dataesr/scientific-paragraphs-categorization huggingface dataset:
Consequently, these results have been interpreted as evidence for the presence of partial transcripts during the gametophyte generation. To determine whether the ORO and SAM transcripts produced in Ectocarpus were incomplete, RNA-seq data from male and female, immature and mature gametophytes was mapped onto the ORO and SAM gene sequences. This analysis indicated that full-length transcripts of both the ORO and SAM genes are produced during the gametophyte generation (.
Searches for HD proteins from other stramenopile species Searches for homeodomain proteins from additional brown algal or stramenopile species were carried out against the NCBI, Uniprot, oneKP  (Matasci et al. , 2014)  and iMicrobe databases and against sequence databases for individual brown algal (Saccharina japonica,  Ye et al. , 2015; Cladosiphon okamuranus, Nishitsuji et al. , 2016)  and stramenopile genomes (Nannochloropsis oceanica, Aureococcus anophagefferens, Phaeodactylum tricornutum, Thalassiosira pseudonana, Pseudo-nitzschia multiseries) and transcriptomes (Vaucheria litorea, Heterosigma akashiwo) using both Blast (Blastp or tBlastn) and HMMsearch with a number of different alignments of brown algal TALE HD TF proteins. As the homeodomain alone does not provide enough information to construct well-supported phylogenetic trees, searches for ORO and SAM orthologues were based on screening for the presence of the additional protein domains conserved in brown algal ORO and SAM proteins.
ORO and SAM domain structure The conserved domains that flank the homeodomains in the ORO and SAM proteins share no detectable similarity with domains that are associated with TALE HDs in the green (Viridiplantae) lineage, such as the KNOX, ELK and BEL domains. Interestingly, both the ORO and SAM proteins possess regions that are predicted to be highly disordered (Figure  5B ). Intrinsically disordered region are a common feature in transcription factors and the flexibility conferred by these regions is thought to allow them to interact with a broad range of partners  (Niklas et al. , 2015) , a factor that may be important for master developmental regulators such as the ORO and SAM proteins. The following previously published datasets were used:.
Ec32_ORO/214-286  Sdo_ULXR-2001726/203-275  Slo_JCXF-2074466/204-276  Pfa_VRGZ-2021267/205-277  Pla_ASZK-2009051/235-307  Csi_QLMZ-2080353/202-274  Cok_s017_4976/190-262  Upi_FIDQ-2014738/224-296  Kcr_RAPY-2013466/227-299  Sja_SJ07622/227-299  Smu_JGGD-2080534/190-262  Hfu_LDRY-2116478/190-262  Iok_APTP-2015225/253-325  Dun_LIRF-2009250/198-268  Hea_231575mod/214-286    Funding Funder Grant reference number.
The flow (flux) of climate-critical gases, such as carbon dioxide (CO 2 ), between the ocean and the atmosphere is a fundamental component of our climate and an important driver of the biogeochemical systems within the oceans. Therefore, the accurate calculation of these air-sea gas fluxes is critical if we are to monitor the oceans and assess the impact that these gases are having on Earth's climate and ecosystems. FluxEngine is an open-source software toolbox that allows users to easily perform calculations of air-sea gas fluxes from model, in situ, and Earth observation data. The original development and verification of the toolbox was described in a previous publication. The toolbox has now been considerably updated to allow for its use as a Python library, to enable simplified installation, to ensure verification of its installation, to enable the handling of multiple sparingly soluble gases, and to enable the greatly expanded functionality for supporting in situ dataset analyses.
This new functionality for supporting in situ analyses includes user-defined grids, time periods and projections, the ability to reanalyse in situ CO 2 data to a common temperature dataset, and the ability to easily calculate gas fluxes using in situ data from drifting buoys, fixed moorings, and research cruises. Here we describe these new capabilities and demonstrate their application through illustrative case studies. The first case study demonstrates the workflow for accurately calculating CO 2 fluxes using in situ data from four research cruises from the Surface Ocean CO 2 ATlas (SOCAT) database. The second case study calculates air-sea CO 2 fluxes using in situ data from a fixed monitoring station in the Baltic Sea. The third case study focuses on nitrous oxide (N 2 O) and, through a user-defined gas transfer parameterisation, identifies that biological surfactants in the North Atlantic could suppress individual N 2 O sea-air gas fluxes by up to 13 %.
Introduction The exchange of climate relevant gases between the oceans and atmosphere including that of carbon dioxide (CO 2 ), nitrous oxide (N 2 O), and methane (CH 4 ) is a major component Published by Copernicus Publications on behalf of the European Geosciences Union. of the climate system, and the ability of the oceans to absorb and desorb these gases varies both temporally and spatially. The need to monitor this exchange has been the driver for international data collation initiatives such as the Surface Ocean CO 2 ATlas (SOCAT;  Bakker et al. , 2016)  and the Ma-rinE MethanE and NiTrous Oxide database (MEMENTO;  Kock and Bange, 2015). These collaborative efforts are now routinely collecting, quality controlling and collating over a million new in situ data points each year. FluxEngine complements these initiatives by providing a standardised tool which can robustly calculate air-sea gas fluxes from such in situ data, with the flexibility to incorporate new data sources and methodologies.
Overview of FluxEngine FluxEngine is a flexible open-source toolbox that allows users to easily exploit Earth observation and model data, in combination with in situ data, to calculate air-sea gas fluxes  (Shutler et al. , 2016). It is available to download from http://github. com/oceanflux-ghg/FluxEngine (last access: 6 December 2019). The toolbox uses plain-text configuration files, allowing the user to configure the input data sources, random noise or bias on input data, the temporal period for the analysis, the structure of the air-sea gas flux calculation, and user-defined gas transfer velocity parameterisations. The calculation itself can be performed using fugacity, partial pressure, or concentration data via a user-defined bulk formulation, including formulations that can account for vertical temperature gradients across the mass boundary layer, the very small layer at the surface over which gas exchange occurs.
, 2015). The toolbox has also been incorporated within undergraduate and postgraduate teaching at the University of Exeter within geography, environmental science, and marine biology degrees, and at Utrecht University for computer science. This paper uses four case studies to illustrate key developments and the extended capabilities now contained within version 3. 0 of the FluxEngine toolbox. Collectively, the case studies illustrate user-selectable grids, support for calculating sea-to-air gas fluxes from time series data collected by fixed monitoring stations and research cruises (and how to incorporate the flux outputs into the original dataset to create a coherent time series), the ability to calculate nitrous oxide (N 2 O) sea-to-air gas fluxes, and the addition of a new forcing variable (kinetic energy dissipation rate).
The extensive support for in situ data contained within version 3 of Flux-Engine means that it can now be fully exploited by three different scientific communities in isolation: in situ, model, and Earth observation, whilst the original capability to enable gas fluxes to be calculated from combinations of in situ, model, and Earth observation data is retained. Section 2 of this paper describes the structural extensions and changes and explains how the toolbox can now be used as a command-line tool or as a Python library. Section 3 then presents the case studies, and Sect. 4 outlines the future direction and developments for the toolbox; Sect. 5 gives conclusions. To aid the user, the appendices of this paper provide a list all of the toolbox utilities (Appendix A), details of all datasets used in the case studies (Appendix B), an overview of the main toolbox features and options (Appendix C), and a description of the automatic software installers and verification tools (Appendix D).
Installation, verification, and use FluxEngine has now been optimised for use on a stand-alone desktop or laptop computer, removing the previous requirement for specialist computing facilities. Installation tools or instructions are provided for Microsoft Windows, Apple MacOS, and Ubuntu/Debian-based Linux operating systems. Separate utilities can then be used to verify that FluxEngine has been successfully installed. Details of the installation, verification process, and execution times are provided in Appendix D. FluxEngine is now implemented as a Python module, which means that FluxEngine and its accompanying utilities can be imported as a Python module and easily integrated with other software or use as stand-alone tools that are called from the command line. This approach offers a larger degree of flexibility than offered by version 1 of the toolbox and supports advanced exploitation.
Flexible input data specification Previous versions of FluxEngine required the user to make changes to the underlying software in order to use new or differently formatted sources of input data. This required additional (and time consuming) testing and verification after modifications were made, making FluxEngine less accessi- ble to those unfamiliar to Python programming. Two features have been added in version 3. 0 to address this issue: (i) file pattern matching (through standard Unix glob patterns and custom date/time tokens, described fully in Flux-EngineV3_instructions. pdf) allows the input file name format and directory structure to be customised using the plain-text configuration file, and (ii) optional preprocessing functions can be used to manipulate input data after the data have been read into memory.
These features can be specified for each input variable in the configuration file and FluxEngine contains a selection of common preprocessing functions, such as unit conversions or matrix transformation of the input data. Additional custom preprocessing functions can be added and tested easily by the user without the need to modify the core FluxEngine software, through copying and then completing the Python template function provided within the source code (data_preprocessing. py). Storing the completed function into the data_preprocessing. py file will then result in the custom preprocessing function being automatically available for use in any configuration file. These features make it possible to use any observational netCDF dataset by specifying the file path and, if required, appropriate preprocessing functions.
Reanalysis of in situ CO 2 fugacity data to a consistent temperature and depth A new utility, reanalyse_socat_driver. py, is CO 2 specific and exploits a reference (sea surface temperature) SST dataset (e. g. climate quality Earth observation SST data) and the original paired in situ measurements of CO 2 fugacity (f CO 2 ) and temperature to recalculate the f CO 2 for the reference temperature dataset (and thus a consistent depth). The reasoning for the reanalysis is to reduce uncertainty and unknown biases that arise due to the f CO 2 measurements being collected using different instrumentation at some unknown and potentially variable depth below the surface. A detailed justification of the method and a full description of the approach are described in  Goddijn-Murphy et al. (2015). The reanalysis step is especially important if in situ data consisting of a collated dataset (originating from different instruments, sampling strategies, or sources) is being used to calculate temporally averaged gas fluxes (e.
g. monthly mean values). In this situation the in situ measurements are highly likely to be collected from a range of different depths and unlikely to fully capture the monthly mean conditions of temperature and f CO 2 (due to aliasing), whereas temporally averaged (mean) satellite observations are likely to provide a better representation (reference) of the mean temperature conditions. Therefore reanalysing the collated f CO 2 dataset to this reference temperature enables the calculation of the equivalent mean f CO 2 data. It is worth noting that ship draught, and thus underway measurement intake depth, can even vary on a single research cruise due to changes in sea state, ballasting, or cargo. So the method can also be important for data collected during a single research cruise. For the case studies presented here, the satellite-observed SST data used for the reanalysis are valid for a depth of ∼ 1 m  (Reynolds et al.
Therefore caution should be used when applying the reanalysis method to data where these assumptions are not valid. The slow re-equilibrium time of CO 2 in seawater (i. e. of the order of months for CO 2 to equilibrate with the atmosphere) ensures that monthly mean, or rolling monthly mean (centred on the day of interest), skin or sub-skin sea surface temperature (SST) values are suitable for reanalysing the in situ data. Arguably, for reanalysing individual in situ datasets (e. g. to calculate gas fluxes for a single cruise dataset) a robust daily skin or sub-skin SST value would be better, even if that is obtained by a seasonal curve fitted to the monthly values and interpolated to the day of interest. Another solution would be to collect paired measurements of skin SST and f CO 2 and SST at depth, all in situ, as has been done on a recent research cruise  (Tarran, 2018)  as ship-ready instruments are available, e.
Custom gas transfer velocity parameterisation The processes that govern exchange, their relative importance, and how gas exchange should be parameterised are all active areas of research (e. g. Pereira et al. , 2018; Wrobel and Piskozub, 2016). FluxEngine has always allowed users to select or define different gas transfer velocity parameterisations. However, version 3. 0 adopts a modular approach to specifying the flux calculation, which makes it simpler for the user to extend the functionality and incorporate new gas transfer parameterisations. Custom parameterisations can be implemented as separate Python classes without modifying the core Flux-Engine software. This is achieved by copying and modifying the template class provided in rate_parameterisation. py. Storing the new class within rate_parameterisation.
Case study examples of the new capabilities The following sections describe the application and results from four case studies that illustrate the new capabilities. Table  1  summarises the new features that are demonstrated in each case study. These case studies were run using Flux-Engine version 3. 0, which can be accessed via the GitHub repository (see "Code availability"). The configuration files for each case study are included as examples in the configs subdirectory of the GitHub repository, and these will be updated to maintain compatibility as new versions of the toolbox are released. In addition, interactive iPython Jupyter Notebook tutorials for the first three case studies are included in the tutorials subdirectory of the repository. Section 4 of this paper provides more information about these tutorials.
Case study 1: calculating CO 2 fluxes from research cruise data Each year over 1 million new in situ data points are included within the annual updates to the SOCAT dataset. Field scien-tists collecting these data often need to calculate the coincident sea-to-air gas fluxes, either using solely in situ measurements or through combining them with satellite Earth observation and/or model data. Here we illustrate the procedure for calculating sea-to-air gas fluxes from in situ data collected during four different sampling campaigns. These in situ data  (Kitidis and Brown, 2017; Schuster, 2016; Steinhoff et al. , 2016; Wanninkhof and Pierrot, 2016 ; hereafter referred to as cruises 1-4, respectively) were all collected in the North Atlantic during October 2013. The in situ data were first downloaded from PAN-GAEA (an open-access data publishing and archiving repository, http://www.
pangaea. de, last access: 6 December 2019) in tab-delimited format. The datasets follow the standard SO-CAT structure and content (see  Bakker et al. , 2016, Table 9). The majority of the measurements needed for the seato-air CO 2 gas flux calculation were included within the downloaded datasets. The aqueous f CO 2 , salinity, SST, and air pressure were measured in situ and the molar fraction of CO 2 in dry air (xCO 2 ) had been extracted from the GLOBALVIEW-CO2 dataset (GLOBALVIEW-CO2, 2013). However, wind speed (needed for calculating the gas transfer velocity) was missing in all cases. Therefore, to complement these in situ data, multi-sensor merged wind speed data at 10 m were downloaded (Cross-Calibrated Multi-Platform, CCMPv2, 6 h temporal resolution, 0. 25 • × 0. 25 • spatial grid;  Atlas et al.
, 2011). These wind speed data were appended to the in situ data by matching each in situ measurement to the closest temporal and spatial grid point. This same process was used to add columns for the second and third moments of wind speed, which were estimated by taking the second and third power of wind speed, respectively. Two datasets  (Schuster, 2016; Steinhoff et al. , 2016 ) were missing xCO 2 data, so the same method of matching temporal and spatial grid points was used to fill in these fields using the GLOBALVIEW-CO2 dataset from the US National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) (GLOBALVIEW-CO2, 2013). For ease, these additional wind speed and xCO 2 data were downloaded, extracted, and then inserted into the tab-delimited in situ file using some simple custom Python scripts but the same process could be performed manually.
These scripts are not part of Flux-Engine. Collectively the in situ data from these cruises were collected from different ships and underway systems, all sampling water at different and unknown depths. These measurements are typically collected from a few metres below the water surface, whereas the CO 2 concentration (combination of f CO 2 and solubility) on either side of the mass boundary layer is required for an accurate gas flux calculation. Before these data from multiple sources can be used for an accurate gas flux calculation, they need to be reanalysed to a common temperature dataset and depth  (Goddijn-Murphy et al. , 2015; Woolf et al. , 2016). Therefore, the reanalyse_socat_driver. py.
2. 5). tool was first used to reanalyse all f CO 2 data to a consistent temperature and depth. Monthly mean sea surface temperatures from the Reynolds Optimum Interpolation Sea Surface Temperature dataset (OISST;  Reynolds et al. , 2007)  were used as the reference sub-skin temperature dataset, resulting in reanalysed f CO 2 that are valid for ∼ 1 m and used to represent the bottom of the mass boundary layer (termed "sub-skin" within  Woolf et al. , 2016). The reanalysed f CO 2 data were then inserted into the tabdelimited in situ dataset producing a single dataset. The tabdelimited file was then converted into a netCDF file using the text2ncdf. py tool. This tool groups all data according to a user-specified spatial sampling grid, calculating the mean value and standard deviation for each cell within the grid as well as the number of data that were used to calculate these statistics.
(b) The difference in the calculated flux resulting from using the reanalysed f CO 2 compared to the original in situ f CO 2 data (reanalysed minus original). reanalysis using an estimated or modelled skin temperature (based on the in situ SST at depth) may be more appropriate (see the discussion in Sect. 2. 4). The append2insitu. py tool was then used to append Flux-Engine output to the original input data file for the  Kitidis and Brown (2017)  dataset. The output from this tool enables the user to visualise FluxEngine output (including any additional input data such as the Cross-Calibrated Multi-Platform (CCMP) wind speed data) as a time series alongside all other in situ data. Figure  4  shows the time series of SST, f CO 2 , and xCO 2 (from the downloaded cruise data). Plotted alongside these are the corresponding CCMP wind speed data and the calculated concentrations and fluxes using the original and reanalysed f CO 2 data.
Data for the Östergarnsholm monitoring station covering a period from 28 January to 9 September 2015 were downloaded from the data repository  (Rutgersson, 2017). These data contain in situ measurements for f CO 2 , salinity, and temperature; model reanalysis air pressure at sea level from the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR) dataset  (Kalnay et al. , 1996) ; xCO 2 from the NOAA ESRL GLOBALVIEW dataset (GLOBALVIEW-CO2, 2013); and World Ocean Atlas salinity data  (Boyer et al. , 2013). CCMP wind speed data were extracted and added to the tab-delimited in situ dataset using the same method as used in case study 1 (Sect. 3. 1). For gridded input data, values were extracted from the single grid point containing the Östergarnsholm station location and were selected from a global 1 • × 1 • projected grid.
The text2ncdf. py tool was configured to convert the textformatted data file into a single netCDF file using a temporal resolution of 1 d. This produced a netCDF file with a temporal dimension size of 246 d, containing the daily mean value for each of the 246 d covered by the dataset. For this case study, FluxEngine was configured to use this file as input. The flux calculation used the rapid model  (Woolf et al. , 2016)  with the  Nightingale et al. (2000)  wind-based gas transfer velocity parameterisation and was performed using the original in situ f CO 2 and SST data. The temporal resolution was set to provide daily calculations for each of the 246 d, allowing seasonal variations to be observed. FluxEngine supports arbitrary temporal resolutions to within minute precision and the choice predominantly depends on the resolution of the available data and the particular research questions to be addressed.
Case study 3: surfactant suppression of N 2 O gas fluxes using the MEMENTO database Nitrous oxide (N 2 O) and methane (CH 4 ) are both climatically important gases. In the troposphere, they act as greenhouse gases (IPCC, 2013), whereas stratospheric N 2 O is the major source for nitric oxide radicals, which are involved in one of the main ozone reaction cycles  (Ravishankara et al. , 2009). Source estimates indicate that the world's oceans play a major role in the global budget of atmospheric N 2 O and a minor role in the case of CH 4 (IPCC, 2013). Oligotrophic ocean areas are near equilibrium with the atmosphere and, consequently, make only a relatively small contribution to overall oceanic emissions, whereas biologically productive regions (e. g. estuaries, shelves, and coastal upwelling areas) appear to be responsible for the major fraction of the N 2 O and CH 4 emissions  (Bakker et al.
, 2014). Surfactants are surface-active compounds that can suppress turbulence at the sea surface thus altering air-sea gas exchange  (McKenna and Bock, 2006; Pereira et al. , 2016; Salter et al. , 2011). There is growing evidence from field and laboratory studies that naturally occurring surfactants can significantly reduce the flux of N 2 O across the water-atmosphere interface  (Kock et al. , 2012; Mesarchaki et al. , 2015). Previous work, which studied CO 2 fluxes, found that surfactants potentially reduce the annual net integrated CO 2 flux by up to 9 % in the Atlantic Ocean  (Pereira et al. , 2018). Here, we use FluxEngine to apply the methodology of  Pereira et al. (2018)  to in situ data from the MEMENTO (MarinE MethanE and NiTrous Oxide) database  (Kock and Bange, 2015)  in order to estimate the equivalent suppression effect on the exchange of N 2 O between ocean and atmosphere.