dataesr/scientific-paragraphs-categorization huggingface dataset:
Although pre-processing of underwater vehicle navigation  (Rigby et al. , 2006; Batista et al. , 2012; Kwasnitschka et al. , 2013)  and attitude  (Hugues Clarke, 2003)  allows to optimize the quality of fine-scale terrain reconstruction, the latter is intrinsically dependent on the data quality primarily acquired by motion sensors (i. e. , spatial accuracy, lower recording time step than acoustic soundings and temporal synchronization). Accuracy and precision of underwater vehicle positioning in the deep sea remains nevertheless a major technological challenge that may particularly affect high-resolution terrain reconstruction efforts. Noisy USBL navigation data of underwater vehicles can create abrupt artifacts in the MBES bathymetry that may lead to inaccurate fine-scale terrain models. In this study, we merged the overall accuracy of the USBL navigation with the precision of the DVL records, to achieve the optimal navigation dataset to avoid terrain artifacts in a vertical reconstruction.
To our understanding, this was a necessary step in the workflow of acoustic data processing since the decisions to remove soundings at the manual cleaning stage remain difficult to make, particularly in complex terrain. Meter-scale features such as outcrops could easily be erased from the point cloud which could lead to a reduction of the terrain complexity. Similarly, navigational uncertainty can remain between separate ROV dives (e. g. , between the MBES and photography dives), and can cause difficulties in linking groundtruthing data to acoustic datasets.
, 2016). Density-based clustering (e. g. , DBSCAN;  Ester et al. , 1996)  is an alternative approach as it is capable of identifying patterns with arbitrary sizes in datasets even containing noise and outliers  (Khan et al. , 2014). Secondly, some clusters did locally exhibit patchy and discontinuous distributions also linked with lower confidence levels nested in The top three species structuring the dissimilarity between pairwise tests are presented along with their average contribution to pairwise dissimilarity, the ratio between dissimilarity and standard deviation values and cumulative contribution toward total pairwise dissimilarity. See  Broad et al. (in prep)  for further information on morphospecies characteristics. p-value: * < 0. 05, ** < 0. 01, *** < 0. 001. the upper part of the underwater cliff.
Biological Interpretation As a last step in this study, we tested the ecological relevance of the unsupervised habitat categories summarizing a multivariable environment (i. e. , depth, terrain and substrate) with the biological information provided by groundtruthing imagery. Abiotic clusters are regularly used as a proxy to reflect the habitat of certain species or groups of species  (Brown et al. , 2011) , but this assumption requires validation in poorly understood ecosystems such as the deep Greenland waters studied here. Although some terrain descriptors may be greatly affected by biogenic structures (e. g. , cold-water coral reefs will affect backscatter at < 1 m resolution,  Masson et al. , 2003 ; terrain heterogeneity at 0. 1 m resolution,  Huvenne et al. , 2011) , such biogenic structures themselves create habitat for other species, and hence can be considered part of the initial habitat characterization, particularly if it has to be based on remote sensing data, with little or no groundtruthing data available for quantitative validation.
However, acquisition of robust groundtruthing data remains necessary to fully characterize the faunal communities, especially in the undersampled deep-sea benthic habitat of Greenland waters. In practice, uncertainties in ROV positioning and attitude recording are still some of the major challenges when working in this type of environment and with high-resolution terrain characterization. While we proposed post-processing methods that help to limit error propagation, positional errors can still affect the habitat mapping outcomes and possibly constrain the spatial accuracy when linking abiotic and biotic datasets. Further investigations and development in vehicle navigation are needed to improve high-resolution habitat mapping in complex deepsea environments.
ACKNOWLEDGMENTS We acknowledge the Captain and the crew of the RRS Discovery as well as the  ROV Isis technical team of the National Oceanography Centre (NOC). We gratefully thank  Tabitha Pearman ,  Tim Le Bas ,  Catherine Wardell ,  Guillem Corbera ,  Michael Faggetter ,  David Price , and  Pål Buhl-Mortensen  for their help and advice and  Erik Simon-Lledó  for his assistance on the BIIGLE platform. We would like to thank  Katleen Robert  who provided R scripts for preprocessing and  Evan Edinger  for reviewing the initial version of our work. We appreciated the constructive feedback of two reviewers who improved the earlier versions of the manuscript.
FUNDING This work was initially part of a thesis supported by the  International Master of Science in Marine Biological Resources  (IMBRSea; LV). IMBRSea is a Joint Master's Degree under Erasmus Mundus coordinated by  Ghent University  ( FPA 574482-EPP-1-2016-1-BE-EPPKA1-JMD-MOB ). Funding for DY081 was from the  European Research Council  ( ERC Starting Grant   678371 ICY-LAB ). VH was supported by the  NERC National Capability Program CLASS  (Grant No. NE/R015953/1 ). EU  H2020  project iAtlantic (Grant No. 818123 ) also supported VH and LV. Paper publication costs were supported by  NERC National Capability  funding.
Methane (CH 4   ) emissions from natural landscapes constitute roughly half of global CH 4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH 4 flux are ideal for constraining ecosystem-scale CH 4 emissions due to quasi-continuous and high-temporal-resolution CH 4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH 4 Version 1. 0, the first open-source global dataset of CH 4 EC measurements (available at https://fluxnet. org/data/fluxnet-ch4-community-product/, last access: 7 April 2021).
FLUXNET-CH 4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH 4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH 4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH 4 Version 1. 0 are freshwater wetlands which are a substantial source of total atmospheric CH 4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH 4 fluxes. Our representativeness analysis.
The few continuous CH 4 flux datasets across representative site years make it difficult to establish trends in seasonal dynamics, though monthly or annually aggregated estimates of CH 4 fluxes from different seasons do exist for high latitudes  (Zona et al. , 2016; Treat et al. , 2018). Seasonal variability in freshwater wetland CH 4 fluxes is expected to be driven by changes in air (TA) and soil temperature (ST), soil moisture (including water table dynamics), and recent carbon substrate availability, which influence the rates of CH 4 production and consumption  (Lai, 2009; Bridgham et al. , 2013; Dean et al. , 2018). Temperature has widely been found to strongly affect CH 4 flux  (Chu et al. , 2014; Yvon-Durocher et al. , 2014; Sturtevant et al. , 2016) , but the relationship is complex  (Chang et al.
, 2017)  with secondary within-wetland influences from temperature and availability of carbon (C) substrates  (Melton et al. , 2013; Castro-Morales et al. , 2018). Bottom-up and top-down global CH 4 estimates continue to disagree on total CH 4 flux magnitudes and seasonality, including the timing of annual peak emissions  (Spahni et al. , 2011; Saunois et al. , 2020). Thus, the variability in and predictors of wetland CH 4 seasonality globally remain a knowledge gap that highfrequency and long-term EC data can help fill. Here, we first describe version 1. 0 of the FLUXNET-CH 4 dataset (available at https://fluxnet. org/data/ fluxnet-ch4-community-product/, last access: 7 April 2021). Version 1. 0 of the dataset expands and formalizes the publication of data scattered among regional flux networks as described previously in  Knox et al.
(2019). FLUXNET-CH 4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH 4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 upland ecosystems. FLUXNET-CH 4 includes an additional two wetland sites (RU-Vrk and SE-St1), but they are not available under the CC BY 4. 0 data policy and thus are excluded from this analysis. Since the majority of sites in FLUXNET-CH 4 Version 1. 0 (hereafter referred to solely as "FLUXNET-CH 4 ") are freshwater wetlands, which are a substantial source of total atmospheric CH 4 emissions, we use the subset of data from freshwater wetlands to evaluate the representativeness of freshwater wetland coverage in the FLUXNET-CH 4 dataset relative to wetlands globally, and we provide the first assessment of global variability in and predictors of freshwater wetland CH 4 flux seasonality.
History and data description The FLUXNET-CH 4 dataset was initiated by the Global Carbon Project (GCP) in 2017 to better constrain the global CH 4 budget (https://www. globalcarbonproject. org/ methanebudget/index. htm, last access: 6 July 2021). Beginning with a kick-off meeting in May 2018 in Washington DC, hosted by Stanford University, we coordinated with the AmeriFlux Management Project, the European Ecosystem Fluxes Database, and the Integrated Carbon Observation System Ecosystem Thematic Centre (ICOS-ETC) to avoid duplication of efforts as most sites are part of different regional networks (albeit with different data products). We collected and standardized data for FLUXNET-CH 4 with assistance from the regional flux networks, AmeriFlux's "Year of Methane", FLUXNET, the EU's Readiness of ICOS for Necessities of Integrated Global Observations (RINGO) project, and a US Geological Survey Powell Center working group.
FLUXNET-CH 4 is a community-led project, so while we developed it with assistance from FLUXNET, we do not necessarily use standard FLUXNET data variables, formats, or methods. FLUXNET-CH 4 includes gap-filled half-hourly CH 4 fluxes and meteorological variables. Gaps in meteorological variables (TA -air temperature; SW_IN -incoming shortwave radiation; LW_IN -incoming longwave radiation; VPD -vapor pressure deficient; PA -pressure; P -precipitation; WS -wind speed) were filled with the ERA-Interim (ERA-I) reanalysis product  (Vuichard and Papale, 2015). We used the REddyProc package  (Wutzler et al. , 2018)  to filter flux values with low friction velocity (u * ) based on relating nighttime u * to fill gaps in CO 2 , latent heat, and sensible heat fluxes and to partition net CO 2 fluxes into gross primary production (GPP) and ecosystem respiration (RECO) using both the daytime  (Lasslop et al.
, 2010)  and nighttime  (Reichstein et al. , 2005)  approaches. Data gaps of CH 4 flux were filled using artificial neural network (ANN) methods first described in  Knox et al. (2015 Knox et al. ( , 2019) )  and summarized here in Sect. 2. 1. 2. Gap-filled data for gaps exceeding 2 months are provided and flagged for quality. Please see Table  B1  for variable description and units, as well as quality flag information. For the seasonality analysis in this paper we excluded data from gaps exceeding 2 months, and we encourage future users of FLUXNET-CH 4 to critically evaluate gap-filled https://doi. org/10. 5194/essd-13-3607-2021 Earth Syst. Sci. Data, 13, 3607-3689, 2021  values from long data gaps before including them in analyses  (Dengel et al. , 2013; Kim et al. , 2020). In addition to half-hourly data, the FLUXNET-CH 4 Version 1.
Gap-filling methods and uncertainty estimates As described in  Knox et al. (2015 Knox et al. ( , 2019)) , the ANN routine used to gap-fill the CH 4 data was optimized for generalizability and representativeness. To avoid biasing the ANN toward environmental conditions with typically better data coverage (e. g. , summertime and daytime measurements), the explanatory data were divided into a maximum of 15 clusters using a k-means clustering algorithm. Data used to train, test, and validate the ANN were proportionally sampled from these clusters. For generalizability, the simplest ANN architecture with good performance (< 5 % gain in model accuracy for additional increases in architecture complexity) was selected for 20 extractions of the training, test, and validation data. Within each extraction, each tested ANN architecture was reinitialized 10 times, and the initialization with the lowest root-mean-square error was selected to avoid local minima.
The median of the 20 predictions was used to fill each gap. A standard set of variables available across all sites was used to gap-fill CH 4 fluxes  (Dengel et al. , 2013) , which included the previously mentioned meteorological variables TA, SW_IN, WS, and PA and sine and cosine functions to represent seasonality. These meteorological variables were selected for their relevance to CH 4 exchange and were gap-filled using the ERA-I reanalysis data. Other variables related to CH 4 flux (e. g. , water table depth, WTD, and soil temperature, TS) were not included as explanatory variables as they were not available across all sites or had large gaps that could not be filled using the ERA-I reanalysis data  (Knox et al. , 2019). The ANN gap-filling was performed using MATLAB  (Mathworks 2018a, version 9.
Dataset structure and site metadata FLUXNET-CH 4 contains two comma-separated data files per site at half-hourly and daily resolutions which are available for download at https://fluxnet. org/data/ fluxnet-ch4-community-product/ (last access: 7 April 2021), along with a file containing select site metadata. Each site has a unique FLUXNET-CH 4 DOI. All data from the 79 sites used in this analysis are available under CC BY 4. 0 (https://creativecommons. org/licenses/by/4. 0/, last access: 6 July 2021) copyright license (FLUXNET-CH 4 has an additional two sites available under the FLUXNET Tier 2 license (https://fluxnet. org/data/data-policy/, last access: 6 July 2021), though these sites are not included in our analysis). Metadata (Table  B3 ) include site coordinates, ecosystem classification based on site literature, presence/absence and dominance for specific vegetation types, and DOI link, as well as calculated data such as annual and quarterly CH 4 flux values.
Subset analysis on freshwater wetland CH 4 flux In addition to the FLUXNET-CH 4 -wide description of site class distributions and annual CH 4 fluxes, we also include a subset analysis on freshwater wetlands given that it is the dominant ecosystem type in our dataset and an important global CH 4 source  (Saunois et al. , 2016). First, we analyze freshwater wetland representativeness and sub-sequently the seasonality of their CH 4 emissions. Freshwater wetlands included in the seasonality and representativeness analysis are indicated in Table  B3 , column "IN_SEASONALITY_ANALYSIS".
Principal component analysis To compare the FLUXNET-CH 4 site distribution to the global wetland distribution, we evaluated their representativeness in the entire global wetland cover along four bioclimatic gradients. Only freshwater wetland sites were included in this analysis. Coastal sites were excluded because salinity, an important control on CH 4 production, could not be evaluated across the tower network due to a lack of global gridded salinity data  (Bartlett et al. , 1987; Poffenbarger et al. , 2011). The four bioclimatic variables used were mean annual air temperature (MAT), latent heat flux (LE), enhanced vegetation index (EVI), and simple ratio water index (SRWI; data sources in Table  B4 ). We use EVI because it is a more direct measurement than GPP from global gridded products and is considered a reasonable proxy for GPP  (Sims et al.
Wetland CH 4 seasonality To examine freshwater wetland CH 4 seasonality across the global range of sites in FLUXNET-CH 4 , we extracted seasonality parameters for CH 4 , temperature, and GPP using TIMESAT, a software package designed to analyze seasonality of environmental systems  (Jönsson and Eklundh, 2002, 2004; Eklundh and Jönsson, 2015). TIMESAT calculates several seasonality parameters, including baseline flux, peak flux, and the slope of spring flux increase and fall decrease (Fig. 1 ). We also calculate parameters such as amplitude (peak flux minus baseline, which is the average of spring and fall baselines; "e" -(("a" + "b")/2) in Fig. 1 ) and relative peak timing (("g" -"f")/("h" -"f") in Fig. 1 ). TIME-SAT uses a double-logistic fitting function to create a series of localized fits centered on data minima and maxima.
, 2009). To extract seasonality parameters with TIMESAT, sites need a sufficiently pronounced seasonality, a sufficiently long time period, and minimal data gaps (we note that while TIMESAT is capable of fitting two peaks per year, all the freshwater wetland sites have a single annual peak). We excluded site years in restored wetlands when wetlands were still under construction. Of the 42 freshwater wetland sites in FLUXNET-CH 4 Version 1. 0, 36 had sufficient data series to extract seasonality parameters. These 36 wetlands had 141 site years of data in total, which we fit with the double-logistic fitting method which followed site data well (representative examples in Fig. 2 ). For extratropical sites in the Southern Hemisphere, we shifted all data by 182 d so that maximum solar insolation seasonality would be congruent across the globe.
Kruskal-Wallis and post hoc tests were implemented in Python Version 3. 7. 4, using stats from scipy for Kruskal-Wallis and posthoc_dscf from scikit_posthocs. We also compared quarterly CH 4 flux sums by dividing data into quarterly periods: January-February-March (JFM), April-May-June (AMJ), July-August-September (JAS), and October-November-December (OND). For the sake of simplicity, we chose to compare quarterly periods rather than site-specific growing/non-growing season periods so that all time periods would be the same length. Quarterly sums were computed from the gap-filled CH 4 fluxes when the longest continuous data gap within the quarter did not exceed 30 d, leading to site-year counts of 67, 92, 95, and 72 for JFM, AMJ, JAS, and OND, respectively. We compared quarterly CH 4 fluxes across latitudinal bands both for the total CH 4 flux and for the quarterly percentage of the annual CH 4 flux.
Results and discussion 3. 1 FLUXNET-CH 4 dataset 3. 1. 1 Dataset description Version 1. 0 of the FLUXNET-CH 4 dataset contains 79 unique sites, 293 total site years of data, and 201 site years with sufficient data to estimate annual CH 4 emissions. A synthesis paper, published prior to the public data release of FLUXNET-CH 4 Version 1. 0, had 60 unique sites and 139 site years with annual CH 4 emission estimates  (Knox et al. , 2019). Freshwater wetlands make up the majority of sites (n = 42), and the dataset also includes five salt marshes and one mangrove wetland. Notable additions to FLUXNET-CH 4 from the previous unpublished dataset used in  Knox et al. (2019)  include six tropical sites (between 20 • S and 20 • N), including one site in South America, two sites in southern Africa, and three sites in Southeast Asia.
The 15 upland sites include six needleleaf forests, three crop sites (excluding rice), two alpine meadows, one grassland, one mixed forest, one tundra, and one urban site. The drained sites represent former wetlands that have been artificially drained for use as grasslands (n = 3) or croplands (n = 3). FLUXNET-CH 4 sites span the globe, though they are concentrated in North America and Europe (Fig. 3 ). Table  B3  includes characteristics of all sites in the dataset. Sites represent a range of ecosystem types, latitudes, median fluxes, and seasonality patterns (Table  1 ). Across all FLUXNET-CH 4 sites (including non-wetland sites), mean average annual CH 4 flux is positively skewed with a median flux of 9. 5 g C m -2 yr -1 , a mean flux of 16. 9 g C m -2 yr -1 , and numerous annual CH 4 fluxes exceeding 60 g C m -2 yr -1.
Freshwater wetland CH 4 characteristics The FLUXNET-CH 4 Version 1. 0 dataset contains 42 freshwater wetlands that span 37 • S to 69 • N, including bogs, fens, wet tundra, marshes, and swamps, and a range of annual CH 4 emission rates (Fig. 4 ). The majority of freshwater wetlands in our dataset emit 0-20 g C m -2 yr -1 , with 10 emitting 20-60 g C m -2 yr -1 , and one more than 60 g C m -2 yr -1. Differences in annual CH 4 flux among wetland types is partially driven by temperature (which is often linked to site type), with mean annual air temperature explaining 51 % of the variance between sites (Fig. 5 , exponential relationship). The global relationship between annual methane emissions and temperature can be described using a Q 10 relationship where Q 10 = R2/R1 ((T 2-T 1)/10) , with R2 and R1 being the CH 4 emission rates at temperatures T 2 and T 1, respectively (temperature in • C).
g. , US-Twt;  Knox et al. , 2016). The dataset has 1 year of urban data from site UK-LBT in London, England. UK-LBT observes CH 4 fluxes from a 190 m tall communications tower in the center of London and has a mean annual CH 4 flux of 46. 5 ± 5. 6 g C m -2 yr -1. This flux is more than twice as high as the mean annual CH 4 flux across all FLUXNET-CH 4 sites, 16. 9 g C m -2 yr -1. The London site has higher CH 4 emissions in the winter comhttps://doi. org/10. 5194/essd-13-3607-2021 Earth Syst. Sci. Data, 13, 3607-3689, 2021 pared to summer, which is attributed to a seasonal increase in natural gas usage  (Helfter et al. , 2016. )  3   (Bartlett et al. , 1987; Poffenbarger et al. , 2011). Saltwater wetlands along the coast have unique CH 4 dynamics attributable to the presence of abundant electron acceptors, most importantly sulfates which inhibit methanogenesis  (Pattnaik et al.
Freshwater wetland representativeness We evaluated the representativeness of freshwater wetland sites in the FLUXNET-CH 4 Version 1. 0 dataset against wetlands globally, based on bioclimatic conditions of our sites. When evaluating bioclimatic variables individually, the distribution of freshwater wetlands across the network was significantly different from the global distribution (alpha > 0. 05; two-tailed Kolmogorov-Smirnov tests; see Table  B4 ). We exclude wetlands classified as "salt marsh" in this representativeness analysis and the seasonality analysis below because of the unique CH 4 flux dynamics in saltwater ecosystems (as discussed in Sect. 3. 1. 4), though we note that some of the coastal wetlands included in the freshwater analysis periodically experience brackish water (i. e.
7d ). Our assessment of wetland CH 4 tower coverage determines the ability of our dataset to represent global wetland distributions and highlights some clear representation gaps in the network, particularly in tropical and humid regions. Other geographic regions such India, China, and Australia, where towers exist but are not included in the current network, should be prioritized when expanding the network even though they are not among the most distant areas to the current network. Similar representativeness assessments have been developed for CO 2 tower networks to identify gaps and priorities for expansion  (Jung et al. , 2009). To improve the geographic coverage of the network for representing global-scale fluxes, locations for new tower sites can be targeted to cover bio-climatically distant areas from the current network  (Villarreal et al.