dataesr/scientific-paragraphs-categorization huggingface dataset:
Literature Analysis and Retrieval of Article and Species Metadata For each retained article, we started by recording the basic information required to identify each article (i. e. , title, first author, year of publication, journal), the species studied and study sites. When an article considered several species or several study sites, we split it accordingly, as our unit of interest for retrieving the environmental-to-home range relationship was the species-specific population. Then, we called a "case-study" each unique species-population, which became our unit of study. We retrieved data related to animal sampling (sample size, age, and sex), duration and period of monitoring (e. g. , all year round, during which season) (Table  1 ). We also recorded information about the location data: type of collars used (VHF or GPS) and number of fixes used per animal to estimate the size of its home range.
We compared models accounting for methodological variables using the Akaike Information Criterion and selected the model with the lowest AIC value. Then, we assessed whether the lifestyle variables (Figure  1 ) could influence the level of support to each ecological driver: body mass (expecting an increased support with increasing body mass), diet (expecting a stronger support for species with moosetype digestive features), and social structure (expecting a reduced support in large group-living species). We fitted GLMs with a binomial distributed error structure and a logit link function, using the "glm" function implemented in the package "stats" of R 3. 3. 3 (R Development Core Team, 2017). We ranked candidate models using the Akaike Information Criterion for small sample sizes (AICc) as implemented in the R package "AICcmodavg"  (Mazerolle, 2020)  and calculated AICc and AICc weights.
Quantifying resources that are edible for ungulates is tricky as it requires information on individual energetic requirements and nutritional state, diet, and diet selection  (Duparc et al. , 2020). Metrics used to measure these factors, derived from remote sensing, field measurements, and proportion of rich or poor habitat available, are often quite crude  (Pettorelli, 2013) , far from the species-specific foodscape (sensu  Searle et al. , 2007; Duparc et al. , 2020). Likewise, the fragmentation perceived by an individual (Sundell-  Turner and Rodewald, 2008)  is difficult to measure from geographic databases where habitats are human-derived categories of land cover and would not necessarily reflect how animals perceive their surrounding landscape  (Li and Reynolds, 1994; Baguette and Van Dyck, 2007).
These difficulties to measure meaningful metrics may explain also why the probability for a case study to conclude opposite to the expectation (i. e. , at least one metric tested contradicting the initial hypothesis) was quite high. The papers with such surprising results were mainly those with a large number of metrics tested. With the continuing increases in map resolution and the growing easiness to analyze patterns in a landscape through dedicated software and packages (e. g. , Fragstats, Fourier transforms,  Rocchini et al. , 2013) , researchers are tempted to include a plethora of variables, which is not advisable  (Streiner and Norman, 2011). This problem probably explains the low proportion of variables found to be statistically significant, the high probability of obtaining at least one variable with an effect opposite to what is expected, and the ad hoc explanation of significant relationships.
ACKNOWLEDGMENTS The study was funded by  ANR  grant  Mov-It coordinated by A. L. ( ANR-16-CE02-0010 ). We thank the  Mov-It consortium  for insightful discussions. We are also grateful to  Marco Apollonio ,  Floris van Beest ,  Steeve Côté ,  Shunsuke Imai ,  Ariane Massé ,  Enrico Merli ,  Andrea Monaco ,  Robin Naidoo ,  Maurizio Ramanzin ,  Sonia Saïd ,  Laura Scillitani ,  Enrico Sturaro  and.
Sow milk yield In contrast to dairy species, milk yield is not easily measured in sows. In the four studies contributing to the analysis, milk yield was estimated indirectly using different methods. To standardize the estimation of milk yield across studies, five prediction methods to estimate milk yield were applied to compare the methods and select the most appropriate method. The comparison of the five methods is described in Supplementary Material S2. The method of  Hansen et al. (2012)  based on data from studies that applied the deuterium oxide dilution technique was selected to estimate milk yield. Average litter gain and litter size during lactation were the two input traits used to estimate milk yield for each day during lactation.
Mammary tissue protein turnover Carbon needed to support the energy demand for peptide bond formation and degradation in mammary tissue protein turnover was based on the mammary tissue protein pool estimated on the day of sampling and the turnover rate of the mammary tissue protein pool. Data presented by  Kim et al. (1999b)  were used to estimate the mammary tissue protein pool on the day of sampling (Supplementary Material S6). The daily turnover rate of mammary protein was assumed to be 61%  (Huber et al. , 2018)  and the average molecular weight of amino acids in mammary tissue protein during lactation to be 131 g/mol  (Kim et al. , 1999a). The energy cost of peptide bond formation and degradation was assumed to be identical to that of milk protein synthesis (i. e. , 4 and 2 ATP/bond;  Wu, 2013).
Statistical analyses Estimated milk yield, MPF, MBF, mammary input of carbon and nitrogen containing nutrients, mammary output of carbon and nitrogen in CO 2 and milk components, and estimations of mammary CO 2 contributing processes were analyzed using the lmer function of the lme4 package  (Bates et al. , 2015)  in R 3. 5. 1. The origin of the study was included as a random effect in the statistical analysis. The same statistical model was used to analyze the results of the static model partitioning carbon and nitrogen input. Linear and quadratic relationships between arterial concentration, AV-differences, MPF, mammary input and mammary output were estimated using the lm function of R 3. 5. 1. Statistical significance was accepted at P ≤ 0. 05, and P ≤ 0. 10 was considered as a tendency.
Discussion The mammary input and output of carbon and nitrogen as well as the input determinants (i. e. , MPF and arterial concentrations and AVdifferences of carbon and nitrogen) were quantified to investigate the partitioning of carbon and nitrogen in lactating sows. Glucose, lactate, NEFA, triglycerides, EAA and NEAA in plasma were considered as the main contributors of carbon and nitrogen inputs to the mammary gland, while other nutrients were assumed to be of minor quantitative importance  (Farmer et al. , 2008). Indeed, short-chain fatty acids and beta-hydroxybutyrate constitute <2. 6% of the total mammary carbon input  (Farmer et al. , 2008; Krogh et al. , 2017)  and were not considered explicitly here. The approach of integrating data sets from four independent experiments required standardization of MPF and milk yield estimates, which both have a quantitative impact on mammary input and output estimates.
11 Partitioning of nitrogen input Nitrogen from essential amino acids, % Milk essential amino acids 87 [68; 100] 3 <0. 001 Milk non-essential amino acids 3 [0; 14] 2 0. 22 Not accounted for 10 [0; 31] 4 0. 08 Nitrogen from non-essential amino acids, % Milk non-essential amino acids 92 [56; 100] 4 <0. 001 Not accounted for 8 [0; 44] 4 0. 14 Estimation of processes contributing to CO 2 , % of mammary output of CO 2 De novo fat synthesis 3 45 [0; 133] 13 0. 05 Peptide bonds, milk protein 4 22 [12; 33] 3 0. 003 Peptide bonds, mammary tissue protein 5 15 [9; 23] 2 0. 003 Lactose synthesis 6 5 [3; 7] 1 0. 008 Mammary glucose input 7 3 [2; 5] 1 0. 01 Mammary amino acid input 7, 8 4 [2; 5] 1 0. 005 Total 94 [40; 179] 11 <0. 001 1 Minimum and maximum values included in the dataset. 2 P-value testing if the mean differs from zero.
Results and Discussion Data were obtained from a total of 113 studies and reports in our research group from 1992 to 2018. A total of 3228 animals, weighing 250-300 g at surgical period, were involved in the present study (n = 348 Wistar; n = 1880 Long-Evans, and n = 1000 Sprague-Dawley strains). The number of animals finally discarded in each study is compiled and presented as a proportion for normalization and inter-study comparison purposes. No effect of strain was ever observed during our experiments, whatever the refinement considered. The proportion of animals that were discarded from the final statistical analyses are spread relative to the category of improvement brought in our experimental procedure. Figure  1  shows the effect of refinements in anesthesia and control of vital signs on the general recovery of the animals (clinical signs of post-surgical pain and distress), Figure  2  illustrates the effect of improvement of aseptic techniques on the number of infectious complications (internal and external), and Figure  3  shows the effects of stereotaxic methodology improvement on the number of animals discarded because of cannula misplacement.
Aspects of genetically altered laboratory animal quality Defining the quality of a laboratory animal is not simple. One operational definition of a "good quality" laboratory animal could be to answer the following two questions affirmatively: (1) Is the use of the initial genetically altered (GA) line appropriate for the scientific question asked? (2) Are the imported animals still appropriate to answer the question? Answering these questions requires a comprehensive description of several criteria, which are summarised in Table  1. Initial quality control usually focuses on the genomic alteration of interest, regardless of the technology used to generate the mutations  (Burgio and Teboul 2020; Birling et al. 2021). Surprisingly, for most published models, the full sequence information of the altered allele is not publicly available or even deposited in a relevant database.
This sequence information is often lost over time as research personnel move on. This lack of information impacts the ability of future users to fully validate the allele, and may ultimately lead to doubts as to the quality and reliability of the research derived from these animals. The materials available from the International Mouse Phenotyping Consortium are a good counterexample of this. The sequences for alleles generated by the Consortium are deposited to a central database that is accessible through the Consortium website (mousephenotype. org). In addition, detailed allele descriptions are deposited to MGI as well as to the repositories from which the mice are distributed. Upon importing a new model, all users should review the way in which the allele of interest was initially validated, taking into account how the genetic alteration was produced (techniques may include embryonic stem cells, additive transgenesis by pronuclear injection, or genome editing, discussed in  Bunton-Stasyshyn et al.
Acknowledgements The authors thank  Dr Louise Tinsley  for expert assistance with the preparation of the manuscript. M. -C. B. and G. P. were supported by the  National Centre for Scientific Research (CNRS) , the  French National Institute of Health and Medical Research (INSERM) , the  University of Strasbourg (UDS) , the  Centre Européen de Recherche en Biologie et en Médecine , the  Agence Nationale de la Recherche  (Grant Nos. ANR-10-IDEX-0002-02  and  ANR-10-INBS-07 , PHENOMIN). M. D. F, S. W. and L. T. were supported by  Medical Research Council  grant  A410  and the  National Institute for Health  (Grant No. U42OD011174 ). L. M. was supported by the  Spanish Ministry of Science and Innovation (MICINN)  (Grant No. RTI2018-101223-B-I00 ). The  Czech Centre for Phenogenomics (CCP)  is supported by the  CAS   RVO 68378050  and by the project  LM2018126   Czech Centre for Phenogenomics  provided by  MEYS.
Statistical Analysis All measurements were tabulated in a Microsoft Excel worksheet and transferred to R Studio (version 3. 4. 3) and JASP (version 0. 8. 6. 0) statistical software for analysis. The homoscedasticity of the variables was tested using the Shapiro-Wilk test (normal distribution) and Levene's test (homogeneity of the variance), and subsequent tests chosen as appropriate. Descriptive statistics, including the mean, SD or median (min/max values of the 25th and 75th percentiles) were used when appropriate. The median age and weight of the foals, the median age of the mares and the median pregnancy length were calculated for each breed. Differences between the Thoroughbred and Standardbred groups in age, weight, pregnancy length and age of the mares were assessed using the Mann-Whitney test.
Discussion In this study, ultrasonographic measurements of the adrenal glands were obtained and compared between a group of healthy neonatal Thoroughbred foals and a group of healthy neonatal Standardbred foals. As previously described  [40, 44] , all the evaluated adrenal glands were characterized by a hypoechogenic cortex and echogenic medulla in the healthy neonatal foals. The normal ultrasonographic appearance, location and measurements of the adrenal glands have been described in healthy puppies and kittens. However, to the authors' knowledge, no data about the effect of age, weight, sex and breed on ultrasonographic measurements of the adrenal glands have been reported in these individuals; consequently, a direct comparison between neonatal foals and neonatal puppies or kittens cannot be made  [45, 46].
Acknowledgments: We would like to thank the  centralized laboratory of the University of Perugia  for all the blood work performed; the physiology laboratory of the  University of Perugia  for the ACTH and aldosterone analyses; the laboratory of the  Department of Food and Animal Science of the University of Udine  for the DHEA, progesterone and cortisol analyses; and all the students and the staff of the  Equine Teaching Hospital of the University of Perugia  and of the  Equine Perinatology Unit of the University of Bologna  for their help with procedures and data collection.
Another use of HCD is monitoring, e. g. background infections or systematic changes in experimental conduct or in the animal model (genetic drift events during breeding). While it is widely acknowledged that the concurrent control is the most relevant control, it is obvious that control groups are subject to random sampling. Hence, the concurrent control can be considered as the most recent sample of the more informative HCD distribution if the concurrent control is sufficiently similar qualitatively to the overall dataset of the HCD. The treatment groups are also subject to random sampling, however, in vivo studies in chemical or pesticide regulation are seldom repeated due to animal welfare concerns. In Europe, regulation legally prevents the repetition of assays in regulatory procedures.
In pesticide registration practice, some authorities, e. g. the European Food Safety Authority (EFSA), assume that since HCD must be available, it is available and thus often requested for endpoints not considered relevant during study conduct (personal experience of the authors). This means that HCD must be obtained, sometimes decades after study conduct, for re-registration processes. Some test guidelines have specific requirements, as in the example below for in vitro assays, or there are regulatory requests based on published recommendations, e. g. Hayashi et al. (2011) , to have at least 10-20 datasets available for genotoxicity HCD. For preclinical studies in pharmaceutical registration, authorities seem to be less stringent in requesting specific time periods. Some OECD documents state specific time periods, as "for example 5 years"  (OECD, 2012a; OECD, 2015) , while others, such as OECD  (2002) , specify for carcinogenicity studies that "[HCD] should come only from studies conducted within five years, or two to three years either side, of the study under review" with the later mitigation that "such data are not always available or may be too limited in scope".
In addition, discussing the use of uncertainty measures and describing HCD as transparently as feasible, depending on the available data is strongly encouraged. Because toxicological evaluations benefit from as much information as possible, HCD datasets should not be included or excluded to aid a toxicological evaluation based on binary criteria, but rather their relevance for an assessment should be weighed. HCD use should be discussed by stakeholders to align interpretation and requirements. This may also facilitate more harmonised documentation of HCD in laboratories and regulatory submissions.
One of the acceptance criteria in 442D for the KeratinoSens™ is that the concurrent positive control should have an EC 1. 5 similar to the positive controls in the HCD: "The EC 1. 5 value of the positive control should be within two standard deviations of the historical mean of the testing facility (e. g. between 7 μM and 30 μM based on the validation dataset) which should be regularly updated. " For the hypoxanthine phosphoribosyl transferase (HPRT) mutagenicity assay, according to OECD TG 476 (2016b), HCD for both positive controls need to be collected and the concurrent control should fall within 95% control limits of the HCD based on a Poisson distribution, which relates to the confidence interval of the mean. The last two examples show an inconsistency in the approach for in vitro assays, which is also seen with other test guidelines.
Setting HCD limits Consider the hypothetical example in Table  2 , showing simulated data for two very small HCD datasets containing three individual assays with three replicates each. The data are assumed continuous and normally distributed, which allows easy calculations and discussion, but are not representative of data most important in HCD use. Dataset 1 represents data that varies similarly within the individual assays but differs in location, i. e. have different means between assays; assays 1 and 3 are 9 units below assay 2. One may discuss whether assay 2 differs so much from assays 1 and 3 that it needs to be excluded from the HCD because of potential systematic differences. Dataset 2 may be considered to vary differently between the individual assays and differ in location between the assays.
Assessing the individual values, an alternative point of view could be that assay 2 includes an "outlier", and one might consider whether this single outlier should be included in the HCD. This hints at multiple inherent issues in HCD documentation and use: 1) Summary statistics may obscure important aspects about the HCD distribution. 2) How to handle between-assay variability? 3) What is an appropriate HCD size to confidently include or exclude new HCD data? That summary statistics may bias assessments has been repeatedly reported  (Fosang and Colbran, 2015; Kluxen, 2019; Kluxen and Hothorn, 2020; Nature Methods Editorial, 2014; Pallmann and Hothorn, 2016; Weissgerber et al. , 2019) , although not in the context of HCD. Table  2  clearly demonstrates that all point estimates obscure the actual distribution, most prominently for assay 2 of Dataset 2.
The data in Table 2 also show the limitation of the range as a summary statistic for the actual distribution, as the datasets are clearly different, but the ranges are identical. When the range is used as a proxy for the HCD distribution, it obviously demonstrates the scale of possible biological responses, but it does not consider their probability. A naïve practical example is that while it is possible to randomly encounter people taller than 2. 5 m, the actual probability is rather low (for the random observer in a random environment). Analogously, an adverse finding can occur in control animals, but the associated probability is very low, hence, it makes sense to consider this information for toxicological evaluations. Additionally, the range naturally and continuously increases with observation number due to random sampling, especially when considering individual observations.
It should be discussed whether nonparametric, empirical methods may be more appropriate in some cases, e. g. for assays with low n, currently done by using the range. Other approaches could be to generate percentile-based limits. Point 2 and point 3 are linked. The example datasets in Table  2  are rather small, and this directly illustrates the challenge: how can extreme responses be robustly included or excluded? In practice, HCD need to be curated with care and experience and considered jointly and not based solely on either toxicological or statistical concerns. This takes time and is rather labour intensive. The inclusion of a very high or low value in the HCD will increase the range of HCD limits. While this might appropriately reflect biological variation, it may also inappropriately increase the range, as the extreme response might be an outlier, which then in turn results in inappropriate assessments.
In addition, for older studies HCD data may not be recovered. Hence, it is helpful to consider HCD as either more or less informative to a toxicological assessment rather than to binarily distinguish HCD similarity and acceptability. The latter has the potential to lead to a situation where no HCD data are available, which is undesirable as pointed out above. Thus, a method for quantifying HCD similarity more along a gradient, which would allow a numerical assessment, should be identified or developed.
Storing HCD In practice, laboratories document and use HCD very differently, especially regarding different assay types. Some studies report means of several runs, means of means and some individual values as HCD, making it sometimes unclear to what the observation numbers or variability measures, e. g. the standard deviation, relate. While the RITA project (Registry of Industrial Toxicology Animaldata) aimed to harmonise the collection of rodent carcinogenicity HCD, such concerted efforts do not exist for data from other bioassays. This might directly affect the values used to discuss HCD with respect to observed toxicity. Furthermore, there are no harmonised data for nonpathology (here also mainly neoplastic and non-neoplastic lesions) findings, e. g. organ weights, haematology, clinical chemistry, urinalysis, ophthalmology, etc.
The meticulous maintenance of HCD also allows other systemic biases to be identified, which may substantially affect toxicological investigation, e. g. diet  (Clode et al. , 2014)  or background infections  (Jacoby and Lindsey, 1998). However, the granularity and stratification detail of collected data may affect the value of assessments, for example, strain-wise databases are useful for strain selection and to estimate and discuss possible outcomes based on strain differences  (Weber, 2017). Test item administration mode and vehicle effects may also play a role  (de Kort et al. , 2020). Control data need to be specified in rodents at least according to study duration, but in non-rodent studies, collection based on animal age is preferable. The reasons for this kind of selection are maturity-and age-related findings.
Whether HCD over different classes or categories, e. g. different application methods, are then pooled for a toxicological assessment needs careful evaluation. However, such an evaluation requires carefully curated and established databases. For non-DART or carcinogenicity data there is no harmonised system available. While preclinical data must be exchanged in SEND format (see www. cdisc. org) for United States of America Food and Drug Administration (FDA) submissions, there is no internationally accepted format for in vitro data. HCD are also stored in different formats and decimal values and not necessarily as individual values. If HCD are to be used systematically and transparently, it would be beneficial to create harmonised databases.