dataesr/scientific-paragraphs-categorization huggingface dataset:
The sites have to be carefully selected because they should be representative for a larger area of the size of the model resolution. Furthermore, validated data sets are typically only available after a few years and only unvalidated data can be used for the near-real-time evaluations. In particular, observations from the European Monitoring and Evaluation Programme (EMEP; http://www. emep. int), and the European air quality database "AIRBASE" (http://www. eea. europa. eu/themes/air/ air-quality/map/airbase) are used to evaluate the reanalysis results. Also evaluations based on the USA "AirNow" observations (http://www. airnow. gov) are in preparation. Apart from ozone, the aerosol composition measurements from these networks will also be considered, as well as other compounds like CO and NO 2.
Absolute extinctions, however, are about a factor of 2 too small in the model due to the much coarser resolution (in order to prevent artefacts due to averaging the ceilometer data over regions with low signal/noise ratios a high resolution is maintained). Many as-pects influence the quantitative comparison, including uncertainties in the source strength (fire radiative power observation and aerosol mass produced) uncertainties in the transport over several days, removal processes, resolution of the model and local representativity issues. Part of these modelling errors may have been corrected by the assimilation of the MODIS observations. The widespread use of ceilometers and their capability to measure the backscatter coefficient offers a level of information content that is well suited for the evaluation of aerosol models.
Their uncertainty of extinction coefficients can be below 30 %, depending on the instrument used, see, e. g. Heese et al. (2010)  or  Wiegner and Geiß (2012). The adequate representation of sources and dispersion of different aerosol types is still a challenge for aerosol models. The evaluation of the MACC analyses with ceilometer observations from the German Weather Service (DWD; http://www. dwd. de/ceilomap) showed the usefulness of the ceilometer data to track fire plumes, (Sahara) dust plumes, and to validate the modelled boundary layer heights. Data from major international measurement campaigns are also used to evaluate if the MACC system is able to describe mean concentrations, transport of pollutants and observed variability. Examples are ACCESS  (Roiger et al. , 2014)  and POLARCAT/POLMIP  (Emmons et al.
Tropospheric nitrogen dioxide Retrievals of tropospheric NO 2 columns from SCIAMACHY and GOME-2 observations are used for the validation of the three MACC systems. Nitrogen dioxide (NO 2 ) satellite observations from the OMI instrument are assimilated  (Inness et al. , 2015) , but this is based on a different retrieval scheme and data from the OMI instrument which has a later overpass time. Comparisons to SCIAMACHY/GOME-2 monthly mean tropospheric NO 2 columns on a global map  (Eskes et al. , 2014a)  show that spatial distributions of tropospheric NO 2 columns are well reproduced by all three NRT model runs throughout all seasons, indicating that emission patterns and NO x photochemistry are generally well represented. A general feature is the underestimation of NO 2 columns over the continents in general and particularly in China (the latter is also evident from Fig.
Aerosol Bulk optical properties of the MACC aerosol model are validated against NRT level 1. 5 AERONET observations (see Fig. 7 ). Level 1. 5 data are the only observations available for validation within days or weeks after sensing. The correlation coefficients are based on consistent daily mean values, from all stations and when observations are available. The figure reveals that the latest model version has on aver-age a positive MNMB of about +20 % for AOD. The positive bias is smaller in winter (+5 %) but increases in spring. A month-to-month variation is observed in the correlation, ranging from 0. 65 to 0. 8. On average, approximately 50 % of the day-to-day AOD variability is predicted by the o-suite. Also the +3-day forecast aerosol distributions are routinely evaluated and show 5-10 % less AOD than the initial day.
The latter being constrained through the assimilation method. The NRT aerosol model evaluation remains limited. One limitation is the quality of the NRT AERONET data, which have a preliminary nature. Retrospective analysis of the year 2011 shows that this level 1. 5 NRT AOD AERONET data, due to undetected cloud contamination and any uncorrected instrumental drift, are on global average 20 % higher than quality assured level 2. 0 AERONET data (see Fig. 8 ). This suggests that the o-suite bias in AOD is likely to be larger than suggested by the comparison with the NRT observations. Another limitation is that little information on the aerosol composition is available, and this can only be assessed indirectly, e. g. through the AE. MACC o-suite dust parameters have been routinely assessed over northern Africa, Middle East, and the Mediterranean basin and southern Europe, using AERONET, MODIS (Aqua), and lidar observations.
Discussion and future perspectives In this paper we provided an overview of the validation approach for the global MACC service products. The principle behind this work is that every product in the catalogue of MACC should be accompanied by validation information based on independent observations, and summarized in validation reports, which is essential for the users. For the global forecast/analysis service this validation report is updated on a very regular 3-monthly basis to provide up-to-date information on the product quality. The validation team is operating largely independently from the modelling teams. The VAL activity is targeted to users, but it also provides feedback to the modelling and data assimilation teams in MACC concerning new model test versions. The assimilation and validation activity within MACC is clearly limited by the finite amount of high-quality observations available for comparison in NRT.
In the near future more focus will be given to the evaluation of the MACC system in terms of trace gas and aerosol boundary conditions to regional air quality models. Suitable evaluation data sets and good quality metrics are currently under investigation. Another aspect not yet well covered in the VAL activity is the evaluation of the aerosol composition and vertical distribution, in particular because no, or very limited NRT observations are available. Additional research will be based on the climatological aerosol composition and variation (as used for AeroCom model evaluations) to obtain relevant information on the quality of the IFS forecast system. Validation of vertical distribution of some components, such as aerosols, could be improved in future, incorporating observations from networks of ceilometers and micropulse lidars functioning operationally.
However, for these measures to be truly useful in MACC validation, calibration constraints must be first overcome. Apart from the observational data sets listed in Sect. 7, which are currently used for the validation of the MACC system, VAL is also expanding its scope by looking at new promising data sets. Previous (e. g. ACCESS) and future field campaign data provide interesting case studies and allow for a more extensive evaluation in the free troposphere. A data set that was considered in MACC are ceilometer observations, and the use of ceilometer networks was discussed in Sect. 8. A second type of new observations studied in MACC involves ground-based MAX-DOAS instruments. These instruments are well suited to probe the amount of pollutants in the boundary layer above urban areas. Because several of the instruments are located close to large cities, these observa- tions are especially valuable to test regional air quality models with enough spatial resolution to simulate fine-scale variability (see, e.
3. The CAMS validation work done should be tested for compliance against general quality assurance principles. During MACC a "validation protocol" was developed  (Lambert, 2013). In part this is based on principles developed in the Quality Assurance Framework for Earth Observation (QA4EO; http://www. qa4eo. org) activity of the Group on Earth Observations (GEO). Some aspects have been incorporated in the VAL practice, but a regular testing against these principles is foreseen. 4. The user driven future service evolution has been the topic of the EU project GMES-Pure (http://www. gmes-pure. eu). The definition of service data requirements (SDRs) was found to be a crucial intermediate step in the systematic approach on service evolution. The validation activity in the future CAMS forms an essential element for the translation of (i) the end-user requirements into SDRs and of (ii) the SDRs into observational requirements for both space and non-space components for assimilation as well as validation purposes.
Acknowledgements. MACC-II was funded by the  European Commission  under the  EU Seventh Research Framework Programme , contract number  283576. MACC-III is funded by the  European Community 's  Horizon 2020 Programme  under grant agreement no. 633080. We wish to acknowledge the provision of NRT GAW observational data by  Institute of Atmospheric Sciences and Climate (ISAC)  of the  Italian National Research Council (CNR) ,  South African Weather Service, National Centre for Atmospheric Science  ( NCAS, Cape Verde ),  National Air Pollution Monitoring Network (NABEL)  ( Federal Office for the Environment FOEN  and  Swiss Federal Laboratories for Materials Testing and Research EMPA), Atmospheric Environment Division Global Environment and Marine Department Japan Meteorological Agency, Chinese Academy of Meteorological Sciences (CAMS) ,  Alfred Wegener Institut, Umweltbundesamt (Austria), National Meteorological Service (Argentina) ,  Umweltbundesamt  ( UBA, Germany ).
The authors acknowledge the  NOAA Earth System Research Laboratory (ESRL) Global Monitoring Division (GMD)  for the provision of ground-based ozone concentrations. We are grateful to the numerous operators of the AERONET network and to the central data processing facility at  NASA Goddard Space Flight Center  for providing the NRT sun photometer data, especially  Ilya Slutker  and  Brent Holben  for sending the data. Much of the AERONET sun photometers used in MACC aerosols validation have been calibrated within AERONET-Europe TNA supported by the  European Community-Research Infrastructure Action  under the  FP7 "Capacities" specific programme for Integrating Activities, ACTRIS  grant agreement no. 262254. The AeroCom tools have been recently developed with support from the  ESA  project cci-aerosol, the  EU  funded project  ACTRIS  ( EFP7/2007-2013 , grant  262254 ), and the  Norwegian research council project Aero-ComP3.
We want to thank the  DLR ACCESS team  for the provision of preliminary CO and O 3 observations during the ACCESS campaign. SCIAMACHY lv1 radiances were provided to IUP by  ESA  through  DLR/DFD. We acknowledge the  Network for the Detection of Atmospheric Composition Change  (http://www. ndacc. org)for providing observations to MACC. We wish to thank  NORS (Demonstration Network Of  ground-based Remote Sensing observations in support of the  Copernicus Atmospheric Service , funded under the European Community's  Seventh Research Framework Programme , grant no. 284421 ) for the provision of the NDACC MWR, lidar, FTIR and UV-VIS observations at  Ny Ålesund, Bern, Jungfraujoch, Izaña, Xianghe, Harestua, Haute Provence , and  Reunion , and for their contribution to the validation work. We owe thanks to the  Finnish Meteorological Institute  for access to the  COST-713 UV Index database and especially  to the contributors to that database which provided the measurements for comparisons.
We acknowledge the  University of Saskatchewan, Canada , for providing the OSIRIS (http://osirus. usask. ca/)observations data and the  National Aeronautics and Space Administration (NASA), USA , for OMPS (http://npp. gsfc. nasa. gov/omps. html). The authors acknowledge the strong support of the  European Commission, Airbus , and the  Airlines (Lufthansa, Air France ,  Austrian ,  Air Namibia ,  Cathay Pacific ,  Iberia , and China Airlines so far) who carry the MOZAIC or IAGOS equipment and perform the maintenance since 1994. MOZAIC is presently funded by  INSU-CNRS (France) ,  Météo France ,  Université Paul Sabatier (Toulouse, France)  and  Research Center Jülich (FZJ, Jülich, Germany ). IAGOS has been and is additionally funded by the  EU projects IAGOS-DS, IAGOS-ERI , and IGAS. The MOZAIC-IAGOS database is supported by  ETHER  ( CNES  and  INSU-CNRS ).
The project deliberately side-steps the traditional boundaries of professional, scientific and artistic practice, to develop shared methods, sources, data and modes of working. The fact that Hubbub is physically located within its funder has influenced the kinds of interdisciplinary practices that it has developed and that are possible, as well as how it has tried to torque various methods for eliciting and capturing interdisciplinarity. The Wellcome Trust, for its part, has described The Hub as a "grand experiment", and hopes that there will be many points of intersection and collaboration between researchers undertaking Hub residencies, Trust staff and Wellcome Collection resources (including the Wellcome Library, Archives and Reading Room). It has championed a wide-ranging and experimental approach to interdisciplinarity, one that we see as extending well beyond the integrationist logic that characterizes many such endeavours (for an analysis of this logic, see  Fitzgerald and Callard, forthcoming).
"In the Diary Room" Our second experiment-one explicitly aesthetic as well as scientific-is designed to bring to visibility the affective dynamics of interdisciplinarity. We are developing a tool to gather verbal, gestural and affective data from collaborators, as they reflect on their experiences of interdisciplinarity, when they are physically present in The Hub. This experiment finds its initial inspiration from what might be described as the epistemologically dubious though culturally resonant mise-en-scène of "The Diary Room", as featured in the television franchise Big Brother (Big Brother UK Wiki, n. d. ). The diary room in Big Brother is a small room accessed via the larger space of the Big Brother house, where participants are summoned (or might venture of their own accord) to answer questions posed by a disembodied voice-for example, about how they are feeling, what they are thinking-as well as be given tasks to carry out.
We advise anyone interested in understanding projects like this one to put time and space at the centre of their investigation. (  4 ) Not all collaborative projects are the same. One of the many elements that (we hope) distinguishes Hubbub is its lively interest in novel dynamics of experimentation-which is an interest that we both attend to in our tracking projects, but that has also informed how we design those tracking devices themselves. Other projects might similarly think through what kinds of iterative relationships might exist between the distinctive elements of their own collaboration, and the means through which they try to bring its progress into understanding. Future publications from Hubbub will present, analyse and visualize data emerging from these three experiments-yielding, we hope, a less sanitized and domesticated account of interdisciplinarity than some of the current endorsements that characterize the field (for example,  Allmendinger et al.
Acknowledgements This research is supported by The  Wellcome Trust  [ 103817/Z/14/Z ], via the award to Hubbub, the first residency of The Hub at  Wellcome Collection. We thank  Charles Fernyhough ,  Daniel Margulies  and  James Wilkes , the other members of the  Hubbub Collaboration Questionnaire (HCQ)  research team, who contributed to finalizing the HCQ, as well as to the  Hubbub Core Group  in its entirety for helping to develop approaches to interdisciplinarity within Hubbub. We are also indebted, in developing the HCQ, to the " Voice Club " questionnaire developed by the "Hearing the Voice" research team. We thank  Harriet Martin ,  Hub Partnership Manager  at The  Wellcome Trust , and  Kimberley Staines ,  Hubbub Project Coordinator , for their many contributions to thinking through and implementing interdisciplinarity in The Hub.
Data collection and analysis Semi-structured, in-depth interviews, based on a topic guide were used to explore women's views and experiences. The indepth interviews were undertaken in a private clinic room by two investigators (ZW, HC) and lasted between 26-80 minutes. Interviews were audio recorded and transcribed in full. Brief notes were taken during the interviews and expanded upon as soon as possible after the interviews. Women were asked about: their knowledge, attitudes and behaviours regarding healthy lifestyles and physical activity in pregnancy; the benefits/motivators and concerns/barriers regarding being active in pregnancy; what sources of physical activity information, advice and support were available to them; weight management in pregnancy; and their views of potential interventions and supports to increase physical activity during pregnancy for women attending the clinic.
This compilation of different regional gridded inventories -including that of the Environmental Protection Agency (EPA) for USA, the EPA and Environment Canada (for Canada), the European Monitoring and Evaluation Programme (EMEP) and Netherlands Organisation for Applied Scientific Research (TNO) for Europe, and the Model Intercomparison Study for Asia (MICS-Asia III) for China, India and other Asian countries -was gap-filled with the emission grid maps of the Emissions Database for Global Atmospheric Research (EDGARv4. 3) for the rest of the world (mainly South America, Africa, Russia and Oceania). Emissions from seven main categories of human activities (power, industry, residential, agriculture, ground transport, aviation and shipping) were estimated and spatially distributed on a common grid of 0.
3 for gap filling the remaining regions and/or sectors for some substances. In the HTAP_v2. 2 database, grid maps were merged together with a "collage/mosaic" approach instead of gridding the global emission inventory with one single proxy data set, as done in for the EDGAR-HTAP_v1 data set compilation  (Janssens-Maenhout et al. , 2012). The HTAP_v2. 2 inventory aims to obtain more local accuracy on the location of single point sources compared to the previous HTAP_v1, but the downside is that a consistent single location of a specific source of multi-pollutants is no longer ensured, when data originated from different sources, possibly leading to spurious chemical reactions involving non-linear chemistry in the air quality models. Sect. 3 discusses the resulting grid maps and addresses the contents of the HTAP_v2.
BC and OC emission data are not available as emission grid maps within the MACC-II data set, but the PM grid maps are accompanied by a recommendation on the PM composition describing the carbonaceous profiles per SNAP code and country. This so-called PM split table (per SNAP code and country) of TNO  (Visschedijk et al. , 2009)  is used to derive the BC and OC from PM 10 and PM 2. 5 emission grid maps (see  Kuenen et al. (2014)  for details). Finally, to derive the monthly grid maps the EMEP modelling group provided the monthly profiles, which are with a monthly factors varying around 0. 0833 specified for each country and for each sector, with a further substance-specific variation for the agricultural sector (M. Schulz, personal communication, 27 May 2013 and A. Nyiri, personal communication, 4 June 2013).
, 2012) , Korean emissions from the Clean Air Policy Support System (CAPSS)  (Lee et al. , 2011) , Indian emissions from the Argonne National Laboratory  (Lu et al. , 2011) , and fill the gap where local emission data are not available using REAS2. 1 8 developed by  Kurokawa et al. (2013). 7 No NH 3 emissions are reported in the energy sector: for the countries Albania, Bosnia-Herzegovina, Cyprus, Estonia, Greece, Ireland, Iceland, Luxembourg, Latvia, FRY Macedonia, Malta, Norway, Poland, Romania, Slovakia, and Slovenia; in the industry sector for the countries Albania, Bosnia-Herzegovina, Greece, Ireland, Iceland, and FRY Macedonia; and in the residential sector for the countries Greece, Iceland and Slovenia. 8 The REAS2. 1 inventory for Japan includes the data developed by Ministry of the Environment of Japan  (MOEJ, 2009)  for NMVOC evaporative emissions from stationary sources, the database developed by the Ocean Policy Research Foundation (OPRF, 2012) for the maritime sector, and the Japan Auto-Oil Program Emission Inventory-Data Base (JEI-DB) developed by Japan Petroleum Energy Center (JPEC, 2012a, b, c) for other sources.
MEIC is developed by Tsinghua University under an openaccess model framework that provides model-ready emission data over China to support chemical transport models and climate models at different spatial resolution and time scale. In the MIX inventory, the MEIC v. 1. 0 data were used, which contain the anthropogenic emissions of China for SO 2 , NO x , CO, NMVOC, NH 3 , CO 2 , PM 2. 5 , PMcoarse, BC, and OC for the years 2008 and 2010 with monthly temporal variation at 0. 25 • × 0. 25 •. For India, MIX used the Indian emission inventory provided by ANL (Argonne National Laboratory) for SO 2 , BC, and OC and REAS2. 1 for other species. With the input from different regions, the MIX inventory provided harmonized emission data at 0. 25 • × 0. 25 • grid resolution with monthly variation for both 2008 and 2010.
Rest of the world covered by EDGARv4. 3 The Emission Database for Global Atmospheric Research (EDGAR) of EC-JRC/PBL (2011) provides historical  global anthropogenic emissions of greenhouse gases 9  CO 2 , CH 4 , N 2 O, HFCs, PFCs and SF 6 , of precursor gases, such as CO, NO x , NMVOC and SO 2 and of aerosols (PM 10 ) per source category at country level on 0. 1 The EDGAR emission data are spatially distributed using an extensive set of global proxy data, which are representative for major source sectors and documented in the EDGAR gridding manual of Janssens-Maenhout et al. (2013). For HTAP_v2. 2, the EDGARv4. 3 database provides yearly emission grid maps with a resolution of 0. 1 × 0. 1 degree for the "rest of the world" countries of Table  S1. 2 of Annex I in the Supplement for all pollutants (SO 2 , NO x , CO, NMVOC, NH 3 , PM 10 , PM 2.
5 , OC, BC) and HTAP sectors for the years 2008 and 2010. The htap_2 SHIPS data are provided for the entire world, while the htap_1 AIR data are provided for the entire world for the international aviation and for the world excluding USA and Canada for the domestic aviation. EDGAR provides also sector-specific monthly profiles, defined with first order estimated factors for each of the three different zones: Northern Hemisphere, the Equatorial region and Southern Hemisphere (Table  S1. 2 in the Supplement). A reverse profile is applied for the two hemispheres from the EDGAR v4. 3 database, while no seasonal pattern is used for the Equatorial regions. Monthly emissions grid maps are generated from the annual emission data per HTAP sector using these EDGAR monthly factors, which resemble most to the EMEP-TNO profiles (see Sect.
Overview of the temporal profiles used in HTAP_v2. 2 The modulation of annual emissions over time is necessary in order to provide the modelers emission data consistent with the seasonal pattern and activities. Monthly data were generated for all sectors except for the international shipping and international aviation, which are considered constant over the year. US-EPA, EMEP and EDGAR provided monthly profiles, but MIX provided directly and solely monthly emission grid maps. Figure  1c  summarizes the sector-specific monthly profiles for each of the regional data sets. The temporal profiles are additive and specified with monthly factors mod-ulating around 1/12 for each of the sectors. For the agricultural sector, EMEP provided compound-specific monthly factors, which characterise high NMVOC emission in spring and high CO emission in autumn.