging scans), NHP scientists may share and therefore send abroad tissue samples that would be best analyzed in a collaborating laboratory. The shipment of tissue samples from NHPs is regulated by the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES; cites. org/eng/disc/text. php). It was created originally to prevent fraudulent and illegal movements of endangered species trophies. The CITES offers museums (e. g. , natural history museums) the opportunity to share specimens based on their recognized CITES accreditation, with limited administrative steps to make an exchange possible.
Cooperate with scientists on a global scale Scientists recognize the importance of working with colleagues across labs and institutions, and across nations to avoid redundancy and share expertise for added value. An exciting aspect of cooperative scientific research is the potential for neuroscientists across the globe to actively participate and share their expertise, while also learning from others. Some great examples of such endeavors are Brainhacks  (Craddock et al. , 2016; Gau et al. , 2021) , Brainweb (https://brain-web. github. io/), and NeuroMatch  (van Viegen et al. , 2020). Global collaborations incorporating NHP neuroscience should support scientific discovery and ensure the best possible animal welfare approaches are obtained -these dual objectives can complement each other. With global collaboration, cooperation is required between teams and institutions to achieve trans-national agreements.
Share and contribute to information resources The accessibility and willingness to cooperate in the sharing of information is a recurring theme in considering (inter)national collaborations and regulations. Not many countries can perform the full spectrum of possible experiments and collect neuroscience data. Thus, it is important to maximize the use of accumulated data worldwide. Recently, we have seen the fast development of open databases for sharing animal data, and experienced the benefits of rapid data sharing during the Covid-19 pandemic caused by the SARS-CoV-2 virus  (Weir et al. , 2022). The SARS-CoV-2 genome was made available across several platforms (e. g. , GISAID, gisaid. org; INSDC, insdc. org) and updated from all around the world in real-time, answering the call for data re-distribution and openness (Van Noorden, 2021).
There are many advantages gained from open database promotion. Data sharing has been promoted by the growth in open access models for scientific publication and information dissemination  (European Commission, 2017; Van Noorden, 2021; Weir et al. , 2022). In recent years, the NHP neuroscience community has started to move towards the sharing of data and resources  (Milham et al. , 2020 (Milham et al. , , 2022)). The PRIME-RE (https://prime-re. github. io) platform was built around this philosophy  (Messinger et al. , 2021). Such collaborations have been shown to lead to further sharing and improvement, and, where relevant and possible, standardization of underlying experimental and welfare protocols  (Basso et al. , 2021; Milham et al. , 2020). Further, hematological, physiological, and behavioral data sharing is essential to better inform long-term health and wellbeing assessments of NHPs involved in neuroscience experiments (e.
g. Wegener et al. , 2021). Certainly, when researchers share experimental data, they effectively increase the sample size of their data, which, in the case of NHP neuroimaging studies, will mitigate some issues of rigor, reproducibility and validity  (Botvinik-Nezer et al. , 2020). However, sharing data alone does not necessarily imply that an increase in the number of animal subjects is the only way to enlarge the available dataset, as considerations of reproducibility can also include increasing the number of experimental sessions. For example, in neurophysiological studies with NHPs, two animals are commonly the smallest number that can be studied. For NHP work, this is justified because the sample size can include the number of data points collected (e. g. , from a recording site) across multiple sessions.
Currently, there are several identified reasons common to scientific groups that stop development of openness and sharing of resources through common databases. Core barriers to data sharing include: the lack of any standardized procedure for appropriate attribution, which can be detrimental to the career prospects of (junior) researchers who collected the precious data; competitive pressure amongst scientists and between nations; the necessity to continually secure further funding; and restrictions on dataset sharing by research organizations and societies  (Matthews et al. , 2020). The true challenge will be to establish an international culture of openness and collaborative science that will allow such shared resources to thrive and, in return, benefit the animals, the research community, and the broader public as a whole.