dataesr/scientific-paragraphs-categorization huggingface dataset:
This would allow for WoE analysis of complex datasets with multiple criteria and metrics and would be particularly useful for more complex AOPs or AOP networks. Such analysis is potentially more relevant in chemical hazard characterisation where there may be a need to consider more quantitatively the extent of supporting evidence for different hypothesised modes of action in the induction of adverse outcomes. Quantitative WoE approaches are based directly on the rank ordering of the qualitative WoE considerations through assignment of relative scores. While the assignment of specific scores to the various considerations is necessarily arbitrary, it should be sufficient to characterise the extent of supporting data in a relative rather than absolute context. As a result, quantitative scores for AOPs or their components can only be interpreted in the context of their magnitude relative to scores for other components or AOPs, respectively.
Data Data are required to populate qAOPs, ranging from in vitro data through to in vivo toxicity and observations of adverse effects. There are numerous resources and data compilations. For example, Pawar et al. [18]  reported nearly 1,000 toxicological data resources that may assist in modelling. Fundamental issues that must be considered when using literature data are the quality of the information in terms of the veracity of chemical structure, intrinsic quality and relevance of the original study and that data have been captured correctly, i. e. no errors in transfer. Thus, the quality and accuracy of the data must be checked by the user. There is need for adoption of FAIR data sharing principles, along with the provision of (meta)data that accurately describe biological and toxicological relevant endpoints of the studies.
Some of the key data resources can complement the qualitative description of AOPs available through the AOP-Wiki (https://aopwiki. org/) and may be utilised to populate qAOPs, for example, JRC EURL ECVAM datasets  [66] , the OECD QSAR Toolbox  [33] , OpenFoodTox  [36] , eChemPortal  [35] , US EPA AOP-DB  [49]  and  [81] , US EPA CompTox Chemicals Dashboard  [67]  or PubChem  [68]  (see Supplementary Table  1  for additional information).
Modelling Implementation of a computational qAOP model generally requires some form of modelling software to assist with the mathematical description and analysis (see Supplementary Table  1  for details on software and availability). A variety of resources for quantitative modelling of KERs is available. These include freely available resources and those that require commercial licenses. Software environments such as R (https://www. r-project. org/) and Python (https://www. python. org/) are readily usable and provide packages which are transferable for the purpose of developing qAOPs. The workshop also considered several other bespoke resources which have been developed for modelling in systems biology, biokinetics and for decision analysis such as DART (Decision Analysis by Ranking Techniques)  [69].
Freely available software (such as KNIME  [70] ) can incorporate these statistical models into usable computational workflows. There are also resources that can assist with the evaluation of literature and more efficient mining of text information. Other resources were also considered such as reported by Madden et al. [71]  who provided a detailed overview of computational resources for the parameterisation of PBK models. PBK models are mathematical description of the adsorption, distribution, metabolism and excretion of chemicals in the body and are usually described by ordinary differential equations. This is complementary to a qAOP model which is a mathematical description of the mechanistic downstream events/mode of action of the chemical after the kinetic process has taken place.
Hazard and safety assessment The translation of qAOPs to usable tools to assist in hazard and safety assessment is essential for their uptake in the broader scientific community. Currently there is no platform that provides a systematic integration of qAOPs for use in hazard and safety assessment. However, the workshop noted some existing resources, such as the OECD QSAR Fig. 6. Graphical summary representing the elements described in the present paper, from qAOP model purpose, through knowledge of biology and quantification of the KE by measured data to modelling predictions driven by regulatory application. For each step e-resources are mapped. Toolbox  [33] , EFSA Knowledge Junction  [72]  and Euromix Toolbox  [73] , as useful now and in the future for this purpose. Several different software tools (Supplementary Table  1 ) are available and although the choice mostly depends on the modeller's preferences, it is advisable to use open-source applications that increase transparency, transferability and acceptance of the model.
Confidence in qAOP models may increase through iterative refinement of the model for example by developing more comprehensive datasets, which many imply the need to conduct additional studies. It is also possible that the inclusion of additional data will result in the need to refine the original model (choice of KEs and KERs). In addition, challenging the model with reference compounds that were not used for the initial model calibration increases confidence in the results. An electronic infrastructure such as the Adverse Outcome Pathway Knowledge Base (AOP-KB, https://a opkb. oecd. org/background. html) provides a perfect host platform to implement automated qAOPs in a user-friendly, open access environment. The suggested modelling strategy is to consider the question of interest and then search the AOP-Wiki for KERs of relevance, considering that quantitative data are more likely to exist or to a larger extent for KERs for which empirical support in qualitative weight of evidence calls is "High", and also that KERs at higher levels of biological organisation (i.
Acknowledgements The authors would like to thank the host of the workshop, the  Lorentz Center in Leiden  (NL; www. lorentzcenter. nl). The authors are in particular grateful for all the help received from  Gerda Filippo , who primarily coordinated the planning of the event. The five-day workshop was hosted at the  Lorentz Center in Leiden (NL) , which promotes international workshops in all scientific disciplines with support from the  Netherlands Organisation for Scientific Research (NWO)  and the  University of Leiden. The workshop was supported with additional sponsorship from the  European Commission's Joint Research Centre , the  European Chemical Industry Council (CEFIC)  and the  PETA International Science Consortium.
Funding Marvin Martens  has received funding from the  European Union 's  Horizon 2020 (EU H2020) research and innovation program  under grant agreement no. 681002  ( EU-ToxRisk ) and  EINFRA-22-2016 program  under grant agreement no. 731075  (OpenRiskNet). Nicoleta Spînu  acknowledges the funds received from the  European Union   Marie Skłodowska-Curie Action Innovative Training Network in3  Project (https://estiv. org/in3/),under grant agreement no. 721975.
learnings Adverse outcome AOPs or AOP networks Reference AOP description Datasets e-resources Modelling approach Skin sensitisation "Covalent Protein https:// • Linear AOP • Data-rich AOP: several OECD QSAR toolbox [33] Bayesian networks that binding leading to aopwiki. org/ • OECD endorsed datasets available in allow combination of Skin aops/40 • Good level of literature e. g. dataset by diverse datasets Sensitisation" - documentation for Urbisch et al. [24] and AOP 40 all KEs and KERs evaluated by Hoffmann et al. [25] • Data available for in vitro, in chemico and in vivo studies. Full matrix for ≥ 120 chemicals. Human data also available for most of the chemicals • Diversity of measurement units between sources (e. g. % of effect at a fixed concentration and time exposure vs. concentration at which effect is 3-fold).
Need to normalise data points. (Developmental) AOPs for [17]AOP- • Network of AOPs • Data limitations: Developmental Bayesian networks that neurotoxicity Case study 2: (developmental) neurotoxicity neurotoxicity -AOP 3, 12, 13, 17, Wiki (http s://aopwiki. • AOPs at different level of development availability of precompiled datasets. Literature reviews NeuroToxicity Data Integration and can be used with relatively sparse data 42, 48, 54, 134, org/) for the (from under needed to extract Visualization Enabling or when multiple 260 single AOPs development to quantitative relevant Resource (DNT-DIVER) pathways can affect the OECD endorsed) information about KEs from [34] AO • Focus on literature intermediate and • Data available from in vitro interconnected KEs and in vivo studies and KERs • Need to convert different data points to the same unit of measurements, apply the same weight to individual studies or data points in a single study Carcinogenicity "Cyp2E1 https://a • Linear AOP • Good data availability: OECD eChemPortal [35] An equation (similar to Activation opwiki.
org • Under final stage of datasets available from EFSA OpenFoodTox Zgheib et al. [31]) Leading to Liver /aops/220 OECD review literature and regulatory database [36]PubMed describing dose Cancer" -AOP • Focus on late KEs dossiers (keywords used: substance response relationship 220 and KERs predictive • Available data mainly name, CAS N. , proliferation, was applied and data of AO originated from in vivo tumour) [37]EFSA were also normalised studies publications (e. g. EFSA's using PBK modelling • Importance of well- Peer Review Conclusions on designed studies of Pesticides) [38]Health dose-response relationships Canada's Assessment for KEs at several levels of Reports [39] biological organisation at relevant time points.
Systematic reviews and meta-analyses have been proposed as an approach to synthesize the literature and counteract the lack of power of small preclinical studies. We aimed to evaluate (1) the methodology of these reviews, (2) the methodological quality of the studies they included and (3) whether study methodological characteristics affect effect size. We searched MEDLINE to retrieve 212 systematic reviews with meta-analyses of preclinical studies published from January, 2018 to March, 2020. Less than 15% explored the grey literature. Selection, data extraction and risk of bias assessment were performed in duplicate in less than two thirds of reviews. Most of them assessed the methodological quality of included studies and reported the meta-analysis model. The risk of bias of included studies was mostly rated unclear.
Methodological characteristics of systematic reviews with meta-analyses of preclinical studies. All reviews conducted an electronic search, and almost all (99. 1%) reported at least one electronic data-Figure  1. Selection process of preclinical systematic reviews with meta-analyses. Adapted from the PRISMA flow diagram  18. base. The search equation was reported in 66. 5% of reviews, and an animal filter was applied in 10. 8%. A search of other sources was reported for 62. 7% of reviews, predominantly the exploration of reference list of selected studies (75. 9%). The grey literature was explored in 25 (11. 8%) reviews and the OpenGrey website for 6 (4. 5%). The selection and data extraction process were performed in duplicate in 67. 9% and 46. 7% of reviews, respectively. Methodological quality of included studies was assessed in 83.
7%. The impact of the methodological quality on meta-analysis results was assessed in 11. 3% of meta-analyses. Small-study effects were explored in 64. 5% of meta-analyses including 10 experimental arms or more. In total, 33 (15. 6%) meta-analyses included both animal and human studies in their systematic review. More than half (54. 5%; n = 18) performed separate meta-analyses according to human or animal data, and 27. 3% (n = 9) meta-analyzed only animal data (Supplementary Table  S2 ).
Discussion This review provides an overview of the methodological quality of recently published systematic reviews with meta-analyses of preclinical research. Regarding the systematic review process, an electronic database was almost always reported, and most systematic reviews assessed the methodological quality of included studies. In addition, almost all reviews reported the meta-analysis model and an evaluation of statistical heterogeneity, which was explored in two thirds of reviews. Still, there is some room for improvement because a clear definition of the control group was often missing both in the methods and in the description of included studies in results. Selection, data extraction and risk of bias assessment were performed in duplicate in only 67. 9%, 46. 7% and 59. 3% of reviews, respectively.
Nevertheless, our study presents several limitations. Our search strategy might have induced a selection bias because we searched only the MEDLINE database and included only systematic reviews associated with meta-analyses, which may feature greater methodological quality than the overall literature  [11] [12] [13]. Our meta-epidemiological analysis also has two weaknesses: lack of power as discussed above and the heterogeneity of results, which may be explained by differences in rating between review authors in that we relied on only data extracted from systematic reviews. To conclude, even if the overall methodological profile of systematic reviews with meta-analyses of preclinical research seems to show substantial improvement, the reporting and methodological quality of the studies they include may still jeopardize their internal validity and limit their interpretation to exploratory data.
Methods This is a methodological review of systematic reviews with meta-analyses of preclinical studies with a metaepidemiological analysis of study characteristics associated with effect size. A protocol (available upon request) was written before starting the review process. Search strategy. We searched MEDLINE via PubMed for systematic reviews with meta-analyses of preclinical studies published between January 1, 2018 and March 1, 2020. We focused on MEDLINE because it is the most widely used database and because our aim was not to be exhaustive but to retrieve a large sample of recent studies. The search algorithm combined terms for meta-analyses and preclinical, in vivo or animal studies and their synonyms  47  (Supplementary File S1). Eligibility criteria. We selected all systematic reviews with meta-analyses reporting the evaluation of a pharmacological (i.
Selection process. References retrieved by the search were imported to a reference manager (Zotero 5. 0). The title and abstract were first screened by one reviewer (NST). The full text was assessed for eligibility by two reviewers (NST and ALG). Disagreements were solved by consensus. Data extraction. Two reviewers (NST and DR) extracted data for all selected studies according to two standardized data extraction forms (one for systematic reviews and meta-analyses and one for included preclinical studies). Disagreements were resolved by consensus. Characteristics of systematic reviews and meta-analyses. From available recommendations for systematic reviews and meta-analyses of preclinical studies  9, 10, 14  , we extracted the following data for each systematic review. General characteristics extracted were publication date, journal name and impact factor; medical condition of interest; reporting of protocol availability (i.
e. , registration or publication); any involvement of epidemiologists or statisticians as defined elsewhere  49  ; funding source and declared conflicts of interest by the review authors; and main objective of the study, expressed as population, intervention or exposure, control group and outcome (PICO  18  ), explicitly provided by the authors or extracted from the text of the article. We also evaluated whether a primary outcome was defined. Data extracted for the systematic review process were first those related to the search strategy: interrogated electronic databases, reporting of the search algorithm for at least one database, and eventual use of an animal filter  47, 50  ; whether restriction of language or publication date was applied, search of reference lists of articles or relevant reviews, manual search of conference abstracts, books or specific journals, investigation of the "grey" literature (Google search engine queries, dedicated websites, and unpublished data obtained by contacting experts in the field).
For characteristics that may vary across meta-analyses (e. g. , effect size measure), we focused on the primary outcome or the first reported if no primary outcome was defined. Characteristics of included preclinical studies. We focused on systematic reviews and meta-analyses reporting an evaluation of the methodological quality for each included study and a standardized mean difference (SMD) as the effect size for the primary or first reported outcome (this sample was also used for the subsequent metaepidemiological analysis). For each included study, we collected the language of publication, the country of the laboratory (based on the affiliation of the last author) and the species used for experimentation. We extracted features related to the risk of bias, adapted from the SYRCLE risk of bias tool for animal studies  22  : reporting of randomization, and if available, appropriate sequence generation; similar group characteristics at baseline; appropriate allocation concealment; random housing of animals; blinding of caregivers; blinding of the animal model; random assessment of outcomes; blinding of assessors; and adequate management of incomplete outcome data and presence of selective outcome reporting.
The quality of reporting and methods of experimental procedures were rated according to the CAMARADES quality items  51  : publication in a peer-reviewed journal, reporting of control of temperature during experimental procedure, use of an anesthetic without protective effect on outcome, description of the animal model, sample size calculation, compliance with animal welfare regulations and statement of conflicts of interest. The value of each individual study SMD and its confidence interval for the primary or first reported outcome was also collected for the meta-epidemiological analysis. All data were extracted directly from the reviews, except language of publication and country of laboratory, which were retrieved from the original publication. Statistical analysis. Descriptive analysis.
Therapeutic intervention Pathophysiology (n = 212) (n = 165) (n = 47) Search strategy Electronic search 210 (99. 1%) 164 (99. 4%) (97. 9%) Medline 209 (99. 5%) 163 (99. 4%) (100%) Embase 135 (64. 3%) 106 (64. 6%) (63. 0%) Scopus 43 (20. 5%) 33 (20. 1%) (21. 7%) Web of science 91 (43. 3%) 69 (42. 1%) (47. 8%) Cochrane library 57 (27. 1%) 49 (29. 9%) (17. 4%) Google Scholar 32 (15. 2%) 24 (14. 6%) (17. 4%) Chinese language databases* 37 (17. 6%) 33 (20. 1%) (8. 7%) Other databases 71 (33. 8%) 54 (32. 9%) (37. 0%) No database reported 2 (0. 9%) 1 (0. 6%) (2. 1%) Reported search equation Yes 141 (66. 5%) 106 (64. 2%) (74. 5%) Only keywords or unclear equation 64 (30. 2%) 54 (32. 7%) (21. 3%) Use of an animal filter** 23 (10. 8%) 17 (10. 3%) (12. 8%) Other sources of identification of studies 133 (62.
ACKNOWLEDGMENTS This work formed part of a key deliverable of the GroupHouseNet (Cost Action  15134 ) supported by  COST (European Cooperation in Science and Technology). The authors would like to thank  Dr. Sabine Dippel  for leading the early conceptualization of this manuscript and offer sincere thanks to  Prof. Mari Heinonen  for her helpful comments on the manuscript. We would also like to acknowledge  Joana Pessoa  and  Martyna Lagoda  for help with the formatting.
Animal-borne cameras have been used in ecology studies for more than three decades. To our knowledge, POV cameras have not been used before on sheep, although they have been used on feral and domestic cats  [19, 20] , and on wild species (recently reviewed by  [21] ). We hypothesize that POV cameras provide video sequences that can be used as a new method to capture feeding behavioural data on free range grazing sheep together with the spatiotemporal hierarchy framework of grazing behaviour, which is described briefly in the next section. Considering the characteristic seasonal changes in sward height and the botanical composition of Mediterranean pastures, our aim was to ascertain whether POV cameras could be used to determine the microstructure of intake (the study of kinetics of food intake during feeding episodes in varying feed conditions), particularly (i) the variation in frequency and duration of meals, feeding bouts, and feeding stations and (ii) the variation in diet selection.
Trial layout and experimental design Two experiments were designed to evaluate the use of POV cameras to measure grazing response. Animals were the experimental unit, and we used repeated-measures designs involving multiple measurements on each experimental unit that functions as an experimental block. Experiment 1 covered the spring pasture growth, assuming that more pronounced behavioural adaptations occur in this period. Experiment 2 aimed to determine whether behaviour variables captured with POV cameras could represent the behaviour of the entire flock. Behavioural data were collected in both experiments using POV cameras (screenshots shown in  Fig 9)  and GNSS receivers (c. f. Behavioural Sensors section). The ethogram of observed behaviours is presented as supporting information (S2 Table ).
In experiment 1, recording periods were set to cover morning and afternoon meals, whereas in experiment 2, an entire day was targeted. Before each recording period, the flock was gathered to replace POV camera batteries. The GNSS receivers were replaced weekly. Experiment 1 was carried out from 4 April to 13 June 2016 across 11 consecutive weeks using a flock of 15 ewes. The whole flock was used for behavioural observations, and 5 of the flock of 15 animals were used for video recording. Video recording was restricted to daylight hours. Recordings from the same 5 animals were conducted once a week and used for extracting behavioural data, totalling 110 observations (5 animals × 11 weeks × 2 meals). Because of the environmental differences occurring between weeks, data were clustered in two seasons (early and late spring) of 5 weeks each, corresponding to the sward vegetative and reproductive stage, with a 1-week interval between seasons.
Each video sequence matched a period of the day, totalling five time periods (6-9am, 10am-1pm, 2-5pm, 6-9pm, and 9pm-6am). Simultaneously, flock behavioural activity (S2 Table ) was measured from 6am to 9pm by direct observation using 10 min intervals with instantaneous scan sampling  [30]  (the behaviour of all individuals in a group of animals is recorded, each animal in turn, instantaneously). For later comparison of both datasets, data were analysed for each hour and again clustered in five periods of 170 minutes each. A repeated-measures crossover design was used to reduce error variance associated with inter-individual differences and to increase the statistical power. Video recording matched the moon quarters (third quarter on 19 April and 19 May; first quarter on 3 May) to have similar nocturnal light conditions.
Pasture measures The study area was virtually divided into 60 regular patches of 380 m 2 each. Sheep are known to adjust their behaviour to maintain group cohesion whenever the space allowance for each animal is over 200 m 2  [31]. The dimension of the defined patches (380 m 2 ) allows for a maximum dispersal distance within a group (social distance) to ensure the location of the entire flock within one patch. Each patch represents a resource unit. Before forage sampling, each patch was assigned to one of the two sets (visited vs not-visited by ewes) by GNSS-tracked sheep movements. Data on animals' georeferenced locations were downloaded each week. The last 72 hours of each week were analysed using geospatial software (c. f. Behavioural Sensors section), and visited vs not-visited patches were newly defined.