dataesr/scientific-paragraphs-categorization huggingface dataset:
Most curatorial effort for GMRT is focused on preparing and integrating multibeam data that are publicly available through the IHO DCDB. Multibeam data processing and curation efforts are focused on the needs of the United States Research Community, with an emphasis on data collected by the United States Academic Research Fleet both during transits and surveys. Data curation efforts include ping editing, sound velocity corrections, adjustments of attitude sensor offsets, the review and assessment of the data in the context of the high-resolution global compilation, and other adjustments necessary to create highquality grids of multibeam data at 100 m resolution or better. Source sonar files that were gridded into the compilation are also available for download. GMRT v. 3. 6, which was released in December 2018, includes edited multibeam data from 1,046 research cruises, conducted between 1980 -2018 aboard 29 different vessels operated by 26 different institutions (Figure  5 ).
GEBCO The general bathymetric chart of the oceans (GEBCO) makes available a range of bathymetric data sets and data products. It operates under the joint auspices of the international hydrographic organization (IHO) and the intergovernmental oceanographic commission (IOC) of UNESCO (United Nations Educational, Scientific and Cultural Organization). The GEBCO chart series has its origins at the beginning of the 20th Century with the initiation of the first chart series by Prince Albert I of Monaco in 1903. Through the 20th Century, five paper editions of the GEBCO chart series were produced (Figure  6 ). In response to the need for digital products, the first edition of the GEBCO Digital Atlas was published on  CD-ROM in 1994. In 2003  the Centenary Edition of the GEBCO Digital Atlas was produced and included GEBCO's first gridded bathymetric product, the GEBCO One Minute Grid.
Published in April 2019, GEBCO's latest grid, GEBCO_2019, is a global terrain model at 15 arc-second intervals, which near the equator is about half a kilometer. This is the first GEBCO grid produced under the framework of the Nippon Foundation-GEBCO Seabed 2030 Project. The GEBCO_2019 Grid uses Version 1 of the SRTM (Shuttle Radar Topography Mission)15_PLUS data set  (Olson et al. , 2014)  as its base. This data set is a fusion of land topography with measured and estimated seafloor topography. The data set is augmented with bathymetric data sets developed by the four Seabed 2030 Regional Centers and the international seafloor mapping community. GEBCO makes available a range of bathymetric products and services, including: -GEBCO_2019 grid. A global terrain model at 15 arcsecond intervals.
A SEAFLOOR MAPPING STRATEGY IS NEEDED Nippon Foundation-GEBCO Seabed 2030 Project Seabed 2030 is a collaborative project between the Nippon Foundation of Japan and GEBCO. It aims to bring together all available bathymetric data to produce the definitive map of the world ocean floor by 2030 and make it available to all  (Jakobsson et al. , 2017). The project was launched at the UN Ocean Conference in June 2017 and is aligned with the SDG 14 -Life below water. The project has established four Regional Centers and a Global Center, is managed by a project director, and is overseen by the GEBCO Guiding Committee. The Regional Centers are responsible for championing mapping activities; assembling and compiling bathymetric information and collaborating with existing mapping initiatives in their regions.
The Global Center is responsible for producing and delivering centralized GEBCO/Seabed 2030 products, such as global bathymetric grids. The most recent GEBCO grid, GEBCO_2019, is the first product of the Seabed 2030 Project. To define the scope of work to populate the map with direct measurement, the project has established a variableresolution and depth-dependent data scheme to be used for determining "mapped" status (Table  1 ), based on the varying resolution of modern hull-mounted swath bathymetry systems as a function of water depth. Using this scheme, an analysis of the source data for the GEBCO_2014 grid, i. e. , those data included in GEBCO before the start of the Seabed 2030 Project, showed that actual bathymetric data were available for approximately 6. 2% of the global ocean   (Mayer et al.
Depth Grid-cell size 0-1,500 m 100 × 100 m 1,500-3,000 m 200 × 200 m 3,000-5,750 m 400 × 400 m 5,750-11,000 m 800 × 800 m grid cells, 6% of those in international waters, and 5. 7% in EEZ. More than two thirds of the data contribution is for grid cells in the 3000-5750 m depth range (Figure  8 ). Using the same scheme, the recently released GEBCO_2019 product has almost 15% of the depth-dependent resolution grid cells based on actual data. Early priorities of the project include identification of existing data that are not yet included in GEBCO products, using sources outlined earlier. Seabed 2030 is working on building relationships with the survey companies and their customers to release the data they hold or own for use in generating the next generation of GEBCO products. Furthermore, it is critical that a concerted effort is made to identify other available sources and how they can be accessed.
Achieving this aim, however, is challenging, especially where the data require to be transferred either via internet or in physical storage devices. First-hand experience in the AusSeabed initiative in Australia has highlighted that transfer of large acoustic seabed data over the internet is difficult for many organizations and can be barrier to accessing these data for producing consistent, consolidated products. In addition, QA of the data prior to submission is necessary to ensure the data can be easily integrated with other existing data. Manual work for preparing and integrating the data and making them available on these endpoint portals is another challenge that can become complicated by the variety of proprietary and open formats commonly used in the community. The Seabed 2030 Project also has, as part of its mission, a requirement to work with the wider bathymetry community to develop strategies for effective mapping.
It is clear that mapping the world's ocean is not a task that can be tackled by one sector or project alone, and that collaboration and coordination across sectors and at a variety of scales is needed. Seabed 2030 is a project borne of this recognition, as presented at the Forum for Future Ocean Floor Mapping, held in Monaco in June 2016. Significant international collaborative efforts already in place within GEBCO, and the bathymetric syntheses described are important components of global mapping initiatives. The success of future mapping efforts will be reliant on the continuation of these existing efforts and appropriate mapping strategies to provide ever-increasing volumes of high quality data from throughout the global oceans. Increasing the flow of existing, and new, high-quality data through the IHO DCDB and other recognized data centers from across the marine community including the international research community, the commercial sector and via crowdsourcing programs, will provide a huge boost to the data availability.
05, ** = p < 0. 01, OR = Odds Ratio; NS = not significant; 95% CI = 95% Confidence Interval. Numbers are reduced due to missing data for demographic data our study identified an 'Ideal Healthy' cluster and a number of clusters containing a combination of healthy and risky HRB. For example, the risky clusters included meeting the PAGL combined with higher alcohol consumption in both sexes. Various associations between the individual behaviours have been observed in the research with positive associations noted for PA and alcohol consumption  [5] , PA and fresh food consumption  [42, 43] , and an inverse association for PA and smoking  [5]. The combination of healthy and risky behaviours may even be explained by a 'work hard, play hard'  [44]  or 'sensation-seeking lifestyle'  [45]  theory. These results help to understand which HRB cluster indefinitely, somewhat or not at all, aiding the creation of interventions that tackle clustered HRB, which have been more effective and less costly in the past  [46].
In addition, if other HRB (e. g. sexual practices) were included, different cluster groups may have arisen, while additional variables (e. g. household income) added to the regression analysis may have altered the results. Lastly, this study was cross-sectional, which means that the data only provides a snapshot of how HRB cluster amongst the population. Understanding the behaviours of university students' is important as both the increase of independent living they experience and the multiple stressors of university life create an environment that supports the engagement of risky HRB  [4]. Health professionals should take note of how HRB cluster when designing multi-health interventions. For example, the targeting of smoking and low PA levels together in females, as opposed to individually, which can have accumulative health effects and be less costly  [46].
Sample selection To examine the relationship between employee well-being and debt maturity, we exploit a firm-level measure of how a firm commits to treat its employees fairly. The source of our data is the KLD STATS (Statistical Tool for Analyzing Trends in Social & Environmental Performance) database that provides information on firms' social ratings. KLD is an independent research provider that specializes in analyzing firms' relations with employees, involvement to community, adherence to diversity standards, improvement of product characteristics, protection of environment, and insurance of good corporate governance. KLD assigns firms social ratings based on a wide variety of data sources, such as company filings, government data, nongovernmental organization data, general media sources, and direct communications with company officers.
After collecting relevant information, KLD's sectorspecific analysts assess the social and environmental performance of firms based on a proprietary analysis grid of positive and negative indicators. Between 1991 and 2000, KLD coverage was limited to approximately 650 companies that comprise the FTSE KLD 400 Social Index and S&P 500. Beginning in 2001, KLD expanded its scope to include the largest 1000 US companies. In 2003, KLD broadened its coverage again to account for the largest 3000 US companies by market capitalization. In this paper, we use all U. S. firms appearing in the KLD database over the period 1991-2014. While most previous studies on employee welfare samples that cover periods beginning in 2003, our analysis includess all periods for which social ratings on KLD are available.
5 Our initial sample is composed of 48,367 firm-year observations. We merge these observations with data from the Compustat database. We exclude unleveraged firms and all observations with missing information on total assets. We also exclude observations with debt maturity values that are less than 0 or greater than 1, as these observations reflect errors. Moreover, we remove financial firms and utilities (firms with primary standard industrial classification SIC codes between 6,000 and 6,999 and between 4,900 and 4,999). We winsorize all the continuous variables at the 1st and 99th percentiles of their distributions to mitigate the effect of outliers. The final sample consists of 19,347 firm-year observations.
Measure of employee welfare The variable of interest is the employee welfare index (EWI), which we derive from the KLD STATS database. KLD screens the covered firms' strengths and concerns of the 'Employee Relations' dimension. It attributes, to each strength and concern, a dummy variable which takes the value of one or zero. Following  Verwijmeren and Derwall (2010)  and  Ghaly et al. (2015) , we create an index that ranges from -5 to +5 from summing strengths and subtracting concerns. +5 indicates the best employee welfare practices. 6 In Appendix B, we summarize these indicators. 5 In robustness tests, we show that our results hold when we restrict the time period to after 2002. 6 Another measure for employee welfare consists of the level of expenditure on employee welfare. However, most of the relevant papers in corporate finance use ratings of the dimension employee relations because of the lack of comprehensive accounting measures for such expenditure.
Alternative periods Our sample consists of all firms that are screened by the KLD database over the period 1991-2014. In 1991, KLD coverage was restricted to companies that comprise the Domini 400 Social SM Index. Beginning in 1995, the database started to screen firms that encompass the Standard & Poor's (S&P) 500 index. In 2003, KLD expanded its coverage to include firms in Russell indexes; the largest 3,000 US companies by market capitalization. As robustness check, we first restrict our sample period to 1995-2014. Second, we use KLD ratings between 2003 and 2014. Finally, we consider the periods spanning from 1991 to 2009 and from 2003 to 2009 since in 2010 some changes have been implemented in the KLD database. We run pooled ordinary least squares regressions over these four time periods.
These firms strive to signal their capacity to deliver on implied promises toward employees about welfare provision, to promote good reputation in the labor market and, ultimately, to preserve firm value from declining. In this paper, we argue that compared to short-term debt, long-term debt allows firms to have more liquidity and to be less constrained to honor their implicit claims about employee welfare. Moreover, long-term debt investors better value sustainability and the increased corporate income in the long run. Thus, they are more likely to favor strong and sustainable employee relations and prefer to invest in socially responsible firms. We assess the effect of firms' commitment to employee claims on its debt maturity structure using an index of employee well-being derived from the KLD STATS database.
Arctic sea ice volume estimates over the past 40 years based on PIOMAS estimations In this introduction let us first concentrate on Arctic sea ice volume deduced from PIOMAS. PIOMAS is a numerical model with components for sea ice and ocean and the capacity for assimilating data from observations (sea ice concentration and sea surface temperature, SST). For the ice volume simulations shown here, sea ice concentration information from NSIDC near-real time product are assimilated into the model to improve ice thickness estimates and SST data from the NCEP/NCAR reanalysis are assimilated in the ice-free areas. NCEP/NCAR reanalysis SST data are based on the global daily high-resolution SST analyses using satellite and in situ observations  (Reynolds et al. 2007). Atmospheric information to drive the model, specifically wind, surface air temperature and cloud cover to compute solar and long wave radiation, are based on the NCEP/NCAR reanalysis.
ERA Interim ERA-Interim is a data set based on a global climate reanalysis from 1979 to date. ERA stands for "European Reanalysis" and refers to a series of research projects at ECMWF which produced various datasets (including ERA-Interim, ERA-40 etc…). ERA-Interim uses a fixed version of a numerical weather prediction (NWP) system based on a data assimilation Integrated Forecast System (IFS-Cy31r2) to produce reanalyzed data. The system includes a 4-dimensional variational analysis (4D-Var) with a 12-hour analysis window  (Simmons et al. [2004]  and Berrisford et al. [2011] ). We took advantage of the 2m air temperature produced by ERA-Interim at a spatial resolution of 0. 75 degree and a temporal resolution of 6 hours from which we calculated the daily average. Then we eliminated all the data South of 60°N and all the data on land (i.
𝐹𝐷𝐷 =  -   0 Eq. (  1 ) The air temperature Ta at 2m altitude is provided by ERA Interim every 6 hours for each ERA interim grid cell. We calculated Ta daily average and then we estimated the cumulative FDD for each ERA interim grid cell by integrating the difference between sea water freezing temperature Tf = -1. 7°C and Ta from September 1 to September 30 (1 month), then from September 1 to October 31 (2 months), then from September 1 to November 30 (3 months) etc…. until we covered the whole freezing time period lasting for 9 months from September to May each year. We also calculated the surface (km 2 ) for each individual grid cell from the ERA-Interim data file using the following formula [0. 75*110]^2*cos[lat x] x being the latitude.
We would like to thank the EU projects ACCESS (EU Grant Agreement N°265863) and ICE-ARC (EU Grant Agreement N°603887) and the Pan Arctic Options Belmont Forum project (ANR-14-AORS-003-01). ERA Interim data set can be found at ftp://ftp. climserv. ipsl. polytechnique. fr/erai/GLOBAL_075/4xdaily/AN_SF/ JZ is supported by the NASA Cryosphere program (NNX17AD27G). The PIOMAS sea ice volume data we used are available at http://psc. apl. uw. edu/research/projects/arctic-sea-ice-volume-anomaly/. Regarding sea ice thickness PIOMAS data are available at http://psc. apl. washington. edu/zhang/IDAO/data_piomas. html. We also would like to thank R. Tilling and A. Ridout (UK Centre for Polar Observation and Modelling) for allowing us to make use of the Cryosat-2 data available at http://www. cpom. ucl.
g. size distribution, particle type) and field observations to estimate atmospheric deposition fluxes  (Mahowald et al. , 2005; Zender et al. , 2003; Zhang et al. , 2015). Often, modelling approaches simulate atmospheric deposition fluxes more accurately in regions downwind from the main aerosol sources  (Huneeus et al. , 2011; Wagener et al. , 2008)  because the aerosol characteristics, due to extensive datasets in these regions, are better constrained in proximity to their source. Modelled atmospheric deposition fluxes often rely on satellite-derived climatologies. The satellite-derived climatologies use properties (i. e aerosol optical depth) which suffer from cloud coverage and are biased towards clear-sky conditions  (Huneeus et al. , 2011). In this paper we present surface ocean dissolved aluminium (dAl) concentration data for the Atlantic Ocean and use these to calculate atmospheric deposition fluxes using the MADCOW model  (Measures and Brown, 1996).
geotraces. org, last access: 1 January 2018). After collection, the samples were acidified to pH ≈ 1. 8 with ultra-clean hydrochloric acid (UpA, Romil, 0. 024 M) and double bagged until analysis. Analysis of dAl during GA01, GA06, and GA10 used the flow injection analysis (FIA) method developed by  Resing and Measures (1994)  and further modified by  Brown and Bruland (2008). Dissolved Al during GA08 cruise was analysed using the batch lumogallion method  (Hydes and Liss, 1976). The analytical figures of merit for the dAl datasets can be found in Table  S2.
Along with Fig. S2  we provide an explanatory note on the relative contribution of each air mass for each region which we then compiled in Table  S3. They calculated the fractional Al solubility for each subregion. Baker et al. (2013)  provide a good spatial coverage of the Atlantic Ocean, thus allowing us to assign values of Al fractional solubility for different biogeochemical regions (Fig. 1 ) based on field observations. However, the aerosols Al fractional solubility might not be representative over an annual timescale due to, for example, the pulses of Saharan dust and where the mineral dust falls which is related to the position of the ITCZ. Yet, it is still the largest dataset on aerosol Al fractional solubility over the Atlantic Ocean. We produced a single weighted averaged aerosol Al fractional-solubility percentage value for each area of interest based on the Al fractional solubility for each air mass to the relative contribution of that air mass to a certain area (Table  S3 ).
Al residence time in the surface mixed layer The residence time is defined as the ratio of the dAl inventory in the surface mixed layer to the rate of input or removal. Residence times of dAl in the surface mixed layer were obtained from  Han et al. (2008)  and are based on an extensive surface water dAl observational database (including 22 cruises in the Atlantic Ocean). The latter residence times were derived using a fixed mixed-layer depth (50 m) and a constant Al fractional solubility (5 %) and take into account Al sources to the surface mixed layer from atmospheric deposition, advection, and mixing. The MADCOW model does not account for advection and mixing processes which, for example, occur in equatorial regions  (van Hulten et al. , 2013). Thus, the use of modelled residence times which account for advection and mixing sources could partially counterbalance the error associated with our modelled atmospheric deposition fluxes.
, 2015 Measures et al. , , 2008;; Measures and Vink, 2000; Pohl et al. , 2011; Schlosser et al. , 2014). These studies relate the high Al values either to wet deposition and/or large dry deposition pulses. Figure  5  shows average, maximum and minimum Al concentration data (filtered and unfiltered) collected over several cruises from approximately 25 • S to 25 • N. The single datasets used for the figure are shown in Fig. S4. From Figs. 5 and S4 we can observe that Al values between 8 • S and 17 • N are highly variable with a large range between maximum and minimum values. This high variability is a consequence of seasonal migration of the ITCZ and the seasonal nature of mineral dust plumes which vary in intensity on a daily basis  (Patey et al. , 2015). These factors, in addition to equatorial upwelling  (Vink and Measures, 2001) , will impact particle fluxes (biogenic and non-biogenic)  (Chester, 1982; Kuss et al.
The higher the Al fractional solubility value, the less aerosol material is required to maintain the observed dAl surface mixed-layer concentrations. We acknowledge the lack of use of Al solubility values in rain, which has been estimated to account for 89 % of the to-tal deposition to the ocean  (Zender et al. , 2003). However, the amount of published data of Al fractional solubility in rain is still minor  (Heimburger et al. , 2013; Losno et al. , 1993). The option to link the few values to global precipitation models  (Liu et al. , 2012 ) seems, at this stage, a more qualitative than quantitative approach in comparison to the larger amount of Al fractional solubility data available for dry aerosols.
e. potential reducers of dAl concentrations in surface waters). This implies an added difficulty in assessing a residence time for Al. The residence time in the NATR could be lower, in the range of weeks, as postulated in previous works  (Croot et al. , 2004). Assuming a residence time of 0. 3 years (original residence time set up to 1. 25 years), for the NATR region would yield atmospheric deposition fluxes ranging between 4. 96 ± 1. 86 and 19. 37 ± 7. 25 g m -2 yr -1 (average 12. 32±4. 66 g m -2 yr -1 ) which, within uncertainty, are equal to those provided by modelling approaches (see above,  Mahowald et al. , 2005; Duce et al. , 1991; Zender et al. , 2003). These results do not match the observations (from field data and satellite retrievals) and this suggests that atmospheric deposition fluxes calculated with the MADCOW model less reliable and likely underestimated in the tropical North Atlantic Ocean if seasonal variations in the residence time of Al are not accounted for.
We acknowledge that there are regions of the Atlantic for which the application of the MADCOW model has limitations (i. e. tropical Atlantic Ocean). In these regions, more work is needed to constrain parameters such as aerosol Al fractional solubility and the residence time of Al within the surface mixed layer. Realistic residence time values would significantly improve our atmospheric deposition flux results. We suggest that forthcoming expeditions should use more than one technique to calculate atmospheric deposition fluxes as different methods provide us with complementary information. The new Intermediate Data Product 2017  (Schlitzer et al. , 2018)  from the GEOTRACES programme along with upcoming dAl datasets will give us a better coverage of under-sampled regions which, combined with historical data, will us to refine atmospheric deposition fluxes (i.