dataesr/scientific-paragraphs-categorization huggingface dataset:
DAPSTOM data set Throughout 2013, EURO-BASIN partners submitted data sets to the lead author of this paper and these were reformatted into the required DAPSTOM relational tables (Fig. 1 ). Data sets made available as part of EURO-BASIN can be summarized as follows. It is clear that the vast majority of the blue whiting data within the DAPSTOM database were collected in recent years, and explicitly for the purposes of EURO-BASIN (1367 records out of 1583), whereas this was not true for herring or mackerel (Table  1 ). It is also apparent from Tables  1  and 2  that for blue whiting and mackerel the number of database records exceeded the number of stomachs examined, confirming that the data were largely non-pooled records from individual stomachs whereas this was not true for herring where 8508 database records were derived from 27 746 stomachs.
The primary explanation for this disparity is the digitization of "pooled" herring data sets from a historical report by  Hardy (1924) , but also "pooled" data from  Brook and Calderwood (1886)  and  Scott (1924). Table  2  shows the number of records and samples by geographic area (Fig. 2 ), including all larval and juvenile fish. From this table it is apparent that herring, blue whiting and mackerel have been sampled over a huge geographic area, spanning from the Bay of Biscay ( ∼ 43 • N), to the high Arctic (∼ 73 • N) and from Greenland in the west (∼ 29 • W) to the Lofoten islands in the east (∼ 9 • E). By contrast, the very limited number of records for albacore and bluefin tuna in the DAPSTOM database were derived from opportunistic sampling in the English Channel and North Sea. The earliest of the data included in the DAPSTOM/EURO-BASIN data set is a single record of an albacore tuna stranded on the Channel coast of England in August 1864, whereas the most recent of the data comes from a single bluefin tuna stranded at Ventnor, Isle of Wight in August 2012.
AZTI-Tecnalia data set The tuna stomach database from AZTI-Tecnalia corresponds to 7 years of sampling from 2004 to 2011. Due to the absence of continuity in the different projects dealing with the feeding ecology of tunas, the sampling could not be performed every year for both species, and no samples were collected in 2008. The lack of temporal continuity is more apparent for bluefin tuna than for albacore. However, a total of 1525 albacore and 686 bluefin tuna stomachs were collected during the study period (Table  3 ).
Discussion A major limitation of the DAPSTOM data set is that it includes a mixture of "pooled" information as well as data collected from individual fish. Sometimes only information on the number of stomachs containing a particular prey item was available (i. e. "frequency of occurrence"), rather than the actual number of a particular prey item. Hence in any data extraction, outputs should be viewed as providing information on the "minimum number" of prey items consumed. This would have little impact in predator species that consume large prey items one at a time (e. g. fish feeders), but also with regard to most of the newer data sets assembled under EURO-BASIN. However, it could be that in certain older data sets, the total number of prey items in plankton-eating species such as mackerel, herring and blue whiting would be underestimated.
An example would be the historical data set containing mackerel stomachs off the Cornish coast from Bullen (1908) a component of the "Cefas Historical" records cited in Table  1 , as well as the herring data sets digitized from  Marshall et al. (1937 Marshall et al. ( , 1939)). A further limitation of the DAPSTOM database is paucity of information on prey weights. In many of the constituent data sets no gravimetric information was provided. A consequence of this is that it can be difficult to judge the importance of a particular prey item to the overall nourishment of the predator, since a mackerel for example, may draw significantly more nourishment from eating a single fish in comparison with 1000+ copepods. To remedy this situation, we plan to develop an updated "PREY" table (see Fig. 1 ) that includes average prey weights, and perhaps energy density for each standardized prey type so that numbers consumed can be converted to total weights.
It would be very desirable to include information on prey availability within the database, however at present this is not feasible. The data included span more than a century and in most cases the scientists involved did not collect zooplankton information at the same time as sampling fish stomachs. Unfortunately it is not possible to go back in time and supplement the data collected. For the most recent data on herring, blue whiting and mackerel, one might try to match the sample location, dates and times with corresponding information from the North Atlantic-wide Continuous Plankton Recorder survey (CPR, see  Edwards et al. , 2014) , but this would be a complicated task to achieve and would require a separate research paper/analysis. For some of the earlier data sets (included in the "Cefas historical" sub-set), plankton data were collected, but these were largely qualitative in nature and not digitized as part of the DAPSTOM initiative.
A global atlas of marine macro and meso-plankton data was published in Earth System Science Data in 2013 (see  Moriarty and O'Brien, 2013; Moriarty et al. , 2013)  and hence these data sets could be used together with CPR data  (Edwards et al. , 2014)  to characterize prey availability. Albacore diet composition in the northeast Atlantic has been reported as being dominated by small, mesopelagic fish, e. g. Maurolicus muelleri (pearlside) and Scomberesox saurus (Atlantic saury), as well as euphausiids and hyperiid amphipods  (Pusineri et al. , 2005; Goñi et al. , 2011). In the AZTI data submitted to PANGAEA  (Goñi, 2014)  similar patterns were observed, with euphausiids being dominant and the most ubiquitous prey in albacore. Albacore diet displayed high plasticity with important spatial variability, both latitudinally and in terms of oceanic vs.
As with albacore, the data suggest a decrease in blue whiting consumption in the most recent years, following a decrease in recruitment  (Payne et al. , 2012). Atlantic saury, possibly because of the effect of SST on their distribution, were not as prevalent in bluefin tuna diet as they were in albacore diet, as bluefin tunas were exclusively sampled in the southeastern part of the Bay of Biscay. However, the results did suggest a higher proportion of saury in bluefin diet in 2011, when it seemed to partly replace anchovy as a high-caloric prey. Bluefin tuna used to be distributed in the North Sea, from where it disappeared in the 1960s. It is thought that in the past they fed primarily on herring and on mackerel in this region  (Tiews, 1978). Occasional individuals have been recorded in the North Sea and around the British Isles in recent years.
The total biomass of albacore in the North Atlantic has been estimated at 76 000 tonnes (in 2012) and bluefin tuna at 285 000 tonnes  (ICCAT, 2014). Within EURO-BASIN, the data sets described in this paper will be used to calculate overall predation pressure exerted by the various species. The longer-term aspiration (through making these data sets available via PANGAEA and DAPSTOM) is that this action will facilitate the construction of more realistic ecosystem or multispecies fisheries models that can subsequently be used to provide multispecies advice, as now mandated by international conventions and EU Directives.
1. during albacore acoustic tracking surveys (summer 2005), albacore dummy archival tagging surveys (summers 2005 and 2006), albacore archival tagging survey (June 2010), bluefin tuna tagging surveys (2009, 2011, 2012). In the case of these samples the location and the hour of each capture was included in the database. 2. through collaborating recreational fishermen in the southeastern Bay of Biscay in 2005, 2006 and 2007.
Acknowledgements. Research leading to these results received funding from the  European Commission   FP7  project  EURO-BASIN (European Basin-Scale Analysis , Synthesis, and  Integration , Grant Agreement  264 933 ). Development of the DAPSTOM data portal was initially supported through a  "data rescue" grant  from the  EU   Network of Excellence programme "EurOceans ". Further iterations were supported by the  UK Department for Environment, Food and Rural Affairs  (specifically contracts  MF1202  and  MF1109 ). AZTI tuna stomach sampling was funded through four research projects supported by the  Basque Government (Agriculture and Fisheries Department ). Edited by:  G. Melvin.
Since 1992, a national working group on goat reproduction was constituted including all stakeholders in the dairy goat industry. This has led to the improvement of the reliability of data from the milk recording national database. However only about 10% of the French goat population are inseminated while this proportion is about 90% for dairy cows and 40% for dairy sheep  (Leboeuf et al. , 2012). Thus development efforts are still necessary to improve diffusion from the nucleus to the base population. Identifying the genetic and non genetic factors influencing AI success is the preliminary step for this. The purpose of this study was to estimate genetic and environmental factors affecting this trait in combining all available information from male and female  (David et al. , 2008).
Data Data came from the national goat database (CTIG, Centre de Traitement de l'Information Génétique, INRA, Jouy-en-Josas, France). Data used in this study were the recorded results of 584 676 AI in Alpine breed and 386 517 in Saanen, performed between 1992 and 2009. The average number of AI per female over the whole career of the female was 1. 74 and 1. 62 for Alpine and Saanen breed, respectively. AI result was a success (Y = 1) if kidding occurred within the range of 140 to 160 days after insemination, otherwise it was a failure (Y = 0). Only one attempt of AI was made per campaign for a given female. The environmental effects tested in the analysis were: flock within year of AI; period within year of AI, which was established by dividing each year into unequal periods of time with the constraint of minimizing the fertility variation within period, maximizing the variation between the periods and getting sufficient observations in each cell, to make the results easier to read, modalities of this factor were labelled with the year and the name of the month corresponding better to the period; month of AI with nine levels (all records from January and February, representing 0.
The age of female was not included in the model because of its strong correlation with LACTN. All records concerning nulliparous female, representing 3% of the data, were discarded because of interaction between physiological characteristics of young does and AI success  (Houdeau et al. , 2008). The number of animals in the pedigree files was 485 734 for Alpine and 359 999 for Saanen breed. A summary of the data is given in Table  1. Data analysis and model There were very few flocks that had both breeds in the database, this led to confounding between breeds and flock within years random effects. Moreover our main objective was to estimate genetic parameters, therefore we conducted separate analysis on each breed. A linear repeatability animal model was used to estimate variance components and fixed effects parameters.
The specification of the full fixed effect model was: After selection of fixed effects, variance and covariance components were estimated using Restricted Maximum Likelihood, implemented in ASReml software  (Gilmour et al. , 2006). Heritability was estimated by σ 2 a m =σ 2 t and σ 2 a f =σ 2 t for male and female fertility respectively, repeatability was estimated by ðσ 2 a m + σ 2 p m Þ=σ 2 t and ðσ 2 a f + σ 2 p f Þ=σ 2 t for male and female fertility, respectively, with σ 2 t ¼ σ 2 a m + σ 2 p m + σ 2 a f + σ 2 p f + σ 2 ε. Estimated means were calculated by forming linear function of a subset of fixed effect coefficients weighted by the number of observations included in each cell of the subset. This was done to obtain more realistic estimated means.
The relation between protein yield and fertility for very short PPI (Figure  3 ) was also clearly negative. So both observations could suggest that QPY could be related to pre-partum body condition score which is known to affect fertility  (Walsh et al. , 2011). This relation could be positive for multiparous female and negative for younger females because of differences in energy balance, as demonstrated on cows by  Friggens et al. , 2007. The effect of modalities of treatment for induction and synchronization of oestrus was important despite the difficulties to gather reliable information and the imperfection of the database structure that did not allow the declaration of simultaneous treatments (such as the combination of light and hormonal treatment which is known to be practised on farm).
To explain these results an investigation among farmers practicing this treatment would be needed, and also the information stored in the database should be made more accurate. The effect of post partum interval was in interaction with QPY but the levels 180 + to 210 or 210+ to 240 were always the best at any level of the QPY factor. Then those levels should be strongly recommended when selecting animals for AI. Similarly the levels <150 or >240 were the worst, animals with such levels should be set aside from AI procedure. The effect of number of lactation was in interaction with QPY for both breeds and with TREAT for Saanen breed. Nevertheless we observed an important decrease in fertility when number of lactations (>2) increase for all levels of QPY and TREAT except for ht3 and ht4. The evolution of buck management (i.
However the information related to the ejaculate (collection date, initial volume and concentration, in vitro quality…) used for AI is not yet available in the database. These shall be recorded in the database in the future for us to adjust a more precise model. The range of variation between extreme female breeding values was 47% and 53% for Alpine and Saanen breeds, respectively. Genetic improvement of AI success could therefore be useful. Similarly the range of variation of estimated value of flock × years random effect was 70% and 65% in Alpine and Saanen breeds, which means an effort to advise the farmers could also be very useful.
Search Terms We used the PubMed database to identify publications for the following terms: for RUCAM, and around 6200 hits were provided; for Roussel Uclaf Causality Assessment Method. 4350 hits; for CIOMS, 191,000 hits; for Council for International Organizations of Medical Sciences, 7,080,000 hits; for the combination of RUCAM/CIOMS or CIOMS/RUCAM, each 2890 hits; for RUCAM DILI, 11,400 hits; for CIOMS DILI, 6080 hits; for RUCAM HILI, 1680 hits; and for CIOMS HILI, 1900 hits. This search revealed that DILI is more often associated with RUCAM and HILI with CIOMS. Since liver injury relates more often to DILI rather than to HILI, and due to historical background and the original work with its validation, we suggest the term RUCAM should be used solely in the future in connection with both DILI and HILI, dismissing the term CIOMS for reasons of clarity and simplicity.
Data Extraction We used primarily our large and actualized private scientific archives, which contain original full-length publications relating to RUCAM, CIOMS, DILI, and HILI. In addition, the search for additional publications was extended through the PubMed database. Prior to our analysis, the publications were assessed regarding their scientific and clinical quality. Publications of relevance and good quality were preferred and considered for evaluation. The focus of our search was on publications in English language, but relevant reports of other languages were also considered. The retrieved publications included case reports, case series, and review articles; they were analyzed to assess whether they were appropriate and relevant for the topic of this article. Publications were also manually searched for additional publications not yet identified.
A General View Back to the Original RUCAM Historically, the development of RUCAM goes back to the late 1980s, partially based on results of international consensus meetings of experts as documented by various reports  [37] [38] [39] [40]  and reviewed recently  [36]. Establishing RUCAM with all core items and specific details was time-consuming and required some years as RUCAM was the first liver-specific causality CAM for DILI ever published worldwide  [8, 9]. Originally, the criteria of causality assessment and their weight can be traced back to a French method for general drug reaction assessment that was not liver-specific and merely qualitative  [37]. This original French method considered criteria under both chronological and clinical aspects. The chronological criteria included three datasets: time to onset of the reaction, the clinical course after cessation or continuation of the drug, and the response to readministration.
These criteria were included in a diagnostic instrument for causality assessment but were fine-tuned, other criteria were added, a score was ascribed to each criterion depending on the type of the liver injury, and finally the method was validated with cases including positive rechallenge as a gold standard. Those who then applied this final scale  [8, 9]  called it either RUCAM or CIOMS in their publications  [33] [34] [35] [36]. There was some preference for the term RUCAM in the United States, whereas experts in Europe and the other countries preferred the term CIOMS. Rather than using both terms interchangeable as in the past, we now prefer for the future only one single term, namely RUCAM, as this is also the most quoted method for DILI in the PubMed database. Consequently, the improved RUCAM version we now present is the updated RUCAM.
These criteria were included in a diagnostic instrument for causality assessment but were fine-tuned, other criteria were added, a score was ascribed to each criterion depending on the type of the liver injury, and finally the method was validated with cases including positive rechallenge as a gold standard. Those who then applied this final scale  [8, 9]  called it either RUCAM or CIOMS in their publications  [33] [34] [35] [36]. There was some preference for the term RUCAM in the United States, whereas experts in Europe and the other countries preferred the term CIOMS. Rather than using both terms interchangeable as in the past, we now prefer for the future only one single term, namely RUCAM, as this is also the most quoted method for DILI in the PubMed database.
[56] , for the WHO from the WHO database  [57] , and for the ad-hoc approach from Kaplowitz  [58]. The symbol + shows that this specific item is published, and the symbol 0 indicates lacking publication. Abbreviations: ALT: Alanine aminotransferase; ALP: Alkaline phosphatase; CMV: Cytomegalovirus; EBV: Epstein Barr virus; HAV: Hepatitis A virus; HBV: Hepatitis B virus; HCV: Hepatitis C virus; HEV: Hepatitis E virus; HSV: Herpes simplex virus; VZV: Varicella zoster virus.
Conclusions The major strength of the updated RUCAM is its potential as a standard scale for DILI and HILI to assess causality by attending physicians, regulatory agencies, expert panels, and the scientific community. It provides a straightforward application in liver and hepatotoxicity specific domains with scored items. This allows data comparability and transparency, reassessment by scientists other than the reporter of the case, and discussions among experts. Each case can individually be assessed and receive a final causality score for each suspected synthetic drug, herb, or dietary supplement. The updated RUCAM is improved by providing a better definition of the elements to take into consideration and more accuracy in data elements to assist the exclusion of alternative causes.
Acknowledgments I would like to thank  E. Kiritsis  for advice during this project and  F. Sannino  for explanations of the mass dependence of the S-parameter. In addition, I would like to thank  D. Albrecht ,  T. Alho ,  F. Aprile ,  B. Goutéraux ,  P. Hoyer ,  T. Ishii ,  K. Kajantie ,  W. Li ,  T. Morita ,  F. Nitti ,  M. Panero ,  C. Pica ,  C. Rosen ,  R. Shrock ,  K. Tuominen , and  R. Zwicky  for interesting discussions, relevant comments, or correspondence. This work was partially supported by  European Union 's  Seventh Framework Programme  under grant agreements ( FP7-REGPOT-2012-2013-1 ) No  316165 ,  PIF-GA-2011-300984 , the  ERC Advanced  Grant  BSMOXFORD 228169 , the  EU program Thales   MIS 375734. It was also co-financed by the  European Union (European Social Fund, ESF)  and  Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF)  under "Funding of proposals that have received a positive evaluation in the 3rd and.
HV studies include a greater number and wider demographic spectrum of patients than are feasible in randomized controlled trials  [1] [2] [3]  and provide additional data about the safety profile of these products. The World Health Organization has issued a recommendation for HV programmes specifically in the field of blood transfusion safety to 'identify and prevent occurrence or recurrence of transfusion-related unwanted events  ' [4]. The European Haemovigilance Network also recommends surveillance for AEs after transfusion of labile blood components  [5] [6] [7]. After centres began routine production of PCT-PLT, Cerus implemented an Internet-based, active HV programme to prospectively collect additional safety information. In this study, each PCT-PLT transfusion was reported and documented regardless of whether an AE occurred or not.
Study design This was a prospective, non-randomized, one-arm, observational, HV study documenting all transfused PCT-PLT components (Table  1 ) in participating centres. Data presented here represent the total HV experience from three studies: HV1 [10], HV2 [9] and HV3 from inception in October 2003 to December 2010. The objective of the study was to determine the proportion of transfusions associated with an acute transfusion reaction (ATR) following administration of PCT-PLT. There were no randomization requirements, no inclusion criteria other than the need for PLT transfusion and no exclusion criteria of patients. The study was conducted in accordance with the European regulations governing clinical investigations of medical devices and the International Conference on Harmonization Guideline for Good Clinical Practice E6 (CPMP/ICH/135/95).
Study report forms Participating centres used a standardized data capture form to record patient characteristics, primary diagnosis, indication for transfusion and clinical observations of AEs  [10]. The data collection form was designed based on HV report forms already in use in Europe  [11]. A data and safety monitoring board (DSMB) approved the HV report form before utilization and provided oversight of the study. Trained HV personnel from the blood centres recorded patient characteristics and demographics, platelet component characteristics, transfusion events and AE documentation following the transfusion. A checklist format was used to capture clinical AE symptoms and signs for each transfusion. The checklist documented the presence of fever, chills, cardiac arrhythmia, hypotension, pruritus, urticaria, skin rash, jaundice, pulmonary oedema, bronchospasm, dyspnoea, tachycardia, respiratory distress, nausea, vomiting, lower back pain, chest pain, abdominal pain and shock.