dataesr/scientific-paragraphs-categorization huggingface dataset:
These fractions are never in contact with water and are non-exchangeable which can lead to an overestimation of the Kd especially when the particle size increases. To evaluate how our datasets could be impacted by the above effect, we assumed that the theoretical decreasing in Kd values as a function of particle size is maximal in the case of an anthropogenic element (zero concentration inside the particles) and physically limited by the effect of particle size  (Abril and Fraga, 1996). The decrease of the Kd as a function of the radius r is faster for anthropogenic nuclides when the concentration of the element inside the particles is negligible. For natural nuclides, this decrease is attenuated when the nonexchangeable concentrations of the elements inside the particles are not negligible.
At the limit, for highly soluble natural nuclides, the apparent Kd can increase as the particles radius increases  (Abril and Fraga, 1996). In our Kd datasets the particle size is not reported but, as stated above, it can be assumed in the field that this parameter is positively correlated to the M/V ratio. This assumption made it possible to apply this analogy to assess the representativeness of our Kd datasets for deriving Kd data for natural elements. We assumed that the Kd value decreases as the M/V ratio increases, and that this decrease is more rapid when the non-exchangeable fraction of the element inside the particle is low and/or the element is almost insoluble. It can then be assumed that the Kd datasets for SS in the field that are most impacted by the use of natural elements are the datasets associated with the weakest decrease in their Kd values as the M/V ratio increases.
Considering Equation 3, the rate of this decrease is mainly dependent on parameter b and it can be assumed that the lower this parameter, the less the dataset is affected by the use of data for natural elements. This is consistent with Figure  5  which shows that the correlations between Equation 3 and the datasets increase when the values of parameter b decrease. The elements in group  [2]  are Ba, Cd, Cs, Mg, Pb, Sr, Ti, V and Zn. The Kd datasets of these elements for SS in the field can be potentially affected by the use of natural elements. Thus, applying these distributions for anthropogenic releases could lead to an overestimation of the Kd values. In Table  2 , the Kd distributions of these elements for SS in the field are labelled Field  [2].  Field[3]: -0. 4 < b < 0 or R 2 < 0. 2 The elements in group [3] are Co, Cr, Na, Ni, Ra and Sb.
The Kd datasets for SS in the field of these elements could be significantly impacted by the use of natural elements. In Table  2 , the Kd distributions of these elements for SS in the field are labelled Field  [3].  Field[4]: 0 < b The elements in group  [4]  are Ce, La and Pr. The Kd datasets of these elements showed an increase in the Kd values when the M/V ratio increased which is typical of the behavior of natural soluble elements under environmental conditions. Consequently, the Kd distributions presented in Table  2  for the elements of group  [4]  were representative of environmental conditions and could not be directly applied for a scenario of anthropogenic releases without explicit acceptance that there was probably an overestimation of the Kd. In Table  2 , the Kd distributions of these elements for SS in the field are labelled Field  [4].
Using this approach, the distinction between DS and SS allowed the derivation of more realistic Kd values for each compartment and reduced the variability of their Kd distributions. However, as for Kd of SS in the field, the use of Kd data including data of natural elements for DS in the field required an assessment of the representativeness of these Kd datasets. Continuing the approach applied to SS (see 4. 1), this assessment was based on the ratio between the GM of Kd for DS in the field with the value given by Equation  2 where the M/V ratio of superficial sediments was assumed to be close to 650 g/L, which corresponds to a mean porosity of 0. 75. Assuming a link of decreasing Kd values when the M/V ratio increases, it was considered that the representativeness of the Kd dataset for DS in the field was identical to that of the Kd dataset for SS in the field when this ratio was lower than or equal to 10.
A ratio greater than 10 indicated a significant increase in the Kd value when the M/V ratio increased and suggested the need to consider a classification with one step higher than that used for the Kd dataset for SS in the field. The approach was only applied to elements for which the dataset allowed determination of statistical distributions for both SS and DS in the field (Figure  8 ): As, Cd, Co, Cr, Cs, Cu, Fe, La, Mn, Ni, Pb, Po, Ra, U, Zn. The method could not be applied to elements (Ag, Ba, Ca, Mg, Mo, Sb, Se, Sr, Th, Ti and V) which reported both SS and DS field Kd, where one or both had a classification of Field[0] in Table  2. The ratio was lower than 10 for As, Cd, Co, Cr, Cu, Fe, La, Ni, Ra and Zn. For these elements, the representativeness of the Kd datasets for DS in the field was classified at the same level as that for their Kd dataset for SS in the field.
The ratio was significantly greater than 10 for Cs, Mn, Pb, Po and U. The representativeness of the Kd datasets of these elements for DS in the field was ranked one level higher than their Kd datasets for SS in the field. The Kd datasets for DS in the field for Dy, Er, Eu, Gd, Hf, Ho and Li were obtained only for natural conditions and they cannot be compared to Kd datasets for SS in the field. Consequently, and by default, their datasets are classified Field[3].
COMPARISON OF Kd DISTRIBUTIONS AS A FUNCTION OF EXCHANGE CONDITIONS This third analysis compared the Kd distributions of DS and SS as a function of the exchange conditions. For DS (Figure  9a ), the dataset was sufficiently large to allow such comparisons for Co (adsorption, desorption, field), Cs (adsorption, desorption, field), Mn (adsorption, field), Pu (adsorption, desorption), Ra (adsorption, field) and Sr (adsorption, desorption). For SS (Figure  9b ), comparisons were possible for Ag (adsorption, desorption), Ba (adsorption, field), Co (adsorption, desorption, field), Cs (adsorption, desorption, field), I (adsorption, field), Mn (adsorption, desorption, field), Sb (adsorption, field) and Sr (adsorption, field). Whatever the component and element, desorption Kd distributions all had higher GM values compared with adsorption Kd distributions.
CONCLUSIONS  Comparisons between Kd distributions of suspended and deposited sediments showed that the Kd values were significantly higher for suspended than for deposited sediments and that the variability was higher for deposited than for suspended sediments. In practice, the separation of these two components enabled more realistic Kd values to be derived and also reduced the variability of the Kd distributions.  In the field, the variability of Kd distributions for suspended sediments can be significantly reduced as a function of the suspended load. For suspended sediments in the field, this parameter explained more than 50% of the variability of the Kd datasets of U, Si, Mo, Pb, S, Se, Cd, Ca, B, K, Ra and Po.  Analysis of Kd distributions as a function of exchange conditions showed that geometric means were systematically greater for desorption than for adsorption while distributions generally overlapped.
Kd distributions in function of environmental components (SS and DS) and conditions of liquid-solid exchange (adsorption, desorption, field) Representativeness of K d datasets for field conditions (see 4. 1 for SS and 4. 2 for DS): Field[0]: no information; Field[1]: relevant for anthropic releases; Field[2]: relevant for anthropic releases with a risk of overestimation; Field[3] and Field[4]: relevant for environmental conditions.
ACKNOWLEDGEMENTS The present work was carried out in the framework of MODARIA (Modelling and Data for Radiological Impact Assessments) programme of the  International Atomic Energy Agency. The authors would like to express their sincere thanks to  P. Ciffroy  which kindly supplied the dataset used to determine Kd distributions published in TRS 472 (IAEA, 2010). Our thanks are also addressed to  M. Vidal  and  D. Kaplan  for their contributions and their scientific support during the  MODARIA program. The  EC  project  STAR  funded plaements of C. Wells at IRSN during part of this study.
The European Virus Archive (EVA) was created in 2008 with funding from the FP7-EU Infrastructure Programme, in response to the need for a coordinated and readily accessible collection of viruses that could be made available to academia, public health organisations and industry. Within three years, it developed from a consortium of nine European laboratories to encompass associated partners in Africa, Russia, China, Turkey, Germany and Italy. In 2014, the H2020 Research and Innovation Framework Programme (INFRAS projects) provided support for the transformation of the EVA from a European to a global organization (EVAg). The EVAg now operates as a non-profit consortium, with 26 partners and 20 associated partners from 21 EU and non-EU countries. In this paper, we outline the structure, management and goals of the EVAg, to bring to the attention of researchers the wealth of products it can provide and to illustrate how end-users can gain access to these resources.
Introduction The European Virus Archive (EVA) was created in 2008 with funding from the FP7-EU Infrastructure Programme, in response to the need for a coordinated and readily accessible collection of viruses that could be made available to academia, public health organisations and industry  (Gould et al. , 2012). Within three years, it developed from a consortium of nine European laboratories to encompass associated partners in Africa, Russia, China, Turkey, Germany and Italy. Following the H2020, 2014 call for Research and Innovation Framework Programme (INFRAS projects) the concept of developing the EVA into a global organization was a natural progression and the project was funded for a four-year period. In April 2015, the EVAg started to operate as a non-profit consortium including 26 partners and 20 associated partners from 21 EU and non-EU countries.
tghn. org/about/). As a future objective, one can imagine the generation of links among all of these organisations to constitute a "Life Science Infrastructure Cooperative" dedicated to virology-related topics, in which the EVAg could play a central role. For the long-term future, we are planning to establish closer collaborative links with other compatible scientific infrastructures by initiating teaching and training programmes amongst younger scientists to ensure that people of the right calibre will be able to undertake the roles currently occupied by experienced but "maturing" scientists. Indeed, one could envisage e-learning programmes being built into diploma courses in universities. As we continue our ambitious mission to build a scientific infrastructure that can benefit scientists operating in all areas of the viral world, we recognise that other agencies and research teams can provide valuable datasets, and analytical expertise from which disease intervention strategies might be developed.
Acknowledgements We wish to thank the following scientists for their expertise and contributions during the successful establishment and development of the EVAg:  C. Baronti  a , L. McElhinney c ,  D. Marston  c , L. Thorne c ,  R. Hewson  d ,  T. Bleicker  e ,  T. Rieger  f ,  M. Korva  i ,  N. Knap  i ,  S. Meschi  j ,  P. Jansen van Vuren  l ,  B. Phahladira  l ,  C. Carbonelle  n ,  S. Carpenter  q ,  A. Nikonova  u ,  F. Deng  v ,  N. Romanenkova 1  ,  S. Becker 2 , M. Saijo  3 , G. Gao Fu 4 ,  A. Lukashev   5 , E. Koray 6 1 St Petersburg Pasteur Institute ,  St Petersburg ,  Russia 2 Institut für Virologie (UMR-DE), Marburg ,  Germany 3 National Institute of Infectious Diseases (NIID) ,  Tokyo, Japan 4 China CDC, Beijing, China 5 Chumakov institute of Poliomyelitis and viral Encephalitides (IPVE), Moscow, Russia, 6 Hacettepe University, Faculty of Medicine (HU), Ankara, Turkey.
QUANTIFICATION AND STATISTICAL ANALYSIS Genotyping experiments 43 F1 males, 14 females and 20 hermaphrodites produced by either APS4 female/APS6 male crosses (denoted ''forward cross'' in Data S1) or by APS6 female/APS4 male crosses (''Reciprocal cross'') were genotyped across the 5 X-linked markers, following the genotyping methodology explained above. The same procedure was used to genotype 24 F2 males, 13 F2 females and 23 F2 hermaphrodites produced by hybrid F1 females crossed with either APS4 or APS6 males were genotyped (denoted as backcrosses in Data S1). Likewise, 21 F2 males, 40 F2 females and 42 F2 hermaphrodites produced by F1 selfing hybrid hermaphrodites resulting from either APS4 female/APS6 male crosses (''Forward cross'') or from APS6 female/APS4 male crosses (''Reciprocal cross'') were genotyped.
Autosomal genotyping of LG4 was performed on 11 F1 individuals (5 males, 3 females, 3 hermaphrodites) resulting from either an APS4 female / APS6 males cross or its reciprocal and 10 F2s produced by selfing hybrid F1 hermaphrodites. Data S1 contains all the information on the individuals and markers genotyped. Failed and ambiguous genotyping is indicated by red and yellow cells.
ACKNOWLEDGMENTS G. D. K. was supported by a  BBSRC  PhD studentship and S. T. by a PhD training grant from  CAPES/CNPq  ( 201116/2014-6 ). A. P. -d. S. was supported by grants from the  National Science Foundation  ( IOS 1122095 ) and  BBSRC  ( BB/ L019884/1 ) and  University of Warwick  start-up funds. D. C. S. was supported by a grant from the  National Science Foundation  ( IOS 1122101 ). We thank  Dr. Sally Adams ,  Dr. Pedro Robles Naharro , and  Dr. James Stratford  for many insightful conversations.
Search strategies A search strategy was devised with input from the research team which was comprised of the six authors. The following databases were to be included: Scopus, Science Direct, Medline (Ebsco), Medline (Ovid), BioMedical Central, ERIC, Ebsco and LISA. The search engine Google Scholar was also used. Grey literature sources searched included ProQuest Dissertations & Theses (UK & Ireland), ProQuest Dissertations & Theses (A&I), University of Limerick Institutional Repository and University College Dublin Institutional Repository and a reference list was also searched for relevant studies. Boolean operators (AND, OR) were used and search terms included "online learning", "distance learning", "medical educators" and "digital literacy". The search was completed over a three month period (May to July 2016) by two researchers independently.
Two searches were completed with different search strings (See Additional file 1) to ensure that all relevant papers were included. Both authors screened titles and abstracts independently into a shared online workbook. Once the initial screening took place authors compared searches to ensure that results were the same. The identified abstracts from each search were combined resulting in an overall number of abstracts to be screened for each database, search engine and grey literature sources. The researchers used the software packages Endnote and Mendeley to organise the citations. This review was completed in a two -stage process; starting with a review of abstracts and titles. Where these met all inclusion criteria, full text articles were sourced and retrieved. All full text articles were then reviewed independently against inclusion and exclusion criteria.
Data appraisal Data appraisal was performed by two researchers using the Critical Appraisal Skills Programme (CASP) Qualitative Research Checklist for qualitative studies  [15]  and National Health Medical Research Council (NHMRC) Appraisal Evidence Matrix for mixed methods and quantitative research  [16]. Quantitative and mixed method studies were ranked as outlined by Andrew et al. [17] , (i. e. Excellent-A; Good-B; Satisfactory-C and Poor-4). Qualitative papers were also ranked according to quality of design (Low/ Medium/High)  [17]. All ten papers were included in the analysis, seven of high quality and three of low quality.
Data analysis Thematic analysis was the most appropriate form of data analysis for the mixed method studies included in this review. Each article was analysed and deductively coded under two headings: 1. Barriers to the development and implementation of online learning 2. Solutions to the barriers to the development and implementation of online learning. A thematic worksheet allowed the two authors to analyse and synthesise data under qualitative and quantitative / mixed method studies independently. Thematic analysis was completed by two researchers manually and using the software package NVivo 10. Final themes were agreed by all authors.
flow diagram of study selection). One hundred and four of these were excluded by not meeting participant criteria (n = 44), i. e. medical educators; or not reporting empirical research (n = 60). Many of the excluded studies focused on the student experience of engaging with e-learning programs / distance education and online learning. Of the 10 studies that met the inclusion criteria, these were included in final analysis. Table  1  describes the 10 studies; eight quantitative / mixed methods studies and two qualitative. Data was extracted using guidelines  [15]  which included aims of each study, sampling approach, participant characteristics, data collection methods and data analysis approach (see Table  1  Outline of studies included in review).
Limitations of the review There are limitations within this study which need to be acknowledged. While four key themes, with a number of subthemes, were identified, the researchers are aware this may not be exhaustive of all the potential barriers facing medical educators engaging with online learning nor all the dominant solutions available. Despite rigorous search methodologies, it is possible that some studies were missed by the nature of the search strings used, if the keywords did not appear in the title or abstract. Furthermore, one of the issues faced by researchers when using the database Medline (Ovid) was that of replicability. Two researchers, searching independently found conflicting results using the same search strategy and inclusion / exclusion criteria. This is a noted phenomenon as discussed by Younger & Boddy  [32]  where it was found that different interfaces can have an impact on the number of hits retrieved from the same database.
e. , that a single body of water was sampled. After analyzing tracer variability and local water circulation at each station, we identify water layers and times where the drifter risks encountering another body of water. While almost no realization of this sampling scheme will be truly Lagrangian, due to the presence of vertical shear, the depth-resolved observations during the three stations show most layers sampled sufficiently homogeneous physical environments during OUTPACE. By directly addressing the concerns raised by these quasi-Lagrangian sampling platforms, a protocol of best practices can begin to be formulated so that future research campaigns include the complementary datasets and analyses presented here to verify the appropriate use of the drifter platform.
For this study, a regional biogeochemical gradient was expected  (Moutin et al. , 2017)  and rationales for this method's application will be provided. The Oligotrophy to UlTra-oligotrophy PACific Experiment (OUTPACE) cruise provided an opportunity to assess the success of the quasi-Lagrangian sampling strategy. Conducted from 18 February to 3 April 2015 in the western tropical South Pacific (WTSP), one of the goals of OUTPACE was to assess the regional contribution of nitrogen fixation as a biogeochemical process to the biological carbon pump  (Moutin et al. , 2017). During the cruise, three long duration (LD) stations employed the quasi-Lagrangian strategy. In the subsequent discourse regarding these stations, we proceed as follows. Section 2 describes how the drifting mooring was deployed, our methodological strategy, how concurrent data were collected, and the analyses undertaken to answer our central question of whether we sampled a single environment.
A summary of time duration for each data source can be found in Table  1. Between the LD stations, 15 short duration (SD) stations lasting approximately 8 h each were interspersed along the ship's trajectory in roughly equidistant sections (Fig. 1b ). Among the measurements made, CTD casts from SD stations will figure into the validation process in this study. Most casts (both LD and SD) were at least 200 m, with at least one 2000 m cast for all stations. These casts were conducted with the same CTD rosette platform described more fully below in Sect. 2. 3. 1. Throughout the cruise, surface conductivity-temperature measurements from the thermosalinograph (TSG) and currents from shipboard acoustic Doppler current profilers (SADCP) were collected. Their processing is described in Sect. 2. 4.
The subsequent time evolution of these properties within the defining dataset served as a first test for whether sampling stayed in one environment. -Spatial scale determination and baseline context. If time series analysis showed no change in water properties, complementary data from farther away were compiled to evaluate the spatial scale at which the water mass did change. These data were also used to contextualize whether the statistical baseline over-estimated or under-estimated water mass variability. -Currents analysis and Lagrangian risk. The spatial scale of the water mass already determined, water trajectories were used to evaluate at what point the observed flow regime might have brought another water mass into contact with LD station sampling near the SedTrap drifter. The following sections in the methods are organized around these steps, detailing the data and analyses involved for each step.
Validity of method application and environmental context Detection of physical structures and biogeochemical gradients used satellite measurements of sea surface temperature (SST), surface chl a, and sea surface height with its associated geostrophic currents. These data were also used in the LD site selection phase (Sect. 2. 1). All processed satellite data were provided by CLS with support from CNES. SST was derived from a combination of AQUA/MODIS, TERRA/MODIS, METOP-A/AVHRR, METOP-B/AVHRR sensors, with the daily product produced being a weighted mean spanning 5 days (inclusive) previous to the date in question. Weighting was greater for more recent data. Similar to SST, chl a was a 5 day weighted mean produced by the Suomi/NPP/VIIRS sensor. The SST and chl a products had a 0. 02 • resolution, equivalent to ∼ 2 km.
CTD data for time series analysis The shipboard CTD employed during OUTPACE was a Seabird SBE 9+ CTD rosette with two CTDs installed. Data from each cast were calibrated and processed post-cruise using Sea-Bird Electronics software into 1 m bins. All CTD data from other instruments mentioned later were likewise processed using Sea-Bird Electronics software. S A , C T , and potential density (σ θ ) were calculated using the TEOS-10 standard  (McDougall and Barker, 2011). In total, over 200 CTD casts were performed during OUTPACE. Most SD stations had three or four casts, except for SD13, which had time for only one cast owing to a medical emergency. The LDA, LDB, and LDC stations had 46, 47, and 46 casts, respectively, each approximately 3 h apart. During LDA, the two drifting moorings accidentally collided and, due to the time necessary to disentangle them, there is a gap of 9 h in the time series.