dataesr/scientific-paragraphs-categorization huggingface dataset:
Half-time calves did not have access to an alternative milk source while the cows were away from the pen. Calves were observed at age of 22 (SD = 7) d (range from 3 to 36 d; wk 3) and 50 (SD = 8) d (range from 27 to 66 d; wk 7) of age. Two calves were randomly selected per pen. In 5 pens (2 full-time and 3 half-time pens) both a bull and a heifer were observed, in 4 pens both observed calves were heifers (3 full-time and 1 half-time pens), and in 3 pens both observed calves were bulls (1 full-time and 2 half-time pens). In total, 4 bulls and 8 heifers were observed in the full-time treatment, and 7 bulls and 5 heifers were observed in the half-time treatment. The videos were recorded by cameras placed above each pen and stored on hard drives. The behavior of the calves was recorded continuously during 24 h (starting at midnight) by focal animal sampling  (Bateson and Martin, 2021)  by one observer (the first author) using the Boris software  (Friard and Gamba, 2016).
Finally, the exact times of departure from and return to the experimental pens of the cows were noted for each block and day, as well as the duration of any human presence in the pen. All recordings were done by the same observer, and to test the intraobserver reliability the first video was watched a second time after all data were collected, and the 2 observations were compared using Cohen's kappa (κ = 0. 932). Analysis was conducted in SAS (PROC MIXED; SAS version 9. 4, SAS Institute Inc. ). Figures were produced using the package ggplot2  (Wickham, 2011). In wk 7, 3 heifer calves and their dam had to be removed from the experiment for health issues (blocks 3, 4, and 6), and thus there were only 9 full-time calves observed in wk 7. For each calf and observation day, the duration in seconds was calculated for the following variables: (1) individual locomotor play, (2) parallel locomotor play, (3) frontal pushing with another calf), (4) head butting another calf, (5) head butting the dam, and (6) object play.
Statistical analysis The statistical analysis was performed using R software (R Development Core Team 2008). Basal data were examined for normality with the distribution and using QQ plots. The Wilcoxon signed ranks test was used to compare values not normally distributed. Samples obtained at rest were compared to basal T0 samples before IR or BR sessions, when available. This was done to assess the stress levels of horses prior to riding and evaluate if sessions were then going to be comparable. If a significant difference was present, the variation between T1 or T2 values and T0 values were compared between IR and BR groups. If no difference was found, T1 or T2 values were directly compared between IR and BR groups. The investigation of a potential linear correlation between the biological stress markers and the stress score was performed graphically separately for each session, and then using Spearman's correlation test.
Acknowledgements. The  Lions Club Nîmes Maison Carrée (France ) represented by  Doctor Jean Delate , and  Zoetis  supported financially the project and helped to buy the ECG phone case, the Salivette swabs and cortisol ELISA kit for salivary analyses. The biochemistry department of  VetAgro Sup Campus Vétérinaire de Lyon (France)  kindly performed the blood analyses free of charge. The authors would like to thank  Marie-Laure Delignette-Muller  for helping with the initial statistical analysis,  Marie-Christine Carlier  from  Université Lyon 1 (France ) for her advice on processing the samples, and also  Edwige Rousseliere  and  Benoît Rannou  from the  VetAgro Sup -Campus Vétérinaire de Lyon (France) biochemistry laboratory  for their help with the biochemistry analyses. A warm thank you to  Sara Marcelin ,  Gérard Violland  and the equestrian centre  Les Cavaliers du Bordelan (France)  for welcoming us and leading the riding sessions.
Ultrasonography Ultrasound of the uterus and ovaries was performed with a SCIL GE Healthcare Logiq Fex machine using a convex transducer of 10 MHz frequency. At Visit 1 the status of the uterus and ovaries before reproduction was assessed for the presence of ovarian cysts, CEH, mucometra and any other abnormalities. Structures were measured when this was possible and there was adequate definition. Normality of dimensions was judged with reference to published data showing that healthy non-pregnant queens have ovary lengths of.
Structured questionnaires As part of the CORE Organic Plus project "PrOPara" (CORE Organic is the acronym for "Coordination of European Transnational Research in Organic Food and Farming Systems https://projects. au. dk/ coreorganicplus/research-projects/propara), a structured questionnaire was developed to collect cross-sectional data on organic dairy goat farming in CH: Switzerland, FR: France and NL: Netherlands and organic meat sheep farming in CH: Switzerland, LT: Lithuania and UK: United Kingdom. The questionnaire included farm characteristics, the use of information sources to stay updated on the parasite challenge, indicators to monitor GIN infection on farm, anthelmintic treatment strategies and the presence of AR, grazing and pasture management routines, the use of alternative GIN control measures, farmer attitudes to six statements on the future of GIN control (measured on a 5-point Likert response scale, from strongly agree to strongly disagree) (Supplementary material 1).
Although data heterogeneity was quite well covered in UK, questionnaire responses resulted in oversampling in CH and LT (more observations compared to true population of farms compared to the UK) and a relatively small sample number for France. Since the responses per country received were not directly indicative of the total number of sheep or goat farmers per country, sampling weights were applied to the data used in the regression analysis, to minimise any disproportionate bias of the smaller and compared to UK rather oversampled countries. The inverse probability of a farmer response in a given country was used to address these shortcomings inherent in the sampling approach and more adequately reflect the importance of individual sampling units  (Scheaffer et al. , 2012). The weights were calculated by dividing the number of responses per country with the number of sheep farms in that country and then inverting the value.
Statistical analysis Graphs were executed using GraphPad Prism version for Windows, GraphPad Software, San Diego, California, USA. Statistical analyses were conducted using R  [version 4. 1. 1 (2021-08-10) ] and RStudio version 1. 4. 1106 (RStudio Team, 2021). Mean EPN concentrations were compared for different treatment regimens within farms using a non-parametric Wilcoxon test.
In 1999, selected strains of mice were tested under standardized conditions (same test equipment, protocols, and environmental variables) in three different laboratories. The results showed that the behavior of the mouse strains depended on the laboratory. The authors warn that experiments performed to characterize mouse strains may be laboratory dependent and therefore less strain specific  [4]. To date, the reproducibility crisis remains unresolved and various approaches are discussed and pursued to improve the reliability of scientific data  [4, 7, [20] [21] [22] [23] [24]. One approach that can be used to minimize variability between laboratories is to monitor the animals in their familiar environment (i. e. , in their home cage). Krackow and colleagues were able to show that both the activity and spatial learning of mice tested in the home cage was constant across different laboratories  [25].
Increasing number of publications on HCM The earliest publication fulfilling our criteria for home cage monitoring (Additional file 1) that we could find was published in 1974. In 2018, 2019 and 2020, 29, 27 and 26 publications were identified, respectively. The yearly number of publications in which mice and/or rats were monitored in their home cages according to our eligibility criteria increased (Fig. 2A ). In 1987, no publication meeting the inclusion criteria was found. In 2021, only one study fulfilled the eligibility criteria, but since the literature search was carried out in February 2021 the databases did not cover the entire year. When comparing the number of HCM publications with the overall number of studies in mice or rats listed in PubMed (search string: "mice"[MeSH Terms] OR "mice"[All Fields] OR "rats"[MeSH Terms] OR "rats"[All Fields]; filter: other animals), the percentage of relevant HCM references increased over time (Fig.
A large range of manual and automatic HCM techniques can be applied for collecting data from group-housed animals In most studies, one HCM technique was used (n = 362). The second most frequently reported option was the combination of two techniques (n = 118). A combination of three (n = 35) or more (n = 6) techniques was less often applied. Table  4  shows that in a group housing setting, more techniques were applied to measure data from individuals than from groups of animals. Approximately as many manual as automatic HCM techniques were used to generate data from individual subjects. Data from groups of animals were rather collected by manual techniques. The techniques listed in Table  5  were used in grouphoused animals and almost all of them allowed for generating data for individual animals.
It may be hypothesized that "simple" parameters are investigated using commercial HCM systems, but custom-built systems are required for more complex parameters. However, the HCM systems were not assigned to the parameters investigated in the present systematic review, and therefore the results do not provide this information. In relation to the total number of PubMed-listed studies including mice or rats, the percentage of HCM (See figure on next page. ) Fig. 8  Historical change in monitoring of A behavioral parameters, B physiological parameters, and C external appearance-related parameters in mice and rats in their home cages (data obtained from n = 520 publications). More than one application (i. e. , parameter) could be indicated in a publication. If the same parameter was investigated using different techniques in a study, the parameter was counted only once for this study.
Since the animals usually are handled when cages are changed, the effects listed above may not only be due to a novel environment but can also be associated with the handling techniques used  [18, 91]. Since habituation times are rarely reported, we refrained from extracting the time the animals were allowed to acclimatize to the respective HCM. Instead, data on the duration of measurement using the HCM techniques were collected. HCM techniques were mainly applied for short-term periods (i. e. , 1-28 days). The number of techniques being applied for intermediate time periods (i. e. , 4-12 weeks) slightly increased over time in the years between 2011 and 2020; however, 2-7 days remained the most frequent duration of measurement. Only in rare cases, HCM techniques were applied for long-term periods (i.
Search strategy and screening Primary databases were searched through PubMed and Web of Science on February 24, 2021. PubMed search terms were refined using the search refiner tool QueryVis  [110]. The following search strings were used for obtaining relevant studies. PubMed: (("mice"[MeSH Terms] OR mice OR mouse) OR ("rats"[MeSH Terms] OR rat OR rats)) AND ((home cage) OR (housing cage)) AND (monitoring OR ("observation"[MeSH Terms] OR observation)). The filter "other animals" was applied. Web of Science: ALL FIELDS: (((mice OR mouse) OR (rats OR rat)) AND (("home cage") OR ("housing cage")) AND (monitoring OR observation)). The data extraction template can be found in Additional file 6. Further detailed information on the objective and materials and methods is provided in Additional file 1.
In brief, only primary studies (published in English language) in which behavioral, physiological, and/ or external appearance-related parameters were monitored in mice and/or rats in their familiar social structure in their home cage or a testing apparatus connected to the home cage were included. The definition of a home cage can be found in Additional file 1. Studies in which no other parameters than calorimetric measurements were examined in a home cage were excluded. In phase 1, titles and abstracts were screened. Thereafter, full texts were screened (phase 2) and data were extracted (phase 3) simultaneously. In all phases, all papers were screened by two independent reviewers and discrepancies were resolved by a third reviewer.
Analysis The responses for the mouse/rat strain were reviewed by two persons: In mice, the 129 substrains were subsumed under "129, " C57BL/6 J and C57BL/6N under "C57BL/6, " and ICR and CD-1 under "CD-1. " In rats, Holtzman and Sprague Dawley were subsumed under "Sprague Dawley. " For genetically modified animals, the background strain was extracted. If strains were of a mixed genetic background, "mixed background" was indicated. If responses appeared not to be plausible or free text-responses had to be clustered, the answers were corrected and the corrected responses were used for further analysis. All original and corrected responses can be found in Additional file 7. SPSS (IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27. 0. Armonk, NY: IBM Corp) was used for the linear regression analysis and for creating the figures.
Introduction Molecular informatics, which incorporates technologies from both chemistry (chemoinformatics) and biology (bioinformatics), has become an advanced tool and a key component in modern structure-based design of new chemical entities. Chemo-and bio-informatics bring together technologies to capture information from chemical structure, molecular and physicochemical properties, as well as biological activities and effects. Specifically, chemoinformatics encompasses techniques to develop databases of chemical structures with associated tools to mine these data and develop predictive modeling strategies. As such, chemoinformatics has become integral in many industrial sectors to leverage chemistry knowledge to make new discoveries. Many databases of chemical structures are linked to biological data which have been measured in a multitude of assays ranging from omics techniques, in vitro tests, in vivo responses.
The power of informatics approaches is to capture, harness and interrogate the information to learn new knowledge and assist in decision making using that knowledge  [1] , then incorporating the new knowledge back into the system beyond the initial cycle. Whilst originally focused on drug discovery with the aim of identifying and optimizing pharmacological activity, many computational tools based on molecular informatics have also been applied to safety assessment of chemicals. These tools utilize the strengths of molecular informatics, which enable efficient use of resources to search complex databases, support making inferences and draw conclusions, identify potential hazards, and support safety assessments. Informatics tools find particular use in product development, but also in regulatory submissions across numerous diverse sectors.
1. As illustrated in Fig. 1 , the workflows adopted by domain experts (SEURAT-1 workflow  [12] ) are mapped to the functional workflows of a platform (digitalized software workflow). Whilst current informatics systems focus predominantly on chemistry, there are opportunities to extend functionality with regard to the needs of the NGRA (and other) safety assessment workflows. There are also other opportunities for further developing a molecular informatics platform in the current age of sophisticated artificial intelligence (AI). The workflow for NGRA should be equipped with data and functionalities of both chemistry and biology, not only to provide data and knowledge, but also to ultimately assist or facilitate timely decisionmaking in both domains within a single framework. There should be a seamless conduit to integrate and federate both chemical and biological domains as the core resources of the system that should include the following components: • Compound safety data, study data (chemical-specific and chemicalagnostic biology), alerts/profilers/models, underlying rules (meta data) • Safety data: human-based limit values for a variety of product use types involved in regulatory offices and authorities • Toxicity datain vitro, historical in vivo data from animals, toxicogenomics/pharmacology • Metabolism datain vitro (human/animal), historical in vivo data from animals, human data • Biology datatranscriptomics, pathways • Prediction models to fill data gapse.
ChemTunes•ToxGPS® platform An NGRA workflow requires a large number of functionalities and diverse resources accessible via complex integration and federation of tools. For this reason, the desired system was built as an extension to the existing ChemTunes•ToxGPS® platform since the existing platform already met many of the pre-requisites. The NGRA process has been captured via contextual interviews with users and this feedback was then transformed into user requirements, which were used to derive functionalities to design the workflows. As illustrated in Fig. 1 , the workflows adopted by domain experts are mapped to the functional workflows of a platform system (digitalized software workflow). The ChemTunes•ToxGPS® platform uses a 3-tier architecture namely chemoinformatics backend, middle layer for communication, and the frontend web application (Fig.
2 ). The three key application components of this platform are database (ChemTunes), in silico workflows for predictions and assessments (ToxGPS), and computational solutions (Express). A high level architectural diagram is illustrated in Fig. 2. The ChemTunes database is implemented in the PostgreSQL version 13. 4 [14] object-relational database system with RDKit-2021. 03. 5  [15]. The PostgreSQL system combined with the RDKit chemistry cartridge facilitates structure searching capability based on a set of molecular topological fingerprints. In addition to RDKit, ChemTunes•ToxGPS® also employs the internally developed MOSES  [16]  and CORINA  [17]  chemistry libraries developed at MN-AM. They are the essential underlying foundation for calculation of physico-chemical properties and molecular descriptors in ToxGPS workflows.
ChemTunes chemical database Descriptions of fundamental database architectures have been published previously  [18]. Highly curated chemistry content in terms of structures and compound information is available for users of the platform. Each chemical record represents a test substance and is linked to information containing regulatory and biological endpoints including safety assessment, toxicity, bioavailability, and metabolic studies. Sophisticated searching functionalities enable flexible and highly customized searches against the various information types. Since ChemTunes is a chemistry-centered database, a robust compound registry system is required. Compound Registry: The ChemTunes chemistry database follows the COSMOS (CMS) registry standard  [18]  and captures rigorous annotations related to structures, compounds, and test articles through a systematic quality control/quality assurance process.
The registry system allows both automatic searching and duplicate detection with a systematic quality scoring system to map many sources, as well as a manual expert-mode review of the underlying chemistry database. Chemical structure attributes such as stereochemistry, double bond geometry, material types, and composition types are annotated in detail along with the names and identifiers. Data sources include United States Environmental Protection Agency (US EPA), United States Food and Drug Administration (US FDA), European Chemicals Agency (ECHA)  [19, 20] , European Food Safety Authority (EFSA) OpenFoodTox  [21, 22] , European Commission cosmetic ingredient database  [23] , PubChem Project, ChemSpider, and CAS Common Chemistry  [24]. Over 500,000 curated substance records are currently stored in the registry and serve as the supporting resources for chemistry databases of ChemTunes and COSMOS NG  [25].
Regulatory and safety evaluation information Risk assessment results leading to human safety are easily retrievable in the ChemTunes database. Specifically, the Safety Evaluation browser displays the critical information in this meta database populated with data from opinions and monographs from the regulatory entities. Along with the safety results, quantitative human health-based limit/guidance values are also included, e. g. , Acceptable Daily Intake (ADI) (EFSA, The Joint FAO/WHO Expert Committee on Food Additives; (JECFA)), Total Daily Intake (TDI) (EFSA, Hazard Evaluation Support System (HESS  [26] )), Reference Dose (RfD) (US EPA) [27], Margin of Safety (MOS) (  [28] ). Also available are benchmark doses for carcinogenicity studies from Carcinogenicity Potency Database  [29] , US National Toxicology Program (NTP)  [30] , and International Agency for Research on Cancer (IARC)  [31].
ChemTunes toxicity database Experimental toxicity data are compiled from regulatory sources as well as the open literature. The in vivo and in vitro experimental toxicity data cover over 80 endpoints including, but not limited to, genetic toxicity, carcinogenicity, skin sensitization, skin/eye irritation, repeated-dose toxicity for target organs and developmental and reproductive toxicity (DART). The toxicity database data model has been published previously  [18]. Over 12,000 substances have some level of toxicity data from 117,762 studies. Also available is a curated no observed (adverse) effect level (NO(A)EL) database for >2,400 test substances, which acts as the underlying data for the NO(A)EL Bounds Estimation in silico tool  [32]. A summary of the endpoints available for toxicity data is given in Supplementary Information Table  S1.
Metabolism database. The ChemTunes metabolism database currently has two biotransformation domains, i. e. , endogenous and exogenous reactions of xenobiotics. Metabolism data for drugs are mostly based on human hepatocytes from the literature  [33]  and Pharmacological Reviews for New Drug Approvals (NDA) from US FDA Center for Drug Evaluation and Research (CDER)  [34]. Data for pesticides are from both in vitro and in vivo studies (laboratory rodents, primates, and farm animals) found in EFSA opinions. Combining both drugs and pesticides, transformations of >500 substances by nearly 3,000 phase I and II reactions are covered in the database. The endogenous database contains transformations from, prokaryotes, vertebrates, plants and yeasts, based on 14,540 molecules, 3,912 reactions, and 640 pathways.
Physiologically-Based Kinetics (PBK) database. The PBK database contains both pharmacokinetic (PK) and toxicokinetic (TK) studies. The sources of the database are US FDA CDER  [34] , and various types of EFSA opinions  [35]. Substances include drugs (pharmacological reviews of NDA), industrial chemicals, plant protection products (EFSA PPR) and residues, food contact materials (EFSA CEP) and additives (EFSA FAF), food contaminants (EFSA CONTAM), and industrial chemicals  [36]. Over 900 total substances provide assay information including kinetics, absorption/bioavailability, excretion, distribution, and bioaccumulation. The database captures study design parameters (route of administration, study duration, doses, dosing regimen, species, sex, number of animals in test and control groups, animals age and/or weight), analytical methods, and results for each assay, e.