dataesr/scientific-paragraphs-categorization huggingface dataset:
The results of this product with 3 day resolution (Figure  3 ) show that although the sea ice drift speed in the N-ICE2015 region follows an increasing trend since 2003, the speeds in 2015 were lower than during years with exclusively first year ice  (2013 and 2014). This implies that our study provides a lower bound for the deformation rates during recent years in the area. As an auxiliary data set to interpret the sea ice drift and deformation time series from the N-ICE2015 arrays, we estimated the distance to the ice edge from the navigational sea ice charts produced by the Norwegian Meteorological Institute. The charts were updated with near-daily frequency by visual analysis of Synthetic Aperture Radar (SAR) images. We calculated the shortest distance between the center of the array (Lance) and the inner edge of the ice class ''open water.
We linearly interpolated (assuming constant drift speed and direction) the position of any such instruments to the hour before using it in the calculations. Also, while the majority of the N-ICE2015 buoys were providing GPS positions every hour, some were only sampling every 3 h (Tables  1  and 2 ). To be able to use all the data, we constructed the time series of deformation from the buoys with hourly data and then downscaled all the data to 3 hourly resolution to obtain as many data points as possible for estimates of total deformation, defined as D5 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi div 2 1shr 2 p. The total deformation was used to assess the scaling law in sea ice deformation where D / L 2b (7) and L5 ffiffiffi A p. For this task, we did not bin the triangles in length scale L classes.
10. 1002/2016JC012403 The sea ice deformation measured by the N-ICE2015 buoys during the early part of the winter deployment peaked during storms and wind direction changes (e. g. , event on 27th January that could be a consequence of a distant storm passing), but was close to zero between events. This may indicate rapid healing of the sea ice cover, as previously described for the Arctic winter  [Hutchings et al. , 2011]. Sea ice drift had a mean speed of 0. 33 m/s, which is high compared to the January to May climatological drift speed of about 0. 02 m/s in the whole Arctic Ocean (https://nsidc. org/data/docs/daac/nsidc0116_ Areas shaded in purple and blue are major and minor storms. Sea ice drift is the average of all the buoys in the array and the wind data are from the meteorological mast in the center of the array.
Hutchings et al. [2011]  previously calculated the exponent for the SEDNA array. To eliminate the methodological differences, we recalculated the deformation rates for SEDNA using our procedures and additionally analyzed the ACSYS and DAMOCLES data. For all three buoys arrays, we assumed the same conservative positioning error as for the N-ICE2015 arrays (d x 550m). DAMOCLES and ACSYS buoy arrays are both missing small scale values, which could influence the steepness of the power law slope. All three earlier buoy arrays continue into the summer, but, for this paper, we analyzed data only with the time spans similar to N-ICE2015 arrays. The earlier Arctic buoy array deployments (DAMOCLES, SEDNA, and ACSYS) all show substantially lower rates of total deformation than N-ICE2015 arrays. While the mean total deformation at 100 km length scale exceeds 0.
1 d 21 in all three cases for N-ICE2015 scatter plots (winter pre-storm and post-storm and spring), this is not the case for any of the earlier deployments. The elevated deformation rates were also confirmed at small length scales (50 m-5 km) by the measurements based on the ship radar images  [Oikkonen et al. , 2017]. b is variable between the experiments (0. 35 for DAMOCLES, 0. 41 for ACSYS and 0. 4 for SEDNA) and it increases southward and toward the sea ice edge, but it remains lower than the winter post-storm and spring N-ICE2015 b values of 0. 54 in all the cases. This suggests that the early winter (N-ICE2015 winter prestorm) and high north (DAMOCLES) deformation values align along a power law with a b around 0. 35, while late winter and spring data sets exhibit higher values between 0.
A linear fit of divergence and ice concentrations higher than 80% shows that the divergent motion is shortly followed by decrease in sea ice concentration. During the spring deployment, only one storm (the late April storm, Figure  6 ) caused clearly divergent conditions. Strong wind events were more evenly distributed across ice drift directions with a frequency peak Journal of Geophysical Research: Oceans  Journal of Geophysical Research: Oceans 10. 1002/2016JC012403 at already predominant southwest drift which in average resulted in convergent motion. If these data are representative of previous years, this suggests that in the N-ICE2015 region only powerful winter storms can cause a strong divergence and shear that opens up leads. In spring conditions, when there was no such powerful storm and when the sea ice cover has been previously broken up by the winter storms, this relationship breaks down.
Although the spring array deformation time series has only a few visible events and the sea ice drift speed was lower than for the winter array, the general level of the deformation and the power law exponent remain high despite low temperatures. The reason for this might be that the spring array was deployed on sea ice that was broken up during the winter storms upstream in the Transpolar Drift and was still drifting in the N-ICE2015 region. In addition to the early February storm, there was a similarly powerful storm also in mid-March during the second drift of Lance  [Cohen et al. , 2017]. During that storm no buoy array was deployed and no deformation data are available. The spring array was deployed less than 100 km upstream of the ice floe on which a meteorological mast was installed during that previous drift.
54) that remained high throughout the N-ICE2015 spring deployment indicates the importance of the high latitude penetrating winter storms for the sea ice deformation scaling. This demonstrates that the scaling law exponent b varies in space and time, and can only be used to compare ice deformation observations, which were obtained under similar conditions but at different scales. In the N-ICE2015 region, b is exceptionally high, indicating close to free ice drift for large parts of late winter and spring 2015. During this period the wind was predominantly northerly, the sea ice was drifting toward the marginal ice zone and was not confined against any boundary. Oikkonen et al. [2017]  analyzed a simultaneous high resolution deformation data set based on the ship radar images taken from Lance for a small area (15 3 15 km) in the center of the buoy array and found a gradual increase of b with decreasing distance to the ice edge.
Acknowledgments We deeply thank the crew and scientists on board R/V Lance. There was a huge number of people to whom we are grateful for their help with the buoy deployments, among them  Bengt Rotmo ,  Ottar Skog , and  Steinar Aksnes  stand out with their efforts during the winter deployment. We also thank the crew of the ice breaker KV Svalbard for assisting the travel of  R/V Lance  through the pack ice and deploying and recovering some of the buoys. The buoys itself were contributed by the  Norwegian Polar Institute (through Centre for Ice, Climate and Ecosystems (ICE) ,  Research Council of Norway  project  STASIS  ( 221961/F20 ), and  ICE-ARC programme from the European Union 7th Framework Programme , grant  603887 ),  University of Hamburg  (through project  DFG EXC177 ),  Finnish Meteorological Institute ,  British Antarctic Survey , the  Helmholtz infrastructure program  FRAM of the  Alfred-Wegener-Institut , and  Norwegian University of Science and Technology (NTNU).
This work has been supported by the  Norwegian Polar Institutes Centre for Ice, Climate and Ecosystems (ICE)  through the  N-ICE  project. P. I. was also supported by  ID Arctic  (funded the by  Norwegian Ministries of Foreign Affairs and Climate and Environment ,  programme Arktis 2030 ). G. S. was supported by the  Institutional Strategy of the University of Bremen , funded by the  German Excellence Initiative. B. C. was funded by the  Academy of Finland  grant  283101. J. H. was funded by the  Academy of Finland  grant  279310. We are grateful to  Jennifer Hutchings  for insightful reviews and valuable advises on the buoy data processing. We thank  Norwegian Meteorological Institute  for ice chart data (http:// polarview. met. no/) and the  University of Bremen  for ice concentration data (http://www.
seaice. uni-bremen. de). N-ICE2015 acknowledges the in-kind contributions provided by other national and international projects and participating institutions, through personnel, equipment, and other support. All the data used in this paper are published in the Norwegian Polar Institute database http://data. npolar. noand partly also at http://www. meereisportal. de(grant  REKLIM-2013-04 ). Journal of Geophysical Research: Oceans.
Note: The full opinion will be published in accordance with Article 10(6) of Regulation (EC) No 1935/2004 once the decision on confidentiality, in line with Article 20(3) of the Regulation, will be received from the European Commission. The following information has been provided under the confidentiality framework and has been redacted awaiting the decision of the Commission: the data provided on the impurities of the products manufactured by three producers of the project consortium of silane and glass fibre producers. Competing interests: In line with EFSA's policy on declarations of interest, Panel member Roland Franz did not participate in the development and adoption of this scientific output. Suggested citation: EFSA CEF Panel (EFSA Panel on Food Contact Materials, Enzymes, Flavourings and Processing Aids), Silano V, Bolognesi C, Chipman K, Cravedi J-P, Engel K-H, Fowler P, Grob K, G € urtler R, Husøy T, K € arenlampi S, Mennes W, Milana MR, Pfaff K, Riviere G, Srinivasan J, Tavares Poc ßas MF, Tlustos C, W € olfle D, Zorn H, Kolf-Clauw M, Lampi E, Svensson K,  Van Haver E and Castle L, 2017.
Non-toxicological data Thermostability on glass fibres treated with the substance was tested under inert atmosphere in a pyrolyser coupled to gas chromatography/mass spectrometry (GC/MS). At 300°C (5 min), the maximum temperature during manufacture of glass-fibre-reinforced plastics, no degradation products were detected with a detection limit of around 10 mg/kg treated glass fibres. The silane function of the substance was found to be hydrolysed already in the sizing solution. The intact substance was not detectable in either the aqueous sizing solutions or in extracts from sized glass fibres (detection limit, 10 lg/kg fibre). The potentially migrating reaction products, including oligomers, were identified in aqueous solutions of the substance (after hydrolysis and polymerisation), in extracts from non-cured glass fibres only treated with the substance as well as treated with the substance and epoxy resin as film former, using flow injection analysis (FIA)-MS, liquid chromatography/mass spectrometry (LC/MS) and liquid chromatography with tandem mass spectrometry (LC-MS/MS).
Quantitative data on the migration of these reaction products were based on LC-MS(/MS) analysis of the identified compounds, mainly linear, branched or cyclic oligomers n = 2-5 with hydrolysed silanes and intact or hydrolysed glycidols, in extracts from treated glass fibres, assuming a response equal to that of the hydrolysed substance, i. e. [3-(2,3-epoxypropoxy) propyl]silanetriol. None of the substances were detected with a detection limit of 60 lg/kg fibre. Using this detection limit, maximum migrations were estimated from the plastics, scenarios of use and the time/temperature conditions of intended uses by modelling, assuming articles of 5 mm thickness and high solubility in foods: • single long-term use (10 days/60°C) with 70% glass fibres in thermoset and thermoplastic polymers • repeated use scenarios at up to 175°C with 50% glass fibres in thermoplastic polymers.
Toxicological data The substance (purity not reported) dissolved in ethylene glycol dimethyl ether was tested for bacterial mutations in three strains of Salmonella Typhimurium TA97, TA98 and TA100 in triplicate plates with and without metabolic activation at concentrations up to 5,000 lg/plate following the OECD guideline TG 471. In TA100 strain, both in the absence or presence of S9 fraction, the test substance induced a statistically significant dose-related increase in the number of revertants. A statistically significant increase of the revertants was also observed in TA97 strain only at the highest concentration tested. No increase of revertants was detected in TA98 strain. The substance (purity not specified) was tested for gene mutation in mouse lymphoma L5178Y cells up to 3,000 ng/mL in the absence and up to 8,000 ng/mL in the presence of metabolic activation system.
Research infrastructure Research infrastructure can be described by its activities or by its legal status. The ESFRI definition presented below is primarily related to the former. This definition covers a range of elements, including tangible or hard research infrastructure (e. g. , the laboratory of CERN), as well as supporting research infrastructure (e. g. , related to Information Communications Technology, ICT), and less tangible or soft research infrastructure (e. g. , training, networks or standard operating procedures, SOP). "The term 'research infrastructures' refers to facilities, resources or services of a unique nature that have been identified by European research communities to conduct top-level activities in all fields. This definition of Research Infrastructures, including the associated human resources, covers major equipment or sets of instruments, in addition to knowledge-containing resources such as collections, archives and data banks.
Research Infrastructures may be "single-sited", distributed", or "virtual" (the service being provided electronically). They often require structured information systems related to data management, enabling information and communication. These include technology based infrastructures such as Grid, computing, software and middleware. " (ESFRI, 2010). This ESFRI definition is in line with that used by the ESF in their 2006-2007 survey, as well as the 2010-2012 follow-up survey that was conducted by MERIL. The findings of MERIL were used to create an inventory of European research infrastructures. This inventory continues to be updated and is free to access or search via an online portal (http://portal. meril. eu/converis-esf/publicweb/startpage?lang=1). In order to decide which infrastructures to include on the MERIL inventory, the term research infrastructure has been operationalised by employing a set of in/exclusion criteria.
Examples of the types of food and health research facilities, resources and services in Europe EuroDISH mapped over 160 items (table  3 ) using desk research, stakeholder interviews (n=30) and a workshop in Brussels, Belgium on the 17-18 th June 2013 (n=49) 1. Desk research and interview data were analysed by each DISH area using a qualitative method of content analysis  (Breakwell, 1995; Greenhalgh, 1997)  and a common data recording template (based on the EuroDISH definition of research infrastructure, table 2). The workshop 1 presented the initial EuroDISH mapping findings and discussed the challenge of defining research infrastructure, as well as the breadth and depth of food and health research areas. From this work, nine research infrastructures were identified that were relevant to facilitate food and health research and had been awarded ERIC status or were present on the MERIL inventory (table 2, 3 and figure  1 ).
The majority of these were not specifically designed to facilitate food and health research and no one research infrastructure was considered applicable to cover all four of the DISH research areas. These nine infrastructures were most relevant to the study of health with determinants considered to be the area of food and health research that was the least represented. As a group however, these nine research infrastructures depicted more than one discipline, with both the Biomedical and Life Sciences, as well as the Humanities and Social Sciences represented e. g. , infrastructures ranged from those that facilitated the implementation of randomised controlled trials (ECRIN: European Clinical Research Infrastructure Network); to those that promoted the use of high-quality data in social, economic and political research (CESSDA: Council of European Social Science Data Archives Consortium).
Short-term projects, networks or research infrastructure The MERIL management criterion states a research infrastructure "does not include short-term projects or networks. However, their component parts may be entries in MERIL in their own right". EuroDISH mapped a number of projects and networks (particularly European funded) which provided or linked major   3 ). These items were initially mapped due to the extensive interactions observed among networks, projects and inter/national research infrastructures. For example, networks were seen to promote specific research consortia, which resulted in active research projects that had the potential to lead to inter/national research infrastructure. It was however, difficult to establish the longevity or potential sustainability of the project repositories, databases or facilities beyond the lifetime of the projects (MERIL management criterion also states "the RI must have funding approved for a period sufficient to deliver the type of access typical for that class of facility").
Inter/national (including European) relevance of research infrastructure The quality criterion for inclusion in the MERIL database suggested a research infrastructure is required to be of "…greater than national relevance…". This criterion was often met for items mapped at the European level (that had not already been discounted as short-term projects or networks). It was more difficult to establish whether this criterion was met for those items funded or predominantly hosted at a national level. For example, the MERIL quality criterion refers to "evidence of the attractiveness of the RI to users from abroad" or "the existence of formal agreements with non-national partners" and this information was rarely available or apparent in the data collected by EuroDISH via desk research, interview or stakeholder workshop.
A particular issue was accessing the information to expertly judge whether "appropriate metadata to enable the data to be analysed, compared and re-used" or "harmonised standards with equivalent centres" was in place. This resulted in several items, such as national surveys (e. g. , CIQUAL: French food composition database; ENNS: French national study on nutrition and health; NutriNet-Santé: NutriNet-Santé databank), being classified as national research infrastructure only. Furthermore, it was difficult to decide the hierarchy of research infrastructure (i. e. which national 'research infrastructure' was subsumed under a larger international 'research infrastructure'). For example, the UK bio-bank was initially considered a standalone research infrastructure; yet, it was eventually classified as the UK node of the international research infrastructure BBMRI-ERIC (Bio-banking and Bio-molecular Resources Research Infrastructure-European Research Infrastructure Consortium).
For example, there remained uncertainty as to whether it was correct to map NOPA (Nutrition, Obesity & Physical Activity database) or whether WHO Europe should be the mapped item, whereby this would include items such as NOPA as well as the VMIS (Vitamin and Mineral Nutrition Information System). 3 , figure  1 ). Similarly, items changed affiliation, either developing as standalone research infrastructures or integrated and advanced into pan-European research infrastructures. This can be seen with the EuroDISH case study 2 (table 3, figure  1 ), where the nutritional phenotype research infrastructure (formerly Nutritional Phenotype database, dbNP) could be considered at different time points as part of NuGO (Nutrigenomics organisation), the JPI-HDHL joint action ENPADASI/DASH-IN or potentially as a standalone research infrastructure in the near future.
Acknowledgements All EuroDISH consortium members are acknowledged, as well as the external expert and stakeholder interview and workshop participants for their invaluable contributions to this work. The work reported herein has been carried out within EuroDISH -studying the need for food and health research infrastructures in Europe. This co-ordination support action is supported by the European Commission under the Food, Agriculture and Fisheries, and Biotechnology theme of the 7th Framework Programme for Research and Technological Development (grant agreement no: 31178). The study does not necessarily reflect the Commission's views or its future policy in this area. Further acknowledgements go to Rachel Berry (IFR) for reviewing the draft manuscript; Jeppe Inversen (DTU) for data collection, Determinants analysis and reviewing the draft manuscript; Camille Hoppe and Astrid Böhm (both DTU) for data collection and Determinants analysis; Julia Bardes for data collection and Health analysis; Lars Eijssen and Chris Evelo for their central roles, alongside Guiditta Perozzi, regarding the nutritional phenotype research infrastructure case study; and finally the ILSI Europe team, in particular Ruth Marquet, regarding workshop organisation.
Author conflicts of interest A number of EuroDISH authors and consortium members have a direct interest in the research infrastructures contained herein. In particular, the pan-European nutrition surveillance research infrastructure and the nutritional phenotype research infrastructure case studies. However, please note that the need for these research infrastructures was identified prior to the development of EuroDISH (e. g. , the nutritional phenotype research infrastructure case study 2 was initiated by -and has subsequently been included in -the JPI-HDHL call to establish a European Nutritional Phenotype Assessment and Data Sharing Initiative, ENPADASI). Marketing group runs studies with eye-tracking equipment. Sensory science group runs studies on eating behaviour using olfactory/gustometery/brain imaging (fMRI) equipment.
Database and data collection tools. ESS (European Social Survey) Academically driven cross-national survey every 2 years since 2001. Social structure, conditions and attitudes in Europe (e. g. , democracy). Provides social not food data. MTH (MetaboHUB) Provides new methods to interpret nutrition and health associations. Originally a French project MetaboHUB developed a distributed coordinated infrastructure for metabolomics dedicated to innovation, training and technology transfer restructure and coordinate France's highest quality research platforms in metabolomics (Bordeaux, Paris, Toulouse, Clermont-Ferrand). These four partners will develop shared tools and expertise for basic and applied projects related to human health and nutrition, food production and quality, preparatory phase), SHARE-LEAP (longitudinal enhancement & access improvement), SHARE-M4 (multinational advancement of RI on ageing) RI on ageing.