dataesr/scientific-paragraphs-categorization huggingface dataset:
The tasks seemed dantean because of two major issues, central to the usability of a database, to be solved as a pre-requisite. One is intercomparing measurements originating from different cruises/ instruments/scientists, and another is establishing rigorous quality control and flagging of the data. To get global concentration fields, methods to inter/ extrapolate in space and time are used introducing uncertainties in the resulting concentrations. Including the uncertainty in gas exchange velocity parameterisation will yield substantial uncertainties in air/sea flux calculations. Any progress in the future in this velocity parameterisation will allow recomputation of fluxes from the existing global concentrations data sets. This section presents some specific initiatives carried out within the COST Action 735 and SOLAS Project Integration framework trying to highlight in each case the difficulties encountered, the solutions found and the recommendations proposed.
MEMENTO (MarinE MethanE and NiTrous Oxide) Database The assessments of radiative forcing from long-lived greenhouse gases such as nitrous oxide (N 2 O) and methane (CH 4 ) depend on an accurate synthesis of the global distribution and magnitudes of N 2 O and CH 4 sources and sinks (see Chap. 3 of this book). Atmospheric dry mole fractions of N 2 O and CH 4 have been routinely available since the late 1970s and they benefit from a highly coordinated global monitoring network (see e. g. Prinn et al. 2000). In stark contrast, although measurements of marine N 2 O and CH 4 date back over almost four decades, they lack the temporal continuity and spatial coverage of their atmospheric counterparts. This is because almost all of them relate to single cruises or at best coordinated cruise programmes of rather limited scope.
In large part this reflects the high costs and organisational difficulties of mounting large coordinated oceanographic expeditions, especially at the international level. Not surprisingly, oceanic data coverage remains fragmentary. Seasonal and interannual variability, spatial heterogeneity in coastal areas and gradients between coastal and open ocean areas all impact the quality of marine emission estimates for N 2 O and CH 4 , compounded by the limited overall data coverage  (Bange et al. 2009)  Once all existing datasets have been incorporated into MEMENTO it will rapidly become a valuable tool for identifying regions of the world ocean that should be targeted in future work to improve the quality of the emissions estimates. The locations of the data archived so far in MEMENTO are shown in Fig.
HalOcAt (Halocarbons in the Ocean and Atmosphere) Compilation of existing air and seawater measurements of halogenated hydrocarbons into the project HalOcAt (https://halocat. geomar. de/) was initiated through SOLAS/COST Action 735 in May 2009 and is still ongoing. Global oceanic and atmospheric halocarbon data with an emphasis on shortlived brominated and iodinated trace gases from the surface ocean and lower atmosphere have been collated in order to obtain concentration fields and air-sea fluxes. At the time of writing, the database contains 191 contributions, comprising roughly 55,400 oceanic and 476,000 atmospheric data points from depth profiles in the surface ocean and a range of heights in the lower atmosphere. Although predominantly bromoform measurements, these data represent 19 different halocarbon compounds and were collected between 1989 and 2011 during research cruises, aircraft missions and coastal studies from all over the globe.
The database stems from active submission of data, literature review and publicly available data. Quality checks as well as precision and error estimates were often missing from the contributions, coinciding with a large range in reported concentrations. Based on the current available scientific knowledge it is unfortunately not possible to retrospectively identify which data is 'correct' or 'incorrect'. Especially for oceanic data, recalibration is highly unlikely and the database is too small to perform a statistical evaluation of the data quality or to take advantage of utilising datasets that physically crossed paths for inter-calibration. There is some hope that it might be possible to check the consistency of atmospheric data from time series stations, and the community is already assessing inter-calibrations for some compounds  (Jones et al.
DMS-GO (DMS in the Global Ocean) In 1999 Jamie Kettle and Meinrat Andreae  (Kettle et al. 1999 ) initiated a collation of available seawater DMS measurements into a global database and subsequently created a climatology of surface ocean concentrations based on the data available. However, this important contribution to the literature (over 285 citations to date) was limited by the number of data points (15,617) of surface seawater DMS concentration and the large areas of the global ocean that lacked data, requiring crude estimates to be made. In the years since, many more DMS measurements have been made and in 2010 the DMS-GO (DMS in the Global Ocean) team initiated a call to update the database and climatology. In April 2010 the DMS database had grown to approximately 47,000 data points (Fig.
After these two steps, the construction of the climatology was based on the combination of 1 Â 1 pixels and the background field. The new DMS climatology along with an estimate of the uncertainty (upper and lower concentration bounds) is available online for free download on the SOLAS-BODC server (http://www. bodc. ac. uk/solas_integration/ implementation_products/group1/dms/). The global sea-to-air DMS flux estimate (between 17. 6 and 34. 4 Tg S year À1 ) using the new climatology has improved understanding of ocean-atmosphere DMS emissions in the global sulphur cycle and suggests an approximate 17 % increase in flux c. f. the previous climatology, mainly due to the inclusion of data from new areas of the ocean (e. g. the Indian Ocean). The updated distribution of global DMS measurements and the new DMS climatology will be useful for the validation of future ocean biogeochemical models.
In addition, the continuous climatological surface ocean DMS field will likely be used as an input variable for atmospheric chemistry models. However, the updated DMS database has also highlighted areas of the global ocean that require more measurements and this is powerful information in the context of recent developments in automatic and semi-automatic DMS analysis systems. As new Fig. 5. 17 Global air-sea flux climatology of bromoform in pmol m À2 h À1 including surface oceanic bromoform data points (white points) extracted from the HalOcAt database techniques become more widely-used the challenge will be ensuring data inter-comparability as the database swells in size (see  Bell et al. 2011).
The Surface Ocean CO 2 ATlas (SOCAT) The Surface Ocean CO 2 ATlas (SOCAT) is a global synthesis of surface ocean carbon dioxide (CO 2 ) measurements collected on research vessels, voluntary observing ships and moored as well as drifting platforms (http://www. socat. info/). The first public release of SOCAT (version 1. 5) took place at UNESCO in September 2011. Version 1. 5 consists of 6. 3 million quality controlled, uniform format and recalculated surface water fCO 2 (fugacity of CO 2 ) data from 1851 voyages in the global ocean between 1968 and 2007 (Fig. 5. 19). SOCAT was initiated at the Surface Ocean CO 2 Variability and Vulnerabilities (SOCOVV) workshop in 2007  (IOCCP 2007). At that time surface ocean CO 2 data were archived in a wide range of formats and at numerous sites around the world, each with its own rules for access, and documentation of the data was frequently poor.
This made it virtually impossible to generate comprehensive data synthesis products for large scale or long-term studies. To alleviate this situation the international ocean carbon community decided to initiate the Surface Ocean CO 2 Atlas as a community driven effort to assemble, harmonise, quality control and document the surface ocean CO 2 data into one open access database. While the outlook of the SOCAT products in version 1. 5 has closely followed this original vision, as documented in the 2007 meeting report, the community underestimated the amount of work involved in the first release of SOCAT. It took 4 years of hard work by marine carbon scientists around the world to assemble and quality control the first version of SOCAT. Two SOCAT products are available: 1. A global data set of recalculated surface water fCO 2 values in a uniform format, which has undergone 2nd level quality control; 2.
A global, gridded product of monthly mean surface water fCO 2 , with no temporal or spatial interpolation. The above SOCAT products can be accessed at the Carbon Dioxide Information Analysis Center (CDIAC, http://cdiac. ornl. gov/oceans/) on a global and basin wide level. In addition to the concatenated data, products are all recalculated and available at the ICSU World Data Centre PANGAEA -Data Publisher for Earth & Environmental Science (http:// www. pangaea. de/) as individual cruise files. Those files Fig. 5. 18 Locations of global surface ocean DMS measurements (approx. 47,000 as of April 2010) for all months of the year (white) plotted over the mean climatological DMS concentration (in nM) from the same data (Data is available for download at http://saga. pmel. noaa. gov/ dms/) are in a uniform format and give access to the detailed metadata, input data and recalculated fCO 2 values.
SOCAT tools include an online data visualisation and analysis tool (PMEL's Live Access Server) and desktop tools like an Ocean Data View Collection. All can be accessed via http://www. socat. info/. The methods in SOCAT are fully documented  (Pfeil et al. 2013; Sabine et al. 2013). The products and individual cruises are citable through Digital Object Identifiers (DOI-s). Cruises in SOCAT have an Expocode (a code containing Cruise ID, Year, Month and Day of Cruise), a DOI and detailed information on the data, so-called metadata (e. g. investigator name, vessel, methods, calibrations). Every data point in SOCAT has a link to its cruise file as archived at PANGAEA via the DOI string in the data file. Preparations for the second version of SOCAT are underway. Regular releases of SOCAT are planned, e.
g. every 1-2 years from the 3rd release onwards. Data can be submitted to CDIAC (http://cdiac. ornl. gov/oceans/submit. html) for inclusion in future SOCAT releases. Prompt data and metadata submission as well as citation of SOCAT in publications are deemed essential for the future existence of SOCAT. Automation of data and metadata submission and quality control as well as inclusion of additional variables (e. g. atmospheric CO 2 and calculated fCO 2 from discrete measurements) is currently being discussed. SOCAT meets the needs of the global carbon community by making high quality surface water fCO 2 data available for addressing major scientific questions in the field of global change. The large number of visitors to the SOCAT website (> 1,000 hits/month) demonstrates the intense interest in these carbon synthesis products.
Aerosol and Rainwater Chemistry Database The development of the aerosol and rainwater chemistry database is motivated by the desire to provide a repository for datasets collected from ships at sea because there had previously been no facility for collecting such data, while databases for land-based measurements are already well established (e. g. the World Data Centre for Aerosols; http://wdca. jrc. it/ data/parameters/data_chem. html). The database has a deliberately broad focus in terms of the chemical species accepted, with nutrients, trace metals and organics being primary targets, but other data equally welcome. So, for instance, iron data may be a key interest for the database, but submitters of iron data are encouraged to also supply any other chemical data associated with those measurements.
This ancillary data may be of use to the iron community as well as in other, unrelated research fields. Data has been collated at BODC since 2007 and a data portal was added to the site in 2011 (http://www. bodc. ac. uk/solas_integration/implementation_products/ group1/aerosol_rain/). At the time of writing the database has ~1,300 aerosol and ~80 rainfall data points, most directly downloadable from the website (Fig. 5. 20). The site also contains links to other on-line databases holding related data and seven datasets of aerosol chemistry obtained from island sites which are not housed in any other publicly available database. Baker et al. 's study (2010) is a good example of the potential benefits attainable from large ship-based datasets. Aerosol and rain data obtained during 12 cruises was used to estimate atmospheric nitrogen inputs to the Atlantic Ocean.
This was achieved partly through a process of classifying the aerosol samples in their database according to which source regions they had recently passed over, and marrying these classifications, and their chemical characteristics, to an air mass climatology for the Atlantic basin. The applicability of this approach is strongly determined by the availability of data, and its spatial and seasonal distribution, as well as the seasonal variation in atmospheric source strength. For example, a subtly different approach has been required for recent attempts to estimate atmospheric iron inputs to the Atlantic  (Powell et al. in preparation). To date, the COST Action 735 database contains only chemical and very limited metadata such as sample positions, but no other potentially valuable resources such as air mass back trajectories.
A Data Compilation of Iron Addition Experiments In the last 3 years datasets from ten mesoscale iron enrichment experiments have been brought together through a SCOR working group (WG 131) entitled 'The Legacy of mesoscale ocean enrichment experiments'. This data collation has then been transformed into a relational database (the Iron Synthesis Database) by the database management team (Cyndy Chandler & Steve Gegg at BCO-DMO) based at Woods Hole Oceanographic Institution (WHOI) in the USA (http://bcodmo. org/data). The database has been structured using project and data directories, metadata, the status of data rescue for each project, and tools that are available to discover and download data of interest (see  Fig. 5. 21). Datasets from the following experiments reside at the BCO-DMO site: IronEX I, IronEX II,   2006).
The datasets within the relational database consist of physical (e. g. mixed layer depth), chemical (e. g. dissolved iron concentrations), biological (e. g. net primary production rates), optical (e. g. incident irradiance) and in some cases meteorological (e. g. wind speed) parameters. The ten experiments all straddle High Nitrate Low Chlorophyll (HNLC) regions from polar to tropical HNLC waters and hence provide a robust test for modeling studies and comprehensive details for synthetic studies. For example, datasets concerning the production of DMSP and its subsequent transformation to DMS are available for multiple experiments including SOIREE and SERIES. Such datasets can be readily related to a wide range of environmental properties such as mixed layer depth, incident irradiance, and microbial rate processes.
The interplay of these factors has been proposed as important in setting DMS concentrations following iron enrichment (Le  Clainche et al. 2006). Data are also available concerning changes in the concentration of other biogenic gases following iron enrichment, and in some cases their efflux and fate. The main challenge in setting up this relational database was obtaining datasets from some of the first in situ experiments -such as IronEX I and II -and from some of the more recent experiments. In the case of the early studies from almost two decades ago, some of the data was on very old laptops. Alternatively, for the most recent experiments, manuscripts are still being written and published and at this point there was an understandable reluctance to contribute to the public domain. So what is the future for large-scale Fe addition experiments and what else would we really wish to better understand? Some scientists have called for larger experiments (100 km length scale) to overcome some of the artefacts such as dilution with surrounding HNLC waters, but these bring enormous logistical challenges with them.
Conclusions The initiatives discussed in this section represent the spectrum of integration activity within SOLAS. MEMENTO and the aerosol and rainwater chemistry databases are still in relative infancy, just beginning to collate data. In contrast, the relatively mature DMS database has recently been substantially updated, tripling the number of data points and re-estimating the climatological DMS flux to the atmosphere  (Lana et al. 2011). Ultimately, the SOCAT project is leading the field, with a lot of data points collated from the global ocean and well established procedures for treating the data and archiving it in a responsible manner. Its progress and willingness to continue pushing forward the boundaries of data collation and synthesis are admirable. Meanwhile, the synthesis of data and information from the various large-scale iron addition experiments has been invaluable.
Past experience suggests that such data collation exercises often prove their worth many times over, informing the scientific community in ways that were previously unforeseen. For example, the original DMS database attempted to find correlations between in situ concentrations and other ancillary parameters such as chlorophyll a  (Kettle et al. 1999). Despite a relative lack of success at the time, understanding of the reduced sulphur cycle has benefitted enormously from this database -much of the work carried out after the initial data collation period has arguably proven to be much more fruitful. Without attempting to predict the future, a similar scenario appears to exist with the HalOcAt database, with initial examinations of the data suggesting no obvious correlating factors; only time will tell.
What challenges remain for the integration of SOLAS data? Success depends in part on the scope of the project. Based on the projects outlined within this chapter, data collation initiatives must overcome the following issues common to many of the databases: Community engagement. The number of scientists willing to be a part of any initiative and who actively engage with the process can have a significant impact on the scale of data collation and the degree of community involvement. Engaging the community to deliver to data bases is an extremely time consuming effort which requires strong motivation and determination. Support for the project. This can take the form of a website, advice on data management and/or project personnel, often data managers. Existing projects that have had resources for a data manager have benefitted enormously from this.
Data management should be an intrinsic part of any project. Approaches to data paucity. Whether extrapolating to biogeochemical provinces (e. g. DMS-GO), performing deep ocean cross over checks (e. g. SOCAT) or using aerosol chemical composition data to characterise different air masses within an Atlantic-scale dataset, these techniques are very important for 'filling' data gaps and estimating global fluxes. The techniques need to be adequately described stating clearly the assumptions being made and resulting uncertainties. Data intercomparability. Arguably, this is one of the major challenges facing many datasets within SOLAS science. Collating a large-scale dataset is almost useless if the data are subsequently shown to be incomparable. An obvious way forward is to carry out regularly, at the international level, measurement intercalibration exercises.
Global Climatologies Based on Observations The need for a global climatology of DMS sea-surface concentrations was identified more than a decade ago. The first global database of DMS measurements was put together by  Kettle et al. (1999)  and  Kettle and Andreae (2000). At the time, the database compiled of the order of 15,000 data points, unevenly distributed in time and space. Since then, the original database has been growing extensively and has now reached more than 45,000 data points (see: http://saga. pmel. noaa. gov/dms/). This on-going extension has been made possible thanks to the scientific community feeding the database with new measurements, but also thanks to the DMS-GO initiative (see  Sect. 5. 1. 4. 3)  and SOLAS integration (Surface Ocean Lower Atmosphere Study, http://www.
bodc. ac. uk/solas_integration/). As discussed in  Sect. 5. 1. 4. 3, Lana et al. (2011)  have used this updated database to obtain a new global climatology of sea surface DMS concentrations following a modified interpolation approach to the one used by  Kettle et al. (1999) (Fig. 5. 22). They obtained a global annual sea-to-air DMS flux, estimated at 28. 1 Tg S year À1 (17. 6-34. 4), which represents a global emission increase of 17 % with respect to previous calculations. Regionally, annuallyaveraged concentrations show rather homogeneous values, most of them between 1 and 5 nM. The characteristics of the seasonal cycle, already shown by  Kettle et al. (1999) , are mostly confirmed: maximum concentrations (up to 15-20 nM) are obtained in summer at high latitudes, in phase with chlorophyll a.
2011). These recent studies illustrate that one way to reduce the very high uncertainties associated with SSA source functions determined from laboratory and field measurements is via integrative studies that combine these source functions in CTMs and GCMs and use top-down constraints based on in situ and satellite observations. Compilations of aerosol observations such as the SOLAS aerosol database (Sect. 5. 1. 4. 5), with a wide distribution over most of the ocean basins are extremely useful in this endeavour.
A cost effective way of using all existing N 2 O and CH 4 measurements, despite the data limitations, is to establish a global database to improve the value of marine emissions estimates. To this end the MEMENTO (MarinE MethanE and NiTrous Oxide) database has been launched as a joint initiative between SOLAS and COST Action 735. MEMENTO's major aims are to: • Collect available N 2 O and CH 4 data (i. e. underway and depth profile data) from the global ocean including coastal areas. To date 129,012 N 2 O and 21,003 CH 4 measurements have been collated; • Archive the data in a database with open access for the scientific community; • Compute global fields of dissolved N 2 O/CH 4 concentrations as well as air-sea fluxes in both the open and coastal ocean, and; • Publish the database and the derived flux data.
White arrows point to the centrosomes. Bar, 10 µm. (C) Quantification of PPFR-1 staining at anaphase and telophase centrosomes expressed as a percentage of enrichment relative to cytoplasmic level. Wild type, n = 15; mel-26 n = 8; mei-1(ct46gf), n = 7 (*, P < 0. 05; **, P < 0. 01; NS, not significant; t test). Error bars, 95% CI. (D) The CRL3 MEL-26 E3-ligase targets both MEI-1 and its activator PPFR-1 for degradation to regulate MT severing upon meiotic exit. at room temperature by time-lapse Nomarski microscopy using an Axioimager microscope (Carl Zeiss), 100× objective, and ICc1 camera (Carl Zeiss) controlled by Axiovision software (Carl Zeiss). The maximal crosssectional area of the PB was measured using Axiovision software. Spindle length, spindle orientation, and fluorescence intensity were measured using Image J/Fiji Software.