dataesr/scientific-paragraphs-categorization huggingface dataset:
Animal This study considered the individual data from 798 cattle (233 cull cows and 565 young bulls). The cull cows were represented by four pure breeds (Aubrac, Charolais, Limousin and Salers) and the young bulls by five pure breeds (Aubrac, Blond d'Aquitaine, Charolais, Limousin and Salers). Charolais (37. 1%), Limousin (27. 2%) and Blond d'Aquitaine (12. 2%) breeds are the three first beef breed in France. The Charolais breed is the first European beef breed with 25% of the cows in Europe. The two others are also well represented in Europe and in the world. The two French hardy breeds, Salers (5. 3%) and Aubrac (4. 9%), are also well represented in Europe and in the world, especially in extensive breeding areas (France G en etique Elevage 2019; Interbev 2019; Institut de l' elevage and Conf ed eration Nationale de l'Elevage 2017).
Slaughtering and carcass traits All animals were slaughtered at the experimental slaughterhouse of INRA at the Auvergne/Rhône-Alpes research Centre (Theix, France). The animals were Table  1. Description of the data set used: the sources of all of the data with the experiments used (Exp), the number of animals (n), the breed, and the variation factors studied for the cull cows and the young bulls. slaughtered in the same conditions in compliance with the ethical guidelines for animal care. Cattle were slaughtered by stun using a captive bolt pistol follow by exsanguination. The Achilles method was used to suspend vertically carcasses. The electrical stimulation was not used in this slaughterhouse. In this study, only the fatness and the conformation scores were considered. These both carcass traits in the EUROP system were graded visually by trained staff (European Commission 2006).
Characterisation of fatness and conformation score clusters In our data set, some EUROP classes of fatness and conformation scores were few or not represented, in both animal categories (Figure  1 ). As the category of animal have an effect on these two carcass traits, the cull cow and young bull data were analysed separately. To establish valid prediction models, it was important to have at least 30 individuals per class for each carcass trait  (Saporta 2011). Otherwise, the class was not considered. According to the distribution of the carcasses in fatness score of EUROP system, only some fatness score could be considered in our study (Figure  1 ). For cull cows, the fatness clusters considered were numbers 3 and 4 (respectively, Fat-3 and Fat-4). For young bulls, the fatness clusters considered were 2 and 3 (respectively, Fat-2 and Fat-3).
Multicollinearity test between the independent variables The independent variables used were the rearing factors measured during the fattening period, in the prediction models established in our study. To have a robust model, the first step was to test the multicollinearity between the rearing factors, before the establishment of the prediction models. For each calibration data set obtained, a linear discriminant analysis (LDA) was realised to select among the 11 rearing factors presented previously, those discriminating the most the fatness and conformation scores clusters, for each category of animal. The rearing factors which had a sufficient discriminant power on the LDA's axes (coordinates > j0. 2j) were retained. After this first selection, the multicollinearity between the rearing factors was evaluated from the variance inflation factors (VIF) values  (Tuff ery 2012).
Establishment and interpretation of the prediction models For each category of animal (cull cows, CC and young bulls, YB), prediction models were developed to predict the fatness and conformation clusters (FAT and CONF) from the rearing factors selected (Table  4 ). Finally, four prediction models were developed: FAT-CC, FAT-YB, CONF-CC and CONF-YB, using the different calibration data sets. As the fatness and the conformation clusters were qualitative variables, the linear regression could not be used, therefore the logistic regression was used. In logistic regression, the model predicted the probability of the cattle carcass to move from a reference cluster (Clust-Ref) to a superior cluster of the predicted variable (e. g. fatness or conformation). For each prediction model, a Clust-Ref was determined and allowed to explain how to increase carcass fatness and conformation scores.
For the logistic regression, the logit model was used  (Rakotomalala, 2014)  and was written as follows: log p=1 À p ð Þ¼ a þ b i X i þ e p ¼ P Y ¼ 1 ð Þ where p 20; 1½ in which, a and b i are fixed effects of the model, X i are the rearing factors i, e is the model error, p is the probability that Y (the predicted variable) is equal to 1. According to the number of clusters of the predicted variable, two logistic regressions were used: The binomial logistic regression is used when the predicted variable has two clusters. In our study, three prediction models were established with this statistical method: FAT-CC, FAT-YB and CONF-CC and their Clust-Ref was: Fat-3, Fat-2 and Conf-Mean, respectively. The binomial logistic regression was realised from the 'lmerTest' package in R  (Kuznetsova et al.
The accuracy calculation was the number of carcasses from cluster i predicted in the cluster i divided by the number of carcasses predicted in the cluster i. For the models realised with the binomial logistic regression, the area under the receiver operating characteristic curve (AUC) was measured with the 'ROCR' package  (Sing et al. 2005)  in R. The AUC indicated the discrimination quality of the model being close to 1  (Nakache and Confais 2003; Rakotomalala 2014).
The conformation model for the cull cows allowed a better discrimination of the conformation clusters than for the young bulls with a success rate in the validation data set of 79. 7% in the cull cows and 61. 6% in the young bulls. According to the OR values, a variation of each rearing factor considered in the CONF-YB model allowed to improve the carcass conformation score (Table  5 ). If the FCE of the young bulls increased by 0. 05 kg kg À1 DM, the carcasses had (3. 4 Â 10 6 ) 0. 05 ¼ 2. 12 chance that their conformations scores were improved. If their DMI Table  6. Description of the coefficients and the odd ratios (OR) of the fatness score (FAT) and the conformation (CONF) prediction models from the calibration data sets for the cull cows (CC) and the young bulls (YB). In all prediction model, the "breed" and "experiment" factors were placed as random effects on the intercept.
2. Steps 6 and 7 began in Nairobi, where experts presented their own findings on how the technological developments were creating change in the specific element of the SES their research studied. The elements on which we focus in this paper fulfill the following criteria: (a) change has already begun to occur, (b) we have data to begin to analyze, and (c) these changes pose the most severe potential impacts on livelihoods. Appendix S1 outlines the disciplinary expertise of the workshop participants and authors, and the datasets used by each to inform the expert elicitation and scoping review. There are elements of the SES where data do not exist-these are highlighted in the conclusion. Given that Gibe III is the most significant major technological development in the region, the majority of data relate to this intervention.
Once written, the individual elicitations were compiled by Hodbod and Stevenson and then the paper draft was sent for iterative editing by the authors until there was consensus on the presentation of data and the equity analysis presented within the discussion. The results section is organized as shown in Fig. 2. After describing the intervention and study context, we present data on impacts on the ecosystem, and impacts on social systems. The final sections summarize these findings in light of a socialecological systems framing, and appraise the distribution of costs and benefits among stakeholders.
Hydrology The Gibe III dam has permanently dampened seasonal flow variability in the Omo River, eliminating the annual flood pulse. Assessing the impact of Gibe III has been complicated by the lack of flow data for the Omo River, last measured by the Ethiopian Water Resources Authority in 1980  (Avery 2009). Hence, hydrological studies of the Basin have made use of satellite datasets to model the water balance  (Avery 2009 (Avery , 2012a, b;, b; Velpuri and Senay 2012). Before the construction of the dam, the Omo had a fluctuating flow rate and flooded annually between July and October, and Lake Turkana water levels fluctuated between 1 and 1. 5 m each year  (Avery 2012a). The Gibe III reservoir began to fill in February 2015 and in October 2015, the reservoir level reached 200 m (the minimum level to begin power generation), 40 m below the dam's crest (Salini Impregilo 2016).
Only in this way are realistic pathways likely to be found that will result in sustainable and equitable solutions to current challenges. Given the pace at which change is occurring, such policies need to be developed and implemented as soon as possible. Acknowledgements This material is based on the work supported by the National Science Foundation under Grant No. 1561106. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. The authors are grateful to the participants of the Oxford workshop in July 2015 for their initial advice in the scope of a research program in this region, and to the British Institute East Africa for hosting the Nairobi 2016 workshop.
Material and Methods A GPU-accelerated MC tool FRED (Fast paRticle thErapy Dose evaluator; Schiavi et al. 2017) was developed at Sapienza University of Rome and is investigated at Krakow PBT centre. FRED was already validated against FLUKA and GEANT4, as well as clinical data from CNAO facility. The depth-dose distributions (DDDs) of single pencil beams measured in water phantom and lateral beam profiles in air for 17 energies in 70-226. 1 MeV range were used to build a FRED dedicated phase space library. The validation procedures included QA measurements of spread out Bragg peak (SOBP) of different range in water and comparison of more than 182 measurements of Krakow patient treatment fields. The gamma index (GI) analysis were used to evaluate the dosimetric results of MC calculations.
Mitral valve repair for non-ischaemic mitral regurgitation Data regarding RMA for MR due to non-ischaemic cardiomyopathy are limited. Observational studies report improved New York Heart Association (NYHA) functional class, better quality of life and LV reverse remodelling after RMA  [30] [31] [32] [33]. Much information regarding the effect of RMA in non-ischaemic cardiomyopathy has been obtained from the Acorn trial  [34, 35]. Primary objective of this RCT was to examine the effect of an external cardiac support device (CSD). The trial enrolled 300 patients with non-ischaemic cardiomyopathy and heart failure into a no MV surgery (n = 107) or MV surgery stratum (n = 192), based on the presence of significant MR. Patients in the MV surgery stratum were then randomized to MV surgery + CSD (n = 91), or MV surgery alone (n = 102).
Results in North America  (Talluto et al. , 2017)  showed that metapopulation processes captured potential future range shifts for most tree species. García-Valdes et al. (2013)  inferred potential changes in species distribution in Spain, but we still lack studies that explore this mechanism at the European scale, covering a larger portion of different species distribution. Here we propose to analyze how local species extinction and colonization probabilities vary within the range of the the main European tree species across the entire continent using more than 80 000 plots of national forest inventories. Species distributions are summarized by occurrence probability estimated with an ensemble SDM fitted to independent data extracted from the EU-Forest data set  (Mauri, Strona, & San-Miguel-Ayanz, 2017).
Materials and Methods Our objective is first to test how extinction and recruitment probabilities vary as a function of the SDM derived occurrence probability for the dominant European tree species. Then we derived a potential equilibrium and compared it to current SDM occurrence probability to analyse the relative importance of each of the two processes, and whether it under or overestimates the current occurrence probability. To do this, we first gathered data on tree local extinction and colonization events from national forest inventory plots. Then, we estimated the occurrence probability with SDM models fitted to independent data extracted from the EU-Forest database. Subsequently, we modelled extinction and recruitment probabilities in function of SDM occurrence probability with two observations of occupancy data via a spatially inhomogeneous Markov chain.
NFI datasets To calibrate our model, we required information on the presence/absence of each species at two different dates over a large geographical area to cover, as far as possible, the entire species distributions. We used a database of tree data from the National Forest Inventories (NFI) of Finland, France, Germany, Spain, Sweden, compiled as part of the FunDivEurope project (http://www. fundiveurope. eu, Baeten et al. (2013) ). Inventory protocols differs between NFIs (see Supplementary materials Section 1 for a detailed description of each survey protocol). Surveys were conducted within a circular plot with a fixed radius or in concentric subplots with different radius and minimum diameter at breast height (DBH) for all NFIs except Germany, where an angle-count method (basal area factor of 4 m 2 ha -1 ) was used.
Joint research center -species local frequency The density of NFI plots is too low to accurately describe the local abundance of trees that can disperse seeds into a given plot. Distances between NFI plots are about 1 km or above whereas most dispersal events occur in less than 100 m from the seed source (see for example  Nathan, Safriel, and Imanuel (2001) ,  Bullock et al. (2017) ). To represent seed inputs into a plot, we thus used the species' local frequency (hereafter F JRC ) in the corresponding 1 km cell produced by the Joint Research Center (RPP -Relative Probability of Presence on JRC website (https://forest. jrc. ec. europa. eu/en/european-atlas/atlas-data-and-metadata/), see San-Miguel-Ayanz,  de Rigo, Caudullo, Houston Durrant, and Mauri (2016). Each map estimates the relative frequency of the species based on datasets of field observations as represented in the Forest Information System for Europe (FISE), which integrates National Forest Inventories, BioSoil and Forest Focus data sets.
The presence/absence data are assimilated at a spatial resolution of 1 km based on multiple smoothing kernels of varying dimension. Independent estimations of forest cover extracted from the Pan-European Forest Type Map 2006 (FTYP2006, http://forest. jrc. ec. europa. eu/forest-mapping/forest-type-map) are used to rescale the species frequency by the cover of broadleaved forest, coniferous forest or other non-forest categories based on 25 m x 25 m pixels  (San-Miguel-Ayanz et al. , 2016). We chose this variable because it summarized a very large amount of data on a European scale and can be considered as a strong indicator of the proportion of adjacent plots in which the species is present within a 1 km patch. An explicit representation of seed availability via dispersal mechanisms was beyond the scope of this work because available data do not provide a detailed description of the seed source in the plot surroundings from where most dispersal events occur  (Nathan et al.
SDM We estimated species occurrence probability (hereafter P occ ) on each NFI plot with ensemble species distribution models fitted to the EU-Forest data set  (Mauri et al. , 2017)  which provides species presence/absence on a 1 km grid. The initial EU-Forest data set includes more than 250 000 plots across Europe including countries not present in FUNDIV. We excluded all NFI observations from the EU-Forest to avoid using the same data to estimate both extinction/colonization and the SDM probability of presence. This exclusion was performed to avoid any potential circularity arising from the use of the same data in both analyses. After excluding NFI observations, we retained 9600 data points across Europe, coming from ForestFocus and BioSoil campaigns. For each grid point, we extracted mean annual temperature, precipitation of wettest quarter, temperature and precipitation seasonality from CHELSA climatologies  (Karger et al.
In this case, an extinction probability higher than the recruitment probability would lead to a null value for P eq. Overall, we also found that P eq overestimated P occ , and P eq showed little variations along the P occ gradient. Figure  5 : Equilibrium probability of presence (P eq ) against SDM occurrence probability (P occ ), calculated with closed formulation and varying extinction (E), varying recruitment (R), or both (ER). There is a long history of analyzing the drivers of population distribution, but surprisingly few studies have explored with field data the link between probability of presence and metapopulation processes such as extinction and colonization. Here, using data from national forest inventories, we explored the question at the scale of the European continent for 17 tree species.
grant ANR-16-SUMF-0002). The authors are grateful to Dr. Christian Wirth and Dr. Gerald Kändler for access to the harmonised inventory data. MAZ and PRB were supported grants DARE (RTI2018-096884-B-C32) and FUNDIVER (CGL2015-69186-C2-2-R); MICINN, Spain. The NFI data synthesis was conducted within the FunDivEUROPE project funded by the European Union's Seventh Programme (FP7/2007-2013) under grant agreement No. 265171. We thank the MAGRAMA, the Johann Heinrich von Thunen-Institut, the Natural Resources Institute Finland (LUKE), the Swedish University of Agricultural Sciences, and the French Forest Inventory (IGN).
( grant ANR-16-SUMF-0002). The authors are grateful to Dr. Christian Wirth and Dr. Gerald Kändler for access to the harmonised inventory data. MAZ and PRB were supported grants DARE (RTI2018-096884-B-C32) and FUNDIVER (CGL2015-69186-C2-2-R); MICINN, Spain. The NFI data synthesis was conducted within the FunDivEUROPE project funded by the European Union's Seventh Programme (FP7/2007-2013) under grant agreement No. 265171. We thank the MAGRAMA, the Johann Heinrich von Thunen-Institut, the Natural Resources Institute Finland (LUKE), the Swedish University of Agricultural Sciences, and the French Forest Inventory (IGN).
Methods For global analysis, we used Lab Times online data (labtimes. org) obtained through the Web of Science (Thomson-Reuters, Toronto, ON, Canada) for articles published from 2007 to 2013 (quoted from 2009 to 2015) to analyse original articles. This analysis dealt with the 352 specialty or subspecialty journals listed in the database of the SCImago Journal & Country Rank portal. However, these data do not take into account cardiovascular studies published in multidisciplinary medical journals or the precise rank of the authors. To overcome these limitations, we further analysed all the studies published between 2005 and 2015 in cardiovascular or multidisciplinary journals with high impact factors (IFs). This analysis was restricted to the three multidisciplinary journals with the highest The number of cardiovascular clinical research projects was analysed by using the ClinicalTrials.
gov database and the French healthcare site for government-funded projects (https://solidarites-sante. gouv. fr/systeme-de-sante-et-medico-social/recherche-et-innovation/linnovation-et-la-recherche-clinique/appels-a-projets/article/les-appels-a-projets-de-la-dgos-les-projetsretenus). For the ClinicalTrials. gov site, the projects list was obtained by requesting: ("Aortic disease"or "Heart disease"or "Cardiac disease"or "Cardiovascular disease"or "Valvular disease") between 01 January 2008 and 31 December 2017, for each country of interest. For the government-funded projects, all data between 2008 and 2017 were obtained via the dedicated website. All projects were classified by the research topic, with 19 topic categories identified (Appendix B). In case of doubt regarding the best category for classification of a project (frontiers form between two specialties), the evaluation was performed by two reviewers (B.
Analysis of the ClinicalTrials. gov database The ClinicalTrials. gov database lists the registries and clinical trials launched in each country. Between 2008 and 2017, there were 23,725 studies in the cardiovascular field, including both interventional and observational studies. The evolution of the number of studies submitted per year is illustrated in Fig. 5A  for the world and for France. There has been an increase in the annual number of declared studies on ClinicalTrials. gov over the past 10 years, from 1797 in 2008 to 2721 in 2017 worldwide; this evolution was similar for French data, with 173 studies registered in 2008 and 245 in 2017. French participation accounted for approximately 9% of cardiovascular research, with a minimal rate of 7. 6% in 2012 and a maximal rate of 10. 1% in 2015 (Fig.
Discussion Clinical research data in the CVD field over the last decade in France show contrasting results. France is very well represented in terms of new CVD projects, whether government-funded projects (HPCR) or worldwide registered projects (ClinicalTrials. gov database). Conversely, actual French scientific production scored poorly and has tended to decline, as illustrated by abstracts presented to the world's largest cardiovascular congress (the ESC congress), and especially by published articles in top journals. Scientific production is mainly assessed by the publication of original articles, in highranking international journals. Although IFs have been vigorously challenged in recent years  [10] , and may indeed be an imperfect metric, they remain the means by which most journals are judged  [11].
The question is to know how long CVD researchers will maintain this high quality of research without appropriate funding, and how to prevent a decline in competitiveness compared with other specialties and other countries that are increasing their financial support for CVD research  [27]. The place of France in CVD research has been great and, with some changes in public policies, more golden years are yet to come. Indeed, as presented in this study, France has the ideas and the energy to produce high quality research, and even ranks first in Europe in terms of the number of cardiovascular projects registered online on the ClinicalTrials. gov website.
French culture: French language and underexploitation of databases The vitality of CVD research, as illustrated by the number of projects launched, does not translate sufficiently enough into high-ranking publications. In addition to the funding limitations detailed in the previous section, French cardiologists do not have a culture of intensive database exploitation. Indeed, whereas large databases in the USA and Sweden, for example, lead to high numbers of publications, most French studies give rise to only one to five publications. The FAST-MI registry should be regarded as exemplary, with more than 50 publications in international journals  [22]. This approach should be taught to researchers and championed by the leaders of CVD research in France. Lastly, in addition to the different barriers listed previously to France being better represented in global CVD research, the French face another limitation compared with other European countries: the inability to speak fluent English.