dataesr/scientific-paragraphs-categorization huggingface dataset:
Ocean Biogeographic Information System (OBIS) OBIS is the world's largest database on the distribution and abundance of marine life. In 2009, IOC Member States recognised the importance of knowledge of the ocean's biodiversity to national and global environmental policies when they adopted it from the Census of Marine Life  (Costello and Vanden Berghe 2006; Costello et al. 2007; O'Dor et al. 2012). OBIS operates through a network of national, regional and thematic nodes, and a secretariat based at the IOC's International Oceanographic Data and Information Exchange (IODE) programme office in Oostende, Belgium. This office provides training and technical assistance, guides new data standards and technical developments, and encourages international cooperation to foster the group benefits of the network.
OBIS is a global science alliance that facilitates free and open access to data and information on marine biodiversity. It provides a single access point to over 45 million observations of 114,000 marine species, collected on 4. 6 million sampling events from 3. 2 million sampling stations, integrated from over 1900 datasets provided by nearly 500 institutions in 56 countries, It grows by about 3 million records per year. Data are subject to a series of quality control steps, including for taxonomic nomenclature and geography  (Vandepitte et al. 2011 (Vandepitte et al. , 2015a, b;, b; IODE Steering Group for OBIS 2013). Communities associated with OBIS include OBIS-SEAMAP (Spatial Ecological Analysis of Megavertebrate Populations) focusing on megafauna, and MICROBIS (http://icomm. mbl. edu/microbis) on microbes.
The latter collects molecular observations of marine microbial organisms at taxonomic ranks from phyla to genus, together with their contextual physical and biochemical data measured in situ or from remote sensing. It has developed tools for extracting diversity measures, as well as other ecologically relevant statistics, from molecular datasets  (Giongo et al. 2010; Buttigieg and Ramette, 2014). More comprehensive taxon based databases include the pioneering FishBase  (Froese and Pauly 2015). So far, 1000 publications have cited OBIS and on average 10 more each month (e. g. ,  Basher et al. 2014a, b; Saeedi and Costello 2012; Costello et al. 2015a). OBIS directly contributes to several international activities, such as the UN Convention on Biological Diversity (for the identification of Ecologically or Biologically Significant Areas), the UN Food and Agriculture Organization (for the identification of Vulnerable Marine Ecosystems), the UN World Ocean Assessment, and the Global Environment Fund Transboundary Water Assessment.
The Global Biodiversity Information Facility (GBIF) and OBIS use the same data standards and data sharing protocol (i. e. , GBIF's Integrated Publishing Toolkit). GBIF contains all OBIS and additional marine data (e. g. ,  Costello et al. 2013d). Most data in OBIS are available from the north-west and north-east Atlantic, South Africa and New Zealand, and some other locations (Fig. 6. 2). The potential of data published through OBIS for time-series analysis was highlighted in a recent global scale analysis  (Dornelas et al. 2014 ). An important development that will aid time series analysis, ecological niche modelling and climate change studies is currently underway as part of a two-year IODE project called 'Expanding OBIS with environmental data' (OBIS-ENV-DATA), which started in March 2015.
The project is working on a solution to retain data in biological datasets that hold more than just species occurrence data, such as providing environmental and ecological context and data. The new approach will be based on the new Darwin Event Core and a modified 'MeasurementorFact' extension. The major change is that it will bring OBIS from a purely species occurrence database to one that can handle hierarchical sampling event structure with additional environmental and biometric measurements as well as details on the nature of the observations, measurements, and data collection methods, including equipment, data processing and sampling efforts.
Time-Series Data Availability At present there are 20 monitoring programmes that have targeted species for more than five years that have been entered in OBIS. The focus of these efforts is on economically valuable and charismatic species (e. g. , Antarctic krill, American lobster, marine mammals and seabirds). By contrast there are many more monitoring programmes targeting marine communities that have data for at least five years; 216 community monitoring programmes have uploaded their data to OBIS. When these programmes are combined, 16,616 stations have been monitored, encompassing most coastlines of the world, with less data available in developing countries or remote regions (Fig. 6. 3a). The accumulation of time-series data has been exponential (Fig. 6. 3b), reflecting both increasing monitoring efforts and global coordination.
There may be an increasing willingness of scientists and institutions to share their data, with programmes such as the European Groundfish Survey showing up as being an important source of biodiversity data in the mid-1990s on a global scale (Fig. 6. 3b). Even so, there are relatively fewer new stations that are being added to OBIS in comparison to the number of stations where monitoring surveys have ceased (Fig. 6. 3b), leading to a net loss of time-series from OBIS in this decade. Explanations for this trend may be delays in data deposition, and/or perhaps the scope of specific monitoring efforts is increasing in extent and coordination.
Global Marine Environment Datasets (GMED) GMED is a compilation of more than 60 publicly available climatic, biological and geophysical environmental layers featuring present, past and future environmental conditions  (Basher et al. 2015). Marine biologists increasingly utilise geo-spatial techniques with modelling algorithms to visualise and predict species biodiversity at a global scale. Marine environmental datasets available for species distribution modelling (SDM) have different spatial resolutions and are frequently provided in assorted file formats. This makes data assembly one of the most time-consuming parts of any study using multiple environmental layers for biogeography visualisation or SDM applications. GMED covers the widest available range of environmental layers from in situ measured, remote-sensed, and modelled datasets for a broad range of quantitative environmental variables from the surface to the deepest part of the ocean.
Data Analysis Although marine biodiversity data analysis requires its own taxonomic, geographic and environmental information context, such as provided by WoRMS, Marine-Regions, OBIS, and GMED, the methods of data analysis are similar to biodiversity in other environments. The data are categorical (i. e. , species, habitats, biotopes), numerical (e. g. , species abundance, cover, biomass), and cartographic. Thus metrics of 'biodiversity' include species richness and abundance, Overall there has been an increase in monitoring. However, since the start of this century there has been a relative decrease in the number of stations being added to OBIS, evidenced by (right axis) the proportional difference in the number of new stations being added to OBIS versus those reaching completion. The blue line indicates where more monitoring stations were gained than lost from OBIS in a given year, while the red line indicates a loss phylogenetic structure (e.
g. , taxonomic distinctness; Warwick and Clarke 1998), indicator species, habitat and/or biotope richness in an area  (Costello 2001). Data may be presented on maps, graphs, tables and as matrices (e. g. , Figs. 6. 1 and 6. 2). Numerous software tools are available for this analysis, including PRIMER-E (www. primer-e. com), PAST (http://folk. uio. no/ohammer/past), MODESTR (www. ipez. es/ModestR), SAGA (System for Automated Geoscientific Analyses; www. saga-gis. org) and  DIVA-GIS (www. diva-gis. org). The open-source software R has the benefit that the analytical process is documented and can be published to aid reproducibility of the analyses. The massive size of modern datasets, such as in OBIS and GMED, can lead to a new set of difficulties in analysis and interpretation. These difficulties include processing times that can exceed the capabilities of extant computers, propagation of undetected errors, unfamiliarity with analytical assumptions (e.
g. , spatial autocorrelation), and difficulties in visualisation  (Edgar et al. 2016). Fortunately, big-data techniques applied in other fields, such as high-performance and parallel computing, are helping to solve many of these problems. In addition packages to overcome significant challenges in compiling large datasets and maintaining these data through time are being improved. For example, the R package 'taxize'  (Chamberlain and Szocs 2013) , which relies on accessing freely available and accurate information on species taxonomy, including from WoRMS. This emphasises the benefits of scientists and institutes publishing monitoring data in order to advance our understanding of biodiversity change.
Discussion Global marine biological databases are well-established for quality assurance of species nomenclature and associated information (WoRMS) and distribution data (OBIS)  (Costello and Wieczorek 2014; Costello et al. 2015b ). The coverage and quality of global marine environmental layers improves each year through a combination of remotely sensed, in situ, and modelling data. These layers and maps of marine regions are also freely available online at GMED and marineregions. org. Species trait information is being added to WoRMS, and more sample information can be added to OBIS so users can select datasets suitable for their purposes. The mapping of available data in OBIS shows how more sampling has been conducted in northern hemisphere and coastal environments compared to open-ocean, deep-sea and developing countries (Fig.
In addition to the CPR, RLS and GEOHAB programmes reviewed here, new networking initiatives, marine biodiversity observation networks (mBON) in the USA  (Muller-Karger et al. 2014) , marine station networks and related organisations  (Costello et al. 2015c) , and groups of scientists interested in the biological and ecological effects of climate change, may establish globally coordinated marine biodiversity monitoring programmes. In addition, several international ocean observing systems, initially focused on the collection of physical and chemical ocean data, are now including biological data as well. These are comprised of the Australian led Integrated Marine Observing Systems (IMOS; www. imos. org. au) and the Southern Ocean Observing System (SOOS; www. soos. au), and NOAA's Integrated Ocean Observing System (IOOS; www.
iobis. org and information on life 114,000 marine Host: marine biodiversity species Intergovernmental Oceanographic Commission (IOC) of UNESCO Global Marine To provide Environmental 60 datasets of Environment standardised global datasets featuring climatic, biological Datasets (GMED) marine environment present, past and and geophysical http://gmed. datasets of climatic, future environmental environmental layers auckland. ac. nz biological and conditions to a ready to use for Host: University of geophysical common spatial species distribution Auckland environmental layers resolution modelling and data visualisation software.
Stairway plot inference on the Yoruba SFS We applied the model-flexible stairway plot method developed by  Liu and Fu (2015)  to the unfolded Yoruba SFS. The unfolded SFS is constructed with the subset of sites S9 for which the ancestral base is available. Once folded, this SFS is highly similar to the SFS constructed with the full set of sites S (their square distance is d 2 ¼ 3:9 3 10 25 ; see below in the methods for the computation of d 2 ). Inferences are made on 200 SFS as suggested by their method. We use the script they provide to create 199 bootstrap samples of the Yoruba SFS. We also ignore the singletons for this method, and use the default parameter values suggested in their paper for the optimization.
2 Inference of the Yoruba demography with ∂a∂i We inferred the demography of the Yoruba population with the software @a@i v1. 7  (Gutenkunst et al. 2009) , testing the three models of explicit demography (Linear, Exponential, and Sudden). The demographic models were specified so that the only parameter to optimize is t like for the distance-based inference method. Singletons were masked and the method was applied on the folded Yoruba SFS. Details on the demographic functions and parameter values used for the optimization in @a@i are provided in File S1. We ran the method 100 times for each model and kept the parameter value with the best maximum log composite likelihood over the 100 runs. In Figure  S4 , we plot the best log composite likelihood of the 100 runs.
Scaling of the coalescent time Optimized values of the parameter t for each model are expressed in coalescent time units, i. e. , scaled in 2N e ð0Þ generations (please note that in a model population where all individuals reproduce, N e ¼ N). As the model population size at time zero, 2N e ð0Þ; is unknown, to scale these coalescent time units in numbers of generations and consequently in years, we used the expected number of mutations per site M. From the dataset, we have M obs ¼ S=L, where S is the number of single nucleotide mutations (a k-allelic SNP accounts for k 2 1 mutations), and L is the length of the accessible sequenced genome in the 1000 genomes project (90% of the total genome length, The 1000 Genomes Project Consortium 2015). For the theoretical value, we get that M theo ¼ m Ttot C; where we know the mutation rate m from the literature, and the total tree length expressed in coalescent time units Ttot from the SFS simulations.
Data availability The 1000 Genomes Project data used in this study is publicly available at ftp://ftp. 1000 genomes. ebi. ac. uk/vol1/ftp/ release/20130502/. The code in Python and C written for the study is available at https://github. com/lapierreM/ Yoruba_demography. The code in C used for the Method 1 of SFS simulation is available upon request to G. A.
On the other hand, the demography inferred by the stairway plot predicts a SFS that does not fit well the observed Yoruba SFS: the distance between the observed Yoruba SFS and the expected SFS under the stairway plot demography is 10 times the distance between any of the one-parameter model SFS and the data (Figure  3B  and Table  1 ). The best fitting SFS under each of the five demographic models all have a square distance d 2 of the order of 10 24 with the observed Yoruba SFS (Figure  3A  and Table  1 ), and have highly similar shapes (Figure  3B ). This suggests that the five demographic models used to infer the demography of the Yoruba are hard to distinguish based only on the observed SFS. To validate the use of a least square distance to find the best fitting SFS, we also inferred the Yoruba demography using the @a@i software.
As the relatively low to moderate coverage of the 1000 Genomes Project could also result in an underestimation of doubletons and tripletons, we optimized t masking also these values. It did not change the estimation of t and thus had no effect on the inferred demographies. As the first bin of the SFS accounts for the mutations that occur in the terminal branches of the coalescent tree, a large part of the excess of singletons can be due to very recent and massive growth. Recent studies with deep sequencing coverage have shown that there is a large abundance of rare variants in human populations  (Coventry et al. 2010; Nelson et al. 2012; Gazave et al. 2014). As the dataset we used for this study had a limited sample size and low-coverage, we focused on the inference of demography in the more distant past.
Acknowledgments We thank  Cécile Delaporte  for preliminary work on this project, and  Simon Boitard ,  Michael Blum ,  Konrad Lohse , and three anonymous reviewers for useful comments on the manuscript. G. A. and M. L. acknowledge support from the grant  ANR-12-NSV7-0012  Demochips from the  Agence Nationale de la Recherche (France). M. L. is funded by the  Ph. D. program 'Interfaces pour le Vivant' of Pierre and Marie Curie University (UPMC) University Paris 06. G. A. , A. L. , and M. L. thank the  Center for Interdisciplinary Research in Biology  for funding.
Distribution of branched glycerol dialkyl glycerol tetraethers (brGDGTs) in soils depends on environmental parameters such as mean annual air temperature (MAAT) and soil pH. MBT 0 /MBT 0 5ME (methylation index of branched tetraethers) and CBT/CBT 0 (cyclization ratio of branched tetraethers) are ratios based on the relative abundances of brGDGTs. Using these ratios, global and regional/local calibrations have been established using surface soils, but generally without any preliminary study of soil types. In this study, we reconsider global MAAT-pH/brGDGT calibrations by assigning soil types to the 358 components of a composite soil dataset. Additionally, we investigate brGDGT-derived proxies in five welldescribed soil profiles along an altitudinal transect in the Mount Fan Si Pan National Park, northwestern Vietnam.
, 2014; Deng et al. , 2016; Nieto-Moreno et al. , 2016). Contrary to the classical MBT 0 index, MBT 0 5ME is well correlated with MAAT/altitude at Mt. Shennongjia, China  (Yang et al. , 2015a (Yang et al. , , 2015b)). It is noteworthy that only a few previous brGDGT-based studies have used well-described soils and/or profiles  (Huguet et al. , 2010; Zech et al. , 2012; Menges et al. , 2014; Yamamoto et al. , 2016). A better consideration of soil pedogenesis may help researchers to properly evaluate the dependency of the observed scatters on soil physico-chemical properties. In the present study, we reassess a composite global soil dataset  (Huguet et al. , 2010; Peterse et al. , 2012; Dirghangi et al. , 2013; Anderson et al. , 2014; De Jonge et al. , 2014)  by assigning a soil type to each of its 358 components to investigate traditional and newest MAAT-pH/brGDGT relationships as well as MAAT (and pH) residuals, per soil type.
Soil type assignment for the global soil dataset and statistical analysis A composite global soil dataset of 358 previously analyzed samples (Supplementary Table  S1 ;  Huguet et al. , 2010; Peterse et al. , 2012; Dirghangi et al. , 2013; Anderson et al. , 2014; De Jonge et al. , 2014)  was used for soil type assignment. For most samples taken from the International Soil Reference and Information Centre (ISRIC), soil types were obtained using the ISRIC Soil Information System (ISIS) database (http://isis. isric. nl, last accessed on  Thursday, September 15, 2016). For the other samples, with the exception of those analyzed by  Huguet et al. (2010)  which were the subject of preliminary pedological studies, the most likely soil type was selected using the SoilInfo App based on SoilGrids1km  (Hengl et al.
, 2011). A linear ordinary least square regression of MAAT vs altitude was established using MAAT data as y values from 17 weather stations in Vietnam and China (Supplementary Table  S2 ) in addition to the Hoang Lien Son Mountain MAAT value as reported in  Podwojewski et al. (2011). The obtained slope and intercept were À4. 5 ± 0. 2 °C/km and 23. 2 ± 0. 2 °C, the 1-r root mean square error (RMSE) was 0. 6 °C and the p-value was much lower than 0. 001. The established linear regression was then applied to the Mount Fan Si Pan National Park to derive MAAT at any altitude.
Global MAAT-pH/brGDGT relationships and MAAT-pH residuals per soil type Twenty-seven different soil types were represented in the 358component composite global soil dataset (Fig. 3 ). Visually, the scatters per soil type were not all similar and some soil types such as Luvisols and Regosols were mainly clustered into relatively small MAAT-pH/brGDGT domains and/or comprised a few outliers (Fig. 3  and Supplementary Fig. S1 ). The composite dataset allowed Fig. 3. BrGDGT-derived proxy data from the composite global soil dataset  (Huguet et al. , 2010; Peterse et al. , 2012; Dirghangi et al. , 2013; Anderson et al. , 2014; De Jonge et al. , 2014)  us to extend the number of soil types suitable for statistical analysis from eight to twelve -from 173 to 297 soil samples. MAAT-pH/ brGDGT relationships (Tables  1  and 2 ) and MAAT (and pH) residual distributions (Fig.
4 ) were thus determined for the most represented soil types only. A few examples of soil types were also selected for more detailed MAAT residual distributions (Fig. 5 ). The soil samples reconsidered by De Jonge et al. (  2014 ) were used to compare the newest with traditional MAAT-pH/brGDGT relationships per soil type, as well as MAAT (and pH) residual distributions per soil type using traditional and newest global soil calibrations  (Figs. 4   and 5  and Table 2 ). Using the composite global soil dataset, five soil types had nonsignificant MAAT-MBT 0 /CBT relationships and seven soil types had non-significant pH-CBT relationships (Table  1 ). For MAAT-MBT 0 / CBT, the R 2 range was 0. 0-0. 8 and the RMSE range was 2. 2-7. 0 °C. For pH-CBT, the R 2 range was 0. 0-0. 8 and the RMSE range was 0.
Using the global soil dataset of De Jonge et al. (  2014 ), two or three soil types had non-significant traditional and new MAAT-pH/brGDGT relationships (Table  2 ). For MAAT-MBT 0 /CBT vs MAAT-MBT 0 5ME , the R 2 range was 0. 1-0. 9 vs 0. 0-0. 9 and the RMSE range was 1. 6-6. 8 °C vs 2. 4-5. 6 °C. For pH-CBT vs pH-CBT 0 , the R 2 range was 0. 1-0. 7 vs 0. 0-0. 9 and the RMSE range was 0. 5-1. 1 pH units vs 0. 2-0. 6 pH units (Table  2 ). Ranges of MAAT-MBT 0 /CBT coefficients were 60. 6 for MBT 0 slope, 10. 9 for CBT slope and 22. 2 for intercept whereas ranges of MAAT-MBT 0 5ME coefficients were 46. 2 for slope and 34. 2 for intercept (Table  2 ). Ranges of pH-CBT vs pH-CBT 0 coefficients were 1. 9 vs 1. 8 for slope and 3. 0 vs 2. 8 for intercept (Table  2 ). For all bivariate traditional and newest MAAT-pH/brGDGT relationships, slopes/intercepts per soil type were not all similar after the homogeneity of slopes tests/ANCOVA (Table  2 ).
When compared to the global MAAT-brGDGT relationships, four soil types had similar MAAT-MBT 0 /CBT coefficients and six soil types had similar MAAT-MBT 0 5ME coefficients (Table  2 ). When compared to the global pH-brGDGT relationships, three soil types had similar pH-CBT coefficients and two soil types had similar pH-CBT 0 coefficients (Table  2 ). Using the composite global soil dataset, mean MAAT residuals per soil type based on the MAAT-MBT 0 /CBT global soil calibration of  Peterse et al. (2012).
8 and 6. 2 °C -on average 2. 3 ± 1. 7 °C (Table  3 ). The CBT-based pH estimate range was 3. 9-5. 9 pH units using the global soil calibration of  Peterse et al. (2012) , corresponding to differences with measured pH between À0. 1 and 2. 1 pH units -on average 0. 9 ± 0. 6 pH units (Table  3 ). BIT varied with depth by maximum 0. 04 in all soil profiles (Fig. 6 ). MBT 0 decreased with depth by about 0. 2 in Podzols (even Table  1  Number of samples (n), coefficients (a, b, c, S, I and R 2 ), and root mean square errors (E) of traditional mean annual air temperature (MAAT)-pH/brGDGT linear regressions per soil type using the composite global soil dataset  (Huguet et al. , 2010; Peterse et al. , 2012; Dirghangi et al. , 2013; Anderson et al. , 2014; De Jonge et al. , 2014). Only soil types comprising at least 10 samples from the global dataset are considered for the linear regressions.