dataesr/scientific-paragraphs-categorization huggingface dataset:
DATA The TTM2-trial collected background, prehospital-, admission-, ICU-level and follow-up data through an electronic case report form. Prehospital-and admission data were registered in accordance with the Utstein template. 16  Blood gas analyses, pH, glucose and lactate were registered from the first arterial blood gas after ROSC, whereas the first available value was recorded for creatinine. Neurological assessment at hospital admission was performed and recorded using the components (eye responses, motor responses, brainstem reflexes, and respiration pattern) of the Full Outline of UnResponsiveness (FOUR)-score, which allows assessment of the level of consciousness in intubated patients. 17  All risk scores assessed in the present study except for MIRACLE2 include both the time from collapse to start of any cardiopulmonary resuscitation (the "no-flow"-time) and the time from start of resuscitation to ROSC (the "low-flow" time).
The TTM2 trial, however, recorded the time to advanced life support (ALS) and time to ROSC as per the latest update of the Utstein-template. 16  Similarly, data on rhythm evolution over the course of resuscitation were not recorded. This necessitated assumptions for variables that could not be derived from available data, i. e. no-flow/lowflow distinction for cases with bystander CPR and the MIRACLE2 item "changing intra-arrest rhythms". For cases with bystander CPR, a no-flow time of 1 minute (median time to BLS among patients receiving bystander CPR in both the TTM-trial  11  and other predominantly European studies  [18] [19] [20]  ) was assumed. Participants with a non-shockable initial rhythm who were defibrillated were assumed to have changing intra-arrest rhythms. Details on data assumptions are outlined in table  S2.
STATISTICAL ANALYSES Score variables were presented as absolute and relative frequency, median and inter-quartile range (IQR) or mean and standard deviation (SD). Their association with a poor outcome was presented as odds ratios (OR) rather than relative risks to preserve comparability to the respective development studies. Missing data was assumed to be missing at random (MAR) and handled using mice: Multivariate Imputation by Chained Equations in R,  21  with all available background, prehospital, admission-level and outcome variables of the TTM2-dataset considered for inclusion in the multiple imputation process. Details on variable selection, multiple imputation and diagnostics thereof are available in the supplementary appendix. A total of 50 datasets including all variables of all evaluated scoring systems were imputed.
The handling and reporting of multiple imputation in this manuscript adheres to guidelines by Sterne et al. 22  Scores were estimated in each of the imputed datasets using the criteria defined in the original publications of the OHCA-, TTM-10 and MIRACLE2 9 -scores. For the CAHP-score, which in its original publication was presented only as a nomogram, parameters for estimation were retrieved from a later publication. 23  Score items are presented in relation to the evaluated scoring systems in figure  1 , and details on their estimations are available in figure  S1. Overall discriminatory performance was evaluated using ROC-curves, with the area under the ROC-curve (AUROC) being estimated for each of the imputed datasets and then pooled (after log-transformation) using Rubin's rules. Diagnostic measures of sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) at the proposed cut-offs of each score were also calculated in all imputed datasets, with results presented as the between-imputations observed minimum-maximum value of each measure.
The first data point yielding a specificity above 95 % was identified with the datasets ordered after each respective score. Pooled confidence intervals of these estimates are available in the supplementary appendix (table  S3 ). The full regression models have not been made publicly available for any of the evaluated scoring systems. With clinical applicability nonetheless being the aim of this study, no attempt to collect original data from the respective authors was made and only the scoring systems themselves were evaluated. As a result, calibration could only be evaluated by indirect measures. The relationship between score and the observed outcome was assessed graphically as the proportion of a poor outcome per level (for discrete scores) or interval (for continuous scores) of the score.
RESULTS Of the 1861 participants included in the intention to treat-population of the TTM2 trial, functional outcome data at 6 months after cardiac arrest were available for 1829 (98. 3 %), which constituted the study population. The mean age was 63. 9 (SD 13. 6) years, 1447 (79. 1 %) patients were male and 988 (54. 0 %) had a poor outcome (mRS 4-6) at 6 months. Data needed for score estimations were available for > 95 % cases with the exception of information on corneal reflexes at admission (valid n = 1031, 56. 4 %) and pupillary reflexes at admission (valid n = 1510, 82. 6 %). All variables were significantly correlated with outcome except for unwitnessed arrest (OR 1. 30, 95 % CI 0. 93 -1. 82 for a poor outcome) and PaCO2 < 4. 5 kPa at admission (OR 1. 34, 95 % CI 0. 90 -2. 02 for a poor outcome).
FUNDING The TTM2-trial was supported by independent research grants from nonprofit or governmental agencies (the Swedish Research Council [Vetenskapsrådet], Swedish Heart-Lung Foundation, Stig and Ragna Gorthon Foundation, Knutsson Foundation, Laerdal Foundation, Hans-Gabriel and Alice Trolle-Wachtmeister foundation for medical Research, and regional research Support in Region Skåne) and by governmental funding of clinical research within the Swedish National Health Service. Authors S. S and H. F received regional research support from Region Skåne and funding from the Laerdal Foundation, respectively, for the present work.
Maritime accident investigation reports: an overview In addressing the research objectives, this study used the publicly available maritime accident reports submitted to the IMO from 2011 to 2021 as the primary data source. However, very few studies have used accident reports because there is a fundamental lack of standardisation of information in the largely qualitative reports, which makes them difficult to analyse  (Fan et al. 2020; Zhang et al. 2019a, b). Thus, there is a need for the data to be prepared more systematically with more consistent definitions so that they can be used to investigate socio-technical causation factors in accident modelling  (Mazaheri et al. (2015). However, this is very time-consuming, and of the few studies that use accident reports, they either only use a limited number of reports as their database or the analysis is too tentative  (Fan et al.
2020; Zhang et al. 2019a, b). This study addresses these shortcomings by demonstrating how such data can be effectively analysed using novel text-mining techniques to extract new insights into maritime accident risk factors. In a maritime accident, each respective national reporting Administration must submit marine casualties and incidents reports and complete marine safety investigations to the International Maritime Organization  (IMO 2021a, b). According to the Code of International Standards and Recommended Practices for a Safety Investigation into Marine Casualty or Incident, this includes information related to vessel details, other vessels involved, occurrence type including external factors (weather and light), voyage data, consequences, injuries and fatalities, description of occurrence (including sequence of events) and recommendations for preventing the accidents in future (IMO 2021a).
The GISIS module is organised into five sections or appendices  (IMO 2014) , where data can be entered into pre-set fields. Completion of the first two appendices is required as a minimum and include Appendix (1) generic information related to the accident, including the State, numbers of ships involved, generic casualty data and recommendations to prevent future marine casualties; Appendix (2) factual information related to each ship involved in the incident/accident including ship particulars, voyage data and casualty data. For the remaining appendices, the IMO requests that the Administrators provide as much data as possible. The data fields for Appendix (3) provide the input of casualty analysis data related to each ship involved, including accidental events and contributing factors. Appendix (4) enables the input of supplementary information according to the particular circumstances relating to the respective casualty/incident.
Methodology Maritime investigation accident reports submitted to the IMO are a valuable primary qualitative data source containing detail including navigational circumstances, actions taken, the chain of events, and environmental conditions, as already detailed earlier. The research methods and steps followed in this study are summarised in Fig. 1. First, data must be prepared by compiling, classifying and formatting it. Once the data was prepared, statistical and text mining methods using the IRAMUTEQ software were applied and explained in detail in the next sections.
Database set-up and descriptive statistics: The data for this research was generated from 504 qualitative maritime accident reports from the IMO database covering 2011-2021. The selection and data generation protocol involved the following steps: (1) Searching the online IMO database for accident reports falling within a ten-year period from 12 July 2011 to 12 July 2021, (2) Manually screening each of the reports to ensure they included all the information required for this study (namely checking for the presence of vessel and accident details, the seriousness of accidents including casualties and accident narratives), (3) Systematically quantifying and organising the data to make it suitable for applying the relevant statistical and text mining techniques. In line with the published research into maritime accident risk indicators, we set up the databases with the four most commonly used variables.
Text mining analysis (IRAMUTEQ) The major aim of text mining is to find patterns in unstructured and semi-structured text, including some but limited metadata such as dates, titles, names etc. (Brown 2016). Through pattern discovery, the contents of largely unstructured/semi-structured qualitative documents (such as the accident reports in this study) can be characterised to address research objectives. Here, text mining provides much richer information and insights into accident characteristics not available from only fixed field entries, which are often limited to the schema and structure of the researchers' objectives and dataset  (Brown 2016). For this analysis, IRAMUTEQ (Interface de R pour les Analyses Multidimensionnelles de Textes et de Questionnaires) created by Pierre Ratinaud was used.
IRA-MUTEQ is a free open source text mining software based on the statistical package R and Python, that has a 'user-friendly interface' and 'provides quality graphical output'  (Sarrica et al. 2016). Until recently, this software could only accommodate the French language but has since included dictionaries in several languages, including English and Portuguese  (Souza et al. 2018) , and is now being used more frequently for qualitative research in social sciences, psychology, and health studies to analyse large textual corpora to highlight thematic categories and analyse similarities  (Chaves et al. 2017; Sarrica et al. 2016).
Text mining analyses As per Fig. 1 , three techniques available in the IRAMUTEQ software were used to analyse the data: (1) Similarity analysis (SA) based on the graph theory, identifies the co-occurrences of words, providing information about their connectivity to help identify the structure of the context of a corpus of text. It also identifies the shared aspects and specificities of the descriptive variables identified in the analysis (Marchand and Ratinaud 2012). Analysing the proximity of the most frequent terms (in this study, 143 most frequent terms) presents the 'universes of words' (Fig. 3 ), constituting the central terms and how they relate to other terms by proximity. This study used the cooccurrences index, which produces similar results to the ChiSquare index). (  2 ) Descending Hierarchical Classification Analysis (DHCA) was used to drill down into the main themes developed in the corpus using the Reinert Method (Fig.
Descriptive statistics In the dataset of IMO reports extracted for this study, there were 504 maritime accidents over the decade from January 2011 to December 2020 and 502 deaths and 744 injuries (Table  1 ). The average number of reported deaths for the period was nearly 1 (0. 996). The majority of the vessels involved in accidents resulting in serious casualties (deaths) were large (over 20,000 gross tonnage), operating under a white flag state, young (under ten years old) and bulker vessels. More descriptive details are summarised in Table  1. The total number of maritime accidents has been falling over the past decade, as illustrated in Fig. 2a. However, since that time, the reason for the sudden halving of accidents in 2015 and a more steady fall in accident numbers is unclear. It will be interesting to monitor this figure over the next few years to see whether this is a consistent trend that marks a real improvement in maritime safety.
Analysis of variance (ANOVA) results The variables summarised in Table  1  were used for the analysis of variance. The metric for accident seriousness was the number of deaths, which was set as the dependent variable. The other categorical variables, Flag State, Vessel type, Age and Size, which are characteristics of the maritime vessel, were set as independent variables to examine the differences between these categories. In this case, the assumption of normality was tested, and the distribution of the data in the dataset (number of deaths) was not normal, which is not unusual in the social sciences and reflects the underlying nature of the construct being measured-namely characteristics of marine vessels and number of deaths. Consequently, we used the nonparametric Kruskal-Wallis test, which does not require the assumption of normality or equal variance.
By applying the clusterisation technique to the IMO dataset, five clear clusters emerged. There are presented in the dendrogram in Fig. 4a. (1) Cluster 1 (red) deals with the safety procedures and their management within the ship organisations ('procedure' , 'safety' , 'company' , 'train') and represents 26. 14% of total text segments (TS). (2) Cluster 2 (grey) deals with accident narration on board related to equipment failures ('fire' , 'room' , 'engine') and represents 14. 68% of total TS. (3) Cluster 3 (green) deals with accident narration on board directly caused by individuals on board ('hold' , 'hatch", 'deck' , 'fall' , 'ladder') and represents 19. 38% of total TS (4) Cluster 4 (blue) deals with international regulations and safety rules that are checked during the investigation ('certificate' , 'imo' , 'panama') and represents 13.
All in all, these clusters represent 75,5% of all the content of investigation reports. The remaining Clusters 1 (the ship's internal ecosystem) and 4 (the ship's external ecosystem and global regulation organisations) only represent 24,5% of the total TS of the IMO investigation reports. We argue that these stakeholders' roles, relationships, potential interdependencies, and responsibilities should be studied in more detail to improve our understanding of maritime navigation accident risk factors. In "Discussion" section, we discuss our findings from the statistical analysis and present the maritime ecosystem framework, which is based on the five clusters that emerged from the data analysis and their in-depth analysis using IRAMUTEQ.
Maritime accident risk indicators based on vessel characteristics Looking firstly at the statistical findings exploring the characteristics of maritime vessels and severity of outcomes (deaths) of maritime accidents, some of the findings from this study were consistent with some studies but inconsistent with others. For instance, contrary to our findings,  Li and Wonham (1999)  found a relationship between accidents and flag states under which the ships were registered using the Lloyd's Register of Ships database. Furthermore, using data from 2012 marine accidents in Hong Kong,  Yip (2008)  found that the potential for fatal accidents and injuries increased according to vessel types and types of accidents. These findings contradict our own, where our ANOVA found no differences in fatalities based on vessel types or flag states.
Conclusion and recommendations Human error continues to be highlighted as the top-cited cause of maritime accidents. The major aim of this study was to broaden and deepen our understanding of maritime accident risk factors beyond those of the human. This study adopted a datadriven approach, statistical (ANOVA) and advanced text-mining techniques (using IRA-MUTEQ software) to extract more in-depth meaning from the often difficult to analyse semi-structured and unstructured narrative descriptions of the IMO accident reports. Building on the text analysis of the IMO accident data, we proposed the Accident Maritime Ecosystem Framework, which incorporates individuals, the ship organisation (on board), the internal ship ecosystem (on board and onshore), the external ship ecosystem (external factors) and the global maritime ecosystem (policies and regulations); moreover, it identifies these entities as risk factors in maritime accidents.
The potential of statistical analysis with vessel characteristics and accident reports to further investigate the risks of maritime navigation accidents Most risk factors of maritime navigation accidents are investigated through statistics with vessel characteristics and accident reports. Our results show that the different research using statistics with vessel characteristics are inconsistent. This is partly due to the development of statistics with incomplete databases, which lead to inconsistent findings and hence a lack of clarity for industry decision-makers for improving maritime safety. The development of open science databases, supported by organisations such as IMO, should improve the potential of further research in this area. Regarding maritime accident investigation reports, there is a valuable resource for learning and improving the understanding of risk indicators across the maritime ecosystem.
Finally, to improve knowledge transfer and learning from maritime accidents, this study recommends an open science database to share knowledge and information and ensure that the often-reported recommendations are enacted and developed into practical and applied measures. As with all research, there are limitations. This study has focused on a limited number of variables extracted from the IMO database because it is so time and resource intensive. Similarly, the text mining analyses are conducted on IMO reports over ten years; other official reports can also be studied. Future studies could extend this research to include additional information such as geo-locations, times, and types of accidents. Also, future studies could repeat this research using reports from other international maritime organisations to compare findings.
Nonetheless, a few answers had to be registered as "several", "a lot" or "too many". In those cases, "several" was counted as n = 5-10 and both "a lot" and "too many" were counted as n > 10. Regarding the seasonality of captures, respondents were asked to indicate the time of the year (months) they had captured H. griseus from 2018 to 2019 and from 2007 to 2017. A Chi-squared test  [26]  was carried out using IBM SPSS Statistics (Version 24) predictive analytics software to check whether captures were distributed similarly between the times of the year when the water column is stratified (May to October) and non-stratified (November to April). A Chi-square test was also used to check whether the probability of capturing H. griseus was due to a change in fishing effort (number of fishing months) between these two times of the year for the fishing gears that captured most individuals (bottom trawling, bottom longline and trammel and gillnets).
Acknowledgements This study was funded by the  Shark Foundation (Switzerland)  (Grant Number:  2019-04-01 ). All authors have been involved at the final draft stage and have seen and approved the final draft for submission. The authors give their warmest thanks to all the fishermen who shared their time and knowledge with us. We also thank  Dr. Esam Buzaid  for his valuable assistance in Libya and  Kristian Beqiri  and  Samira Enajjar  for their assistance in collecting data. Finally, we would like to thank the editor of the journal and the two anonymous reviewers for their constructive comments, which helped improve the manuscript and  Samuele Richetta  for his language revision.
2017; Hueter et al. 2018). The challenges of studying elusive species in the open ocean have led to a reliance on fishery-dependent data, which provide valuable information on geographic distribution but are limited by gear size selectivity, non-random distribution of fishing effort, and underreporting of shark landings  (Kendall et al. 2009; Jacquet et al. 2010; Temple et al. 2019). The recent development of mid-water stereo-baited remote underwater video systems (BRUVS) has facilitated non-extractive sampling within previously inaccessible environments  (Santana-Garcon et al. 2014; Letessier et al. 2019 ) and reduced our reliance on fishery-dependent data to enhance our understanding of pelagic wildlife  (Letessier et al. 2017; Thompson et al. 2021). We mined data from a mid-water BRUVS database curated by the Marine Futures Lab for 39 locations across three ocean basins to: (1) document the distribution, abundance and size structure of silky shark populations, (2) identify the most important environmental, geographic and anthropogenic predictors of their presence, abundance and body size, and (3) assess if their abundance is greater within designated MPAs than in locations not designated as MPAs.
Survey methods Our analysis is based on a dataset of video-based records of ocean wildlife collected using midwater BRUVS. Each mid-water rig is composed of a lightweight 1. 45-m long stainless-steel frame, a perpendicular 180-cm long bait arm, a 45-cm long perforated PVC canister containing 1 kg of pilchards (Sardinops sagax) and two high-definition small action cameras, in waterproof housings, that are mounted 80 cm apart and angled inwards by 8° on a horizontal base bar (Fig. 1 ). Stereo cameras enable identification, counts and length measurements of individuals present in the field of view within a range of ca. 10 m. Individual mid-water BRUVS are deployed in a longline configuration of 'sets' of three to five rigs, each separated by 200 m of horizontal line. Mid-water BRUVS are deployed, suspended at a depth of 10 m and then recovered following a 2-h soak time.
Video treatment and analysis This analysis is based on video imagery from 1418 deployments of mid-water BRUVS during 66 surveys at 39 locations between April 2012 and March 2020 held in a dataset curated by the Marine Futures Lab at the University of Western Australia (Supplementary Information Appendix 1; Appendix 2). All locations were within the latitudinal range of silky sharks, between 42° N and 43° S  (Ebert et al. 2013). Each longline of three to five mid-water BRUVS was considered and analysed as a single deployment given that the number of rigs per set varied and because the rigs on a given set are not statistically independent of each other  (Hurlbert 1984). For each deployment, video analysts identified all observed individuals to the greatest taxonomic resolution possible, estimated the relative abundance of each taxon recorded as the maximum number of individuals in a given frame (MaxN), and measured individual fork lengths (FL).