dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS This work was supported by institutional funds from  CNRS ,  INSERM ,  Université de Strasbourg (UDS) ,  Université de Grenoble Alpes , the  Labex INRT  and by grants from  La Ligue Nationale contre le Cancer Equipe labellisée  (A. H. ),  Agence National pour la Recherche  ( ANR-20-CE17-0008   CA-Ts ,  ANR-18-CE12-0010   ZFun ,  ANR-16-BSV8-0013 EPIVARZ ) and  USIAS of the University of Strasbourg  ( USIAS-2015-42 ). This research work was funded by  Institutional Fund Project  under grant number ( IFPNC-005-130-2020 ). Authors therefore gratefully acknowledge technical support from  Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia. We would like to thank the bioimaging facility (MicroCell) of the  Institute for Advanced Biosciences  and the  IGBMC Microarray and Sequencing platform , which is member of the ' France Génomique' consortium  ( ANR-10-INBS-0009 ).
, 2016; Roper et al. , 2017) , this study aims to examine the role of ICT and R&D investment as innovation inputs  (Hall et al. , 2013) , as well as external knowledge sources such as open collaboration for innovation  (Gassmann et al. , 2010; Van Beers and Zand, 2014)  and knowledge spillovers  (Bloom et al. , 2019; Audretsch and Belitski, 2020b)  for productivity and innovation. We use data across four industries in the UK during 2002-2014: manufacturing, information and communication technologies (ICT), creative and scientific and professional services to test our research hypotheses. This study expands the prior research by investigating how a simultaneous increase in investment in internal knowledge  (Hall et al. , 2013; Belitski, 2019)  and external knowledge inputs  (Gassmann et al.
Data matching and sample description To test our hypotheses, we used six pooled crosssectional datasets Business Structure database known as Business Structure database (BSD) and the UK Innovation Survey (UKIS) over 2002-2014. First, we collected and matched six consecutive UKIS waves (UKIS 42002-04, UKIS 52004-06, UKIS 62006-08, UKIS 72008-10, UKIS 82010-12, and UKIS 92012-14) each conducted every second year by the Office of National Statistics (ONS), United Kingdom (UK). Second, we matched the BSD variables for years  2002, 2004, 2006, 2008, 2010, and 2012  to a correspondent CIS survey wave. The BSD data include information on firm legal status, ownership, export, turnover, employment, industry, and postcode. Having finalized the sample construction, we realized that the sample is very diverse, which raised the issue of its pullability.
37 0. 78 Lead (UKIS) How effective were lead time advantages as a method for maintaining or increasing the competitiveness of product and process innovations: patents (0 -not applicable to 3 -high)? 0. 03 0. 12 Complexity (UKIS) How effective were complexity of goods or services as a method for maintaining or increasing the competitiveness of product and process innovations: patents (0 -not applicable to 3 -high)? 0. 02 0. 10 Secrecy (UKIS) How effective were Secrecy (include non-disclosure agreements) as a method for maintaining or increasing the competitiveness of product and process innovations: patents (0 -not applicable to 3 -high)? 0. 05 0. 10 Source:  UK Innovation Survey, 1994 -2016 and Business Structure Database, 1997 -2017:  Secure Access. UK Data Service.
Note: Reference category for legal status is Company (limited liability company), industry (mining), city-region (Newcastle). Industry, year, and city region controls are supressed to save space. Robust standard errors are in parenthesis. Robustness check for standard errors included their clustering by 2 digit SIC. The coefficients of the SURE regressions for binary variables are the marginal effect of the independent variable on the probability of dependent variables in each regression. For dummy variables, it is the effect of a discrete change from 0 to 1. Significance level: *P <. 10; **P <. 05, ***P <. 01. Breusch -Pagan test for independence chi2(6) = 905. 0, P-value <. 01. Source:  UK Innovation Survey, 1994 -2016 and Business Structure Database, 1997 -2017:  Secure Access. UK Data Service.
Note: Reference category for legal status is Company (limited liability company), city-region (Newcastle). Industry, year, and city region controls are supressed to save space. Robust standard errors are in parenthesis. Robustness check for standard errors included their clustering by 2 digit SIC. The coefficients of the SURE regressions for binary variables are the marginal effect of the independent variable on the probability of dependent variables in each regression. For dummy variables, it is the effect of a discrete change from 0 to 1. Significance level: *P <. 10; **P <. 05, ***P <. 01. Breusch-Pagan test for independence chi2(6) = 685. 02, P-value <. 01. Source:  UK Innovation Survey, 1994 -2016 and Business Structure Database, 1997 -2017:  Secure Access. UK Data Service.
Limitations and future research One of the limitations of this study is that data were collected using a survey that was limited to six time periods. In the UKIS, the sample is rotated, allowing only a small fraction of firms to get into the survey again. A balanced long-term longitudinal study with firm representation across industries, type of innovation, regions, firm size, and age will measure how internal and external knowledge investment change over time and their effect on innovation and productivity. Future research will focus on understanding the different combinations of knowledge and expand the industry perspective. Another limitation is a reduction in observations in the final three waves of  UKIS (2008 UKIS ( -2014)). The global financial crises likely impacted the availability and efficiency of internal and external resources and in particular, the willingness to collaborate and the availability for knowledge spillovers.
Department of Enterprise, Trade and Investment. (2018). UK Innovation Survey, 1994-2016: Secure Access. [data collection]. 6th Edition. UK Data Service. SN: 6699, http://doi. org/10. 5255/UKDA-SN-6699-6. Office for National Statistics. (2017). Business Structure Database, 1997-2017: Secure Access. [data collection]. 9th Edition. UK Data Service. SN: 6697, http://doi. org/10. 5255/UKDA-SN-6697-9. Further source: UK Innovation Survey, 1994-2016 and Business Structure Database, 1997-2017: Secure Access. UK Data Service.
60 0. 49 Herfindahl Index (BSD) 0. 03 0. 05 Foreign (BSD) 0. 51 0. 50 Subsidiaries (BSD) 0. 97 0. 77 Appropriability (UKIS) 0. 13 0. 19 R&D intensity (UKIS/BSD) 0. 01 0. 03 ICT intensity (UKIS/BSD) 0. 02 0. 04 Collaboration (UKIS) 0. 74 1. 24 Spillover (UKIS) 0. 33 0. 28 Source:Department for Business, Innovation and Skills, Office for National Statistics, Northern Ireland. Department ofEnterprise, Trade and Investment. (2018). UK Innovation Survey, 1994-2016: SecureAccess. [datacollection]. 6thEdition. UK Data Service. SN: 6699, http://doi. org/10. 5255/UKDA-SN-6699-6. Office for National Statistics. (2017). Business StructureDatabase, 1997-2017: Secure Access. [datacollection]. 9thEdition. UK Data Service. SN: 6697, http://doi. org/10. 5255/UKDA-SN-6697-9. Further source:UK Innovation Survey, 1994-2016 and Business Structure Database, 1997-2017: Secure Access.
57** 1. 50*** (2. 80) (0. 43) 1. 58 -0. 03 (2. 16) (0. 39) 38. 42 -4. 19 (26. 01) (3. 90) 19. 82*** -1. 32*** (3. 10) (. 47) 18. 86*** 0. 14 (2. 01) (. 31) 18. 62* 6. 36*** (8. 41) (1. 22) -177. 10*** 65. 76*** (47. 00) (7. 10) 53. 30 13. 34*** (29. 02) (4. 44) -207. 71 80. 01 (368. 04) (56. 22) -4. 41 1. 74*** (2. 38) (0. 35) -12. 32** 3. 61*** (6. 01) (0. 90) Industry Specification Age Employment Scientist Exporter Survival Herfindahl Index Foreign Subsidiaries Appropriability R&D β 1 (H1a, H1b) ICT β 2 (H2a, H2b) R&D × ICT β 3 (H3a, H3b) Collaboration β 4 (H4a, H4b) Spillover β 5 (H5a, H5b) Note:Source:UK Innovation Survey, 1994-2016 and Business Structure Database, 1997-2017: Secure Access. UK Data Service.
g. Cermak et al. , 2010). The decrease stems particularly from reductions of SO 2 emissions from coal use in the residential sector, power plants, and industry. For other regions, evidence is lacking or more anecdotal. However, to understand global climate change, it is relevant to ask to which extent the aerosol climate forcing has become less negative at the global scale. Here, we propose that aerosol trends and their effects can be best investigated in the satellite era since the turn of the century. We analyse multiple observational and model datasets to demonstrate that both ERFari and ERFaci show reduced trends since 2000 in regions which demonstrate a robust and substantial aerosol ERF trend in models.
, 2007; Tørseth et al. , 2012; Cherian et al. , 2014; Crippa et al. , 2016; Costa-Surós et al. , 2020). A similar behaviour is documented for North America (e. g. Streets et al. , 2009; Aas et al. , 2019; Elguindi et al. , 2020). Sulfur and nitrogen deposition over the USA, reflecting anthropogenic emissions, have been declining by between 1 % yr -1 and 3 % yr -1 during the period 1989-2010  (Sickles II and Shadwick, 2015). In contrast, anthropogenic aerosol emissions over China have been increasing until around 2010, and decreasing thereafter  (Klimont et al. , 2017; Zheng et al. , 2018; Aas et al. , 2019; Wang et al. , 2021). The exact temporal evolution of aerosol emissions over the past 20 years, especially over China, was erroneously represented (a too weak decline since 2010) in some emission datasets, leading to some incompatibility of aerosols in the sixth Coupled Model Intercomparison Project (CMIP6;  Eyring et al.
tinued to rise throughout the period 2000-2019  (Klimont et al. , 2013; Wang et al. , 2021). Over remote oceanic regions, ship emissions played a substantial, increasing role during the first part of the time period of interest  (Smith et al. , 2011). Since 2010, they have declined first in emission-control areas  (IMO, 2008)  and since 2020, over much of the global oceans  (IMO, 2019). This declining signal is also visible in cloud properties  (Gryspeerdt et al. , 2019). Here, we consider more specifically emissions from the newest version of the Community Emissions Data System (CEDS; O'  Rourke et al. , 2021). The trends are shown in Fig. 1. A previous version of this dataset was used in CMIP6 and described by  Hoesly et al. (2018). Sulfur emissions were mostly declining since 2000, there were substantial declines over North America and Europe in particular, continuing decreasing trends that started in the last decades of the 20th century.
The analysis of trends from remote sensing, especially from satellite remote sensing, is challenging, because datasets may not be homogeneous over the lifetime of a satellite instrument, due to changing instrument response and satellite orbit. However, for NASA's Earth observation satellites (EOS), Terra and Aqua, care has been taken to avoid many of the issues that hamper satellite trend analysis such as orbital drift  (Levy et al. , 2013). Studies show that trends from various satellites are, at least qualitatively, consistent  (Wei et al. , 2019). Declining trends in aerosols in certain regions, such as over Europe  (Stjern et al. , 2011; Cherian et al. , 2014; Li et al. , 2014; Georgoulias et al. , 2016; Cherian and Quaas, 2020)  and over the USA  (Li et al. , 2014; Jongeward et al. , 2016; Cherian and Quaas, 2020) , as seen from satellite analysis, have been documented earlier.
, 2020; Yu et al. , 2020) , and to quantify the changes between -3. 1 % yr -1 and -1. 2 % yr -1 for the different regions affected by the declines in anthropogenic aerosol emissions from 2000 to 2014  (Mortier et al. , 2020). Mortier et al. (2020)  further documented that climate models were able to reproduce these trends quantitatively. We report aerosol optical depth (AOD) trends from various satellite datasets on a common scale in Fig. 2. It specifically shows AOD and fine-mode AOD (AODFM) from the MODerate Resolution Imaging Spectroradiometer (MODIS;  Levy et al. , 2013)  instrument on board the EOS, Terra and Aqua, and the Multi-Angle Imaging Spectro-Radiometer (MISR;  Garay et al. , 2017)  instrument on board the Terra EOS. Also the -presumably more stable -ground-based retrievals from the AERONET network  (Holben et al.
(2019) , the MODIS trends in the Southern Hemisphere were reported to show stronger positive trends than the six other satellite products they examined for the 2003-2010 period in the oceanic regions of the Southern Hemisphere that they investigated (South Atlantic and Indian oceans). However, in the regions discussed above with pronounced trends in anthropogenic aerosol (precursor) emissions, the satellite trends show the same behaviour qualitatively in all three datasets. These trends are largely consistent with those from AERONET data (circles in Fig. 2 ). The decreasing trends over North America, Europe, and East Asia are clearly seen and statistically significant at many grid points (at 5 % significance level according to a t test with correction as in  Santer et al. , 2008) , as are the increasing trends over India.
The integral, net decline over the 20-year period according to these models was thus 0. 32 W m -2. This result can be compared to the assessment by IPCC AR6  (Forster et al. , 2021). Their assessment is based on multiple lines of evidence that are incorporated in an emulator ensemble simulation. The time series of the diagnosed ERFaer is available via the IPCC website and at https://doi. org/10. 5281/zenodo. 5705391 (Smith et al. , 2021). Computing the linear trend between 2000 and 2019 on the basis of the emulator ensemble yields an increase by +0. 0145 W m -2 yr -1 between 2000 and 2019 (5 %-95 % confidence interval of +0. 0068 to +0. 0253), i. e. by +0. 29 (+0. 14 to +0. 51) W m -2 over the full period  (Gulev et al. , 2021; Forster et al. , 2021). The ERFaer may be inferred from the Earth radiation budget which is measurable at the top of the atmosphere.
, 2021). (Stockholm University), Bjorn Stevens (Max Planck Institute for Meteorology, Hamburg), and Songmiao Fan (GFDL Princeton) for insightful comments. We thank all data producers for making their data available. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modelling groups for producing and making available their model output, and the multiple funding agencies who support CMIP6 and ESGF. We would also like to thank Michael Diamond and two anonymous reviewers for constructive and helpful comments. We further acknowledge the Open Access Publishing Fund of Leipzig University supported by https://doi. org/10. 5194/acp-22-12221-2022 Atmos. Chem. Phys. , 22, 12221-12239, 2022 the German Research Foundation within the programme Open Access Publication Funding.
Data availability. The MODIS cloud products MYD08_D3 from Aqua and MOD08_D3 from Terra were used in this study from the Level-1 and Atmosphere Archive and Distribution System (LAADS) Distributed Active Archive Center (DAAC), https://doi. org/10. 5067/MODIS/MOD06_L2. 006 (Platnick  et al. , 2015a); https://doi. org/10. 5067/MODIS/MYD06_L2. 006 (Platnick  et  al. ,  2015b), and https://doi. org/10. 5067/MODIS/MOD04_L2. 061 (Levy et al. ,  2017a); https://doi. org/10. 5067/MODIS/MYD04_L2. 061 (Levy  et al. , 2017b). MISR data were obtained from the NASA Langley Research Center Atmospheric Science Data Center (https://opendap. larc. nasa. gov/opendap/MISR/MIL3YAEN. 004, Garay et al. , 2017). CERES data were obtained from https://ceres. larc. nasa. gov/data/ (Loeb et al. , 2018a). The Metop-A data are available as PMAp Climate Data Record (CDR) at https://doi.
org/10. 15770/EUM_SEC_CLM_0053 (EUMETSAT, 2022). AERONET data were used from https: //aeronet. gsfc. nasa. gov/data_push/AOT_Level2_Monthly. tar. gz (Holben et al. , 1998; Giles et al. , 2019). RFMIP model output is available from the Earth System Grid Federation (ESGF). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-22-12221-2022-supplement.
Data products We use the gridded data from the Global Ocean Data Analysis Project (GLODAP) to evaluate the model performance. This database is built with bias-corrected water column bottle data -merged with CTD data for salinity -from the ocean surface to bottom  (Olsen et al. , 2020). In particular, we made use of the second update of the second version of the gridded product (GLODAPv2. 2020,  Olsen et al. 2020) , with an improved and extended coverage compared to the original second version  (Lauvset et al. , 2016)  and the first version  (Key et al. , 2004) , especially in the Arctic. GLODAPv2. 2020 -referred to as GLODAPv2 hereafter -contains data from 946 cruises, covering the global ocean from 1972 to 2019 with two quality controls, and adjustments to minimize severe biases. Note that we use the GLODAPv2 product that was normalized to the year 2002 for DIC to avoid biases due to the accumulation of anthropogenic carbon over the observational period.
Alk and DIC initialization As previously discussed, drift can depend not only on the spin-up strategy but also on the initialization strategy. Indeed, it is interesting to examine the initialization of Alk and DIC in ESMs, and to assess how this may influence model performance. Alk and DIC fields are recommended to be initialized using the second version of the GLODAP product (GLODAPv2, Lauvset et al. , 2016) following the OMIP-BGC protocol for CMIP6  (Orr et al. , 2017). For CMIP5, many groups initialized with the first gridded version (GLODAPv1,  Key et al. , 2004)  even though this was not specific to a protocol. GLODAPv2 surface fields are more heterogeneous than GLODAPv1, but it is mainly in the coastal ocean and in particular in the Arctic where the two datasets diverge due to the addition of new observations.
Besides, this absence of aragonite representation could influence projections in ESMs of how the distribution of Alk responds to acidification. The shoaling of the saturation horizon should increase Alk at depth with a reduction in CaCO3 burial (see Sect. 4. 3. 2) but also change the Alk vertical distribution with an increase in dissolution, and thus Alk, in sub-surface waters. There is no consensus on the interaction between POC and PIC in the ocean interior considering a protection and/or ballast effect (see Sect. 3. 1. 3 and Fig. 2Fig. 2). Observational and laboratory data diverge  (Klaas and Archer, 2002; Passow and De La Rocha, 2006; De La Rocha and Passow, 2007; Lee et al. , 2009; Engel et al. , 2009; Moriceau et al. , 2009) , but a recent expedition dataset brings it back to the forefront, especially explaining dissolution in shallow supersaturated waters with a PIC microenvironment influenced by POC remineralization  (Subhas et al.
Acknowledgements: We are very pleased to have been able to work in synergy with all the marine biogeochemistry modelling groups. We acknowledge the  World Climate Research Programme's   Working Group on Coupled Modelling , which is responsible for the  CMIP exercises. For CMIP, the  US Department of Energy's Program   for Climate Model Diagnosis and Intercomparison provided coordinating  support and led the development of software infrastructure in partnership with the  Global Organisation for Earth System Science Portals. This study benefited from the ESPRI (Ensemble  de Services Pour la Recherche à  l'IPSL) computing and data center (https://mesocentre. ipsl. fr, last access: July 2022), which is supported by  CNRS ,  Sorbonne Université, École Polytechnique  and  CNES  and through national and international grants.
Fundings:  Alban Planchat ,  Laurent Bopp ,  Lester Kwiatkowski  and  Olivier Torres  thank the ENS-Chanel research chair. Laurent Bopp  acknowledges the European Union's  Horizon 2020 research and innovation program  under grant agreement No. 820989  (project  COMFORT , Our common future ocean in the Earth system- quantifying coupled cycles of carbon , oxygen, and nutrients for determining and achieving safe operating spaces with respect to tipping points). Momme Butenschön  and  Tomas Lovato  acknowledge funding from the  Italian Ministry of Education, Universities and Research  via the  JPI Ocean and Climate program   CE2COAST. Roland Séférian  and  Laurent Bopp  acknowledge the European Union's  Horizon 2020 research and innovation program  under grant agreement No. 101003536  ( ESM2025 -Earth   System Models for the Future ).
Data availability: We share additional figures (https://doi. org/10. 5281/zenodo. 7144330) to offer an ESM intercomparison (profiles, sections and maps) of the main three-dimensional variables processed in this study (Alk, sAlk, DIC, sDIC, NO3, PO4, CO3, T, S), both for CMIP5 and CMIP6, and in comparison with GLODAPv2 observations. All the ESMs data, both for CMIP5 and CMIP6, were available on at least one of the Earth System Grid Federation (ESGF) nodes, except for CNRM-ESM1 (CMIP5; https://climatedata. cnrm-game-meteo. fr/cnrm/CMIP5/output/CNRM-CERFACS/CNRM-ESM1/) as well as 'dissic' and 'talk' for the piControl of CanESM2 (CMIP5; shared by James R. Christian) which were not available.
Fires emit a substantial amount of non-methane organic gases (NMOGs), the atmospheric oxidation of which can contribute to ozone and secondary particulate matter formation. However, the abundance and reactivity of these fire NMOGs are uncertain and historically not well constrained. In this work, we expand the representation of fire NMOGs in a global chemical transport model, GEOS-Chem. We update emission factors to Andreae (2019) and the chemical mechanism to include recent aromatic and ethene and ethyne model improvements  (Bates et al. , 2021; Kwon et al. , 2021). We expand the representation of NMOGs by adding lumped furans to the model (including their fire emission and oxidation chemistry) and by adding fire emissions of nine species already included in the model, prioritized for their reactivity using data from the Fire Influence.
g. , for savannas, the fraction of NMOC emitted mass that is unidentified is ∼ 50 % -this number is typical across the other ecosystems). They also identify unknown NMOCs as one of the largest sources of biomass burning (BB) emissions uncertainties. The Global Fire Emissions Database version 4 with small fires (GFED4s) inventory  (van der Werf et al. , 2017)  includes the  Akagi et al. (2011)  NMOG EFs. GFED4s includes six land use types (savanna, boreal forest, temperate forest, tropical forest, peat, and agriculture). The Fire Inventory from National Center for Atmospheric Research (NCAR; FINN) v1. 5 also uses the  Akagi et al. (2011)  species-specific EFs, as well as total NMOC and total non-methane hydrocarbon (NMHC) EFs  (Wiedinmyer et al. , 2011). Both the Quick Fire Emissions Dataset (QFED)  (Darmenov and da Silva, 2015)  and Global Fire Assimilation System (GFAS)  (Kaiser et al.
Model description The GEOS-Chem model We use GEOS-Chem (https://geos-chem. org, last access: 15 January 2021), a global CTM, to explore fire NMOGs globally and in specific large fire regions and outflow regions, such as the US, boreal Canada, the Amazon, and Africa. GEOS-Chem is driven by assimilated meteorology from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), from the NASA Global Modeling and Assimilation Office (GMAO). We use version 13. 0. 0 (https://doi. org/10. 5281/zenodo. 4618180; The International GEOS-Chem User Community, 2021) of GEOS-Chem with a horizontal resolution of 2 • × 2. 5 • and 47 vertical levels with a chemical time step of 20 min and a transport time step of 10 min as recommended by  Philip et al. (2016). We perform 12-month spin-up simulations prior to the time periods of interest, June-July 2008, May-June 2012, April-August 2016, January-December 2017, October 2018, and January-December 2019.