dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Despite the existence of dense networks of meteorological monitoring stations around the world, plant ecophysiology and biogeography suffer from a lack of globally distributed observational data, especially those central to the estimation of ecosystem-level photosynthesis, including photosynthetic photon flux density and soil moisture. To overcome this deficiency, we present simple process-led algorithms for simulating habitats (SPLASH) for generating driving datasets for ecological and land-surface models (e. g. , monthly carbon and water fluxes or seasonal plant functional trait distributions) from more readily available meteorological observations. SPLASH is a continuation of the STASH (static shell) model, which was originally developed for modeling the climatic controls on plant species distributions at a regional scale  (Sykes and Prentice, 1995, 1996; Sykes et al.
Unlike the STASH model, SPLASH explicitly distinguishes potential and equilibrium evapotranspiration, recognizing that under well-watered conditions the excess of the former over the latter is a requirement for foliage to be cooler than the surrounding air, as has long been observed under high environmental temperatures (e. g. ,  Linacre, 1967). Input values of latitude, φ (rad), elevation, z (m), mean daily near-surface air temperature, T air ( • C), and fractional hours of bright sunshine, S f (unitless), are used for calculating the daily quantities of net radiation and evapotranspiration. Daily precipitation, P n (mm d -1 ), is used for updating daily soil moisture. T air and P n may be derived from various sources, including the freely available daily-averaged air temperature and precipitation reanalysis data from the Water and Global Change (WATCH) program's meteorological forcing dataset  (Weedon et al.
, 2014). Meteorological variables are also available in the Climatic Research Unit (CRU) gridded monthly time series datasets  (Harris et al. , 2014) , which may be downscaled to daily quantities by means of quasi-daily methods (e. g. , linear interpolation). Cloud cover fraction, for example the simulated quantities given in the CRU TS3. 21 dataset, may be used to approximate S f. Penman's one-complement approximation based on the cloudiness fraction is regarded here as a sufficient estimate of S f  (Penman, 1948). The piecewise linear method of  Hulme et al. (1995)  -an adaptation of the Doorenbos-Pruitt estimation procedure  (Doorenbos and Pruitt, 1977)  -as used in the development of the CRU cloudiness climatology  (New et al. , 1999)  gives similar results. We present SPLASH comprehensively re-coded in a modular framework to be readable, understandable, and repro-ducible.
To facilitate varied application requirements (including computational speed), four versions of the code (C++, FORTRAN, Python, and R) are available in an online repository (see Sect. 6). The algorithms as presented here focus on application to individual site locations, but a natural extension is towards spatially distributed grid-based datasets. In line with the intention of the original STASH algorithms, we also present bioclimatic indices at the monthly and annual timescales to exemplify the analytical applications of the SPLASH model outputs.
The first year of the simulation (i. e. , 1991) was iterated (approximately twice) until the daily soil moisture, initialized at zero, reached equilibrium, after which the model was spun-up for eight years  (i. e. , 1992-1999). The results, shown in Figs. 4  and 5 , are for the year 2000. Accompanying the daily SPLASH results in Fig. 4 , shown in red, are daily surface fluxes based on the three-layer variable infiltration capacity (VIC) model, extracted from the 1/16 • pixel over each of the six sites from the datasets provided by  Livneh et al. (2015). Figure  4a  shows the daily results for a tundra region over Banff National Park in Alberta, Canada, with a mean annual temperature of -4 • C and annual precipitation of 986 mm. The SPLASH H N depicts a bell-shaped curve characteristic for the Northern Hemisphere.
Following the spin-up to equilibrate the soil moisture, the model was driven once again to produce daily simulations of net radiation and soil moisture. Figure  7b  and e show the SPLASH results of the mean daily relative soil moisture (%) for the months of June and December, respectively. An ice sheet was imposed over Greenland (i. e. , no soil moisture). For comparison, the National Center for Environmental Prediction (NCEP) Climate Prediction Center (CPC) Version 2 mean soil moisture  (van den Dool et al. , 2003)  for June and December of the same year are plotted in Fig. 7a  and d , respectively. The relative soil moisture in both datasets is computed as the ratio of millimeters of soil moisture over the total bucket size (i. e. , 760 mm in NCEP CPC and 150 mm in SPLASH). Overall, the SPLASH model simulates soil-moisture patterns similar to the NCEP CPC model results.
Code availability The code, in four programming languages (C++, FOR-TRAN, Python, and R), is available on an online repository under the GNU Lesser General Public License (https: //bitbucket. org/labprentice/splash). The repository includes the present release (v1. 0) and working development of the code (with makefiles where appropriate), example data, and the user manual. All four versions of the code underwent and passed a set of consistency checks to ensure similar results were produced under the same input conditions. The following describes the requirements for compiling and executing SPLASH v. 1. 0. For the C++ version, the code was successfully compiled and executed using the  GNU C++ compiler (g++ v. 4. 8. 2)  provided by the GNU Compiler Collection  (Free Software Foundation, Inc.
, 2016). It utilizes the C numerics library (cmath), input/output operations library (cstdio), and the standard general utilities library (cstdlib), and it references the vector container and string type. For the FORTRAN version, the code was successfully compiled and executed using the PGI Fortran compiler (pgf95 v. 16. 1-0) provided by The Portland Group -PGI Compilers and Groups (NVIDIA Corporation, 2016) and the GNU Fortran compiler (gfortran v. 4. 8. 4) provided by the GNU Compiler Collection  (Free Software Foundation, Inc. , 2016). For the Python version, the code was successfully compiled and executed using Python 2. 7 and Python 3. 5 interpreters  (Python Software Foundation, 2016). It requires the installation of third-party packages, including  NumPy (v. 1. 10. 4 by NumPy Developers, 2016)  and SciPy (v.
0. 17. 0 by SciPy Developers, 2016) and utilizes the basic date-and time-type (datetime), logging facility (logging), Unix-style pathname pattern extension (glob), and miscellaneous operating system interface (os) modules. For the R version, the code was successfully compiled and executed using R-3. 2. 3 "Wooden Christmas-Tree" (The R Foundation for Statistical  Computing, 2015). )  where L v ranges from about 2. 558×10 6 to 2. 413×10 6 J K -1 for T air between -20 and 40 • C. The temperature and pressure dependence of the density of water, ρ w , may be expressed as follows  (Chen et al. , 1977) : The equation for ρ o is based on the work of  Kell (1975) : , ( B2 ρ w = ρ o K o + ρ o = 8 i=0 C i T air i. (B4) The equation for K o is also based on the work of  Kell (1975) : K o = 5 i=0 C i T air i.
Acknowledgements. This work was primarily funded by  Imperial College London  as a part of the  AXA Chair Programme on Biosphere and Climate Impacts. It is a contribution to the  Imperial College initiative on Grand Challenges in Ecosystems  and the Environment, and the  Ecosystem Modelling And Scaling Infrastructure (eMAST)  facility of the  Australian Terrestrial Ecosystem Research Network (TERN). TERN is supported by the  Australian Government  through the  National Collaborative Research Infrastructure Strategy (NCRIS). BDS funded by the  Swiss National Science Foundation (SNF)  and the  European Commission 's  7th Framework Programme , under grant agreement number  282672 ,  EMBRACE  project. WC contributes to the  Labex OT-Med  (no. ANR-11-LABX-0061 ) funded by the  French government  through the A *  MIDEX  project (no.
ANR-11-IDEX-0001-02 ). AGS has been supported by a  Natural Environment Research Council  grant ( NERC  grant number  NE/I012915/1 ). VIC simulations utilized the Janus supercomputer, which is supported by the  National Science Foundation  (award number  CNS-0821794 ) and the  University of Colorado Boulder. The Janus supercomputer is a joint effort of the  University of Colorado Boulder , the  University of Colorado Denver , and the  National Center for Atmospheric Research. CERES EBAF data were obtained from the  NASA Langley Research Center Atmospheric Science Data Center. CPC  soil moisture data provided by the  NOAA/OAR/ESRL PSD, Boulder, Colorado, USA , from their website at http://www. esrl. noaa. gov/psd/. Edited by:  D. Roche  Reviewed by: two anonymous referees.
These data are complemented by in situ analyses of the Pb isotope composition of the various mineral phases acquired by secondary ionization mass spectrometry (SIMS) for a subset of these chondrules, which allows us to determine the nature of the carrier phase of uranium and, hence, radiogenic Pb in these objects. Finally, using multiple collector plasma source mass spectrometry (MC-ICPMS), we have measured the 238 U/ 235 U ratio of 7 individual chondrules (6 of which have been Pb-Pb dated) to test for potential U-isotope heterogeneity as well as the Zn stable isotope composition of 7 chondrules to assess the thermal history of their precursors.
Our chondrule age dataset based on the analysis of 22 individual objects demonstrate that the production and melting of chondrules began contemporaneously with CAI condensation and melting and continued for ~4 Myr (Fig. 1A ). We show in Fig. 1B  the Pb-Pb age distribution of chondrules, which, at face value, indicates a progressive reduction in chondrule production rate through time. Indeed, approximately 50% of the chondrules investigated here formed within the first Myr of the protoplanetary disk, suggesting that chondrule formation was more efficient in early times. The residence time of millimeter-sized solids in evolving disks is predicted to be extremely short relative to the typical lifetimes of protoplanetary disks due to to aerodynamic drag  (11). Although some mechanisms exist to limit the inward drift of millimeter-sized solids such as, for example, dust trapping, these operate on timescales typically shorter than the age variability reported here for chondrules  (12).
Thermally-processed precursors may represent chondrule fragments or, alternatively, entire chondrules having experienced more than one chondrule-forming event. A prediction of chondrule reworking in a closed system is a progressive increase in the inferred µ values of individual chondrules through time. Although such a correlation appears to be present in our dataset (Fig. S27 ), a number of different disk processes may perturb this relationship. For example, the chondrule-forming process may have been more efficient (with higher temperatures) at early times such that a progressive decrease in µ values with time would be expected for chondrules formed during the epoch of primary chondrule production, namely in the first Myr. Moreover, the accumulation of small amounts of Pb-rich dust onto the rims of chondrules that may be incorporated into the chondrule interior during flash melting will have the effect of lowering the µ value, which would prevent the µ from reaching extreme values in these cases.
We determined the Pb isotope composition of the individual phases of chondrules 1-C2 and 3-C1 using a CAMECA 1280 ion microprobe at the Swedish Museum of Natural History. The U isotope compositions of an aliquot of the chondrules used for Pb-Pb dating was determined for seven chondrules using the ThermoFisher Neptune Plus MC-ICPMS at the Centre for Star and Planet Formation based on protocols described in  (6). Similarly, the Zn isotope compositions of an aliquot of the material used for Pb-Pb dating was determined for seven chondrules using the ThermoFisher Neptune Plus at the Institut de Physique du Globe de Paris based on protocols described in  (21). All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. S1. Electron microprobe analyses of the NWA 5697, NWA 7655, and NWA 6043 chondrules studied.
Bioinformatics data processing and analyses The resulting 16S pyrosequenced reads were processed, clustered and taxonomically assigned using the Quantitative Insights into Microbial Ecology (QIIME) v. 1. 7. 0 pipeline  [29]. A workflow of all commands is available in the supporting materials (S1 File). After processing and quality filtering, 31 of the 50 samples originally collected had sequence counts of !500; therefore, the dataset composed of those 31 samples was used for downstream analyses (S2 and S3 Tables). Dataset demultiplexing and quality checks were performed using the split_libraries. py script to remove low quality and short sequences (< 250bp). Next, reads were clustered into de novo operational taxonomic units (OTUs) using UCLUST  [30]  via the pick_otus. py, with a pairwise sequence identity cut off value of 97%.
Representative sequences for the clusters were then generated using the pick_rep_set. py script. Additionally, chimeras and "quasi-singletons" (sequences appear < 3 times in the dataset) were removed using USEARCH v. 6. 1  [30]. Finally, taxonomy was assigned to the representative sequences using the RDP classifier method implemented through the assign_taxonomy. py script in QIIME, with the Greengenes 2013 reference database  [31]  at 80% threshold identity  [32]. Multiple rarefactions were applied to obtain standardized estimates of alpha diversity using Chao1 measurement for species richness  [33]. Chao1 was calculated using the specpool function in the Vegan community ecology package (v. 2. 3-2) executed in the software R (v. 3. 2. 2). Analysis of Similarities (ANOSIM) and Permutational Analysis of Variance (PERMANOVA) were performed, to test for the null hypothesis of homogeneity of bacterial communities among sample groups (i.
e. organs, molts, life stages) using PAST (Paleontological statistics software package,  [34] ). ANOVA testing of OTU frequency among groups was performed using the group_significance. py script in QIIME. Finally, Multidimensional Scaling (MDS) analyses were performed based on Bray-Curtis distances in PAST. Either as a consequence of amplification failures during the library preparation stage or low DNA concentration in some of the samples, the initial balanced sampling design resulted in uneven sample numbers for some "treatment" categories (i. e. organs, molts and life stages) after the quality filtering process of the data. This impeded the nested design initially planned for ANOSIM and PERMANOVA statistical testing, which was thus performed on treatments that had equal or near equal representation of each category.
exoculata was collected (see  [35] ). Sequences that were ascribed to the ectosymbiont OTUs were filtered from the main dataset using the filter_fasta. py command in QIIME. Alignment and processing of the sequences sets was completed executing the make_alignment. py and filter_alignment. py commands via the PYNAST method  [36]  at the default parameters. Alignments were performed with the aid of the SILVA 123 core aligned reference dataset (  [37] , http://www. arb-silva. de/). After filtering, alignments were imported into DNAsp v. 5. 10. 1  [38]  where identical sequences were clustered into haplotypes. Haplotype outputs were exported in Roehl format for network calculation and drawing in Network by Fluxus (http://www. fluxus-engineering. com). Due to the presence of multiple possible characters for numerous sites, we implemented the Median-joining procedure  [39].
Results Our pyrosequencing of the bacterial communities associated to the vent shrimp Rimicaris exoculata produced a total of 366,134 raw sequence reads. The raw reads were quality filtered, resulting in 106,179 sequences that had a mean sequence length of 406-bp. Next, the sequence dataset was clustered into 1,726 representative OTUs; 1,621 (94%) of these OTUs were taxonomically assigned to homologs in the RDP database. Those OTUs that remained unassigned (6. 1%) were discarded for remaining analyses.
OTUs assigned only to the domain level Of the assigned OTUs, 1,063 (about 65%) were described only to the domain level, referred to as 'bacteria', and had an average length of 320-bp. Further inspection identified that seven of the 31 samples had >25% of the sequences assigned to 'bacteria' (S5 Table ). As these samples represented various vents, organs and molts, there was no specific sample type to which the unclassified bacteria were most common. Given their large percentage in the total dataset, 100 of the most common 'bacteria' OTUs (those to which the highest number of sequences were assigned) were individually blasted against NCBI's global public database. Only two of the 100 received hits to isolates of known R. exoculata symbionts, revealing the limited number of known lineages in the pool assigned only to the domain.
In attempt to identify possible phylogenetic clustering of the unclassified bacteria, the same 100 OTUs were aligned with R. exoculata ectosymbiont sequences from classes Epsilonproteobacteria, Gammaproteobacteria, Mollicutes and Deferribacteres, which were identified by previous researchers  [14, 20, 24]. The overall findings showed no specific clustering with any of the known symbionts (S1-S4 Figs), which confirms that these sequences are too ambiguous to be reliably assigned to known taxonomy. The large percentage of the unclassified bacteria prompted us to perform statistical testing with and without this group. While there were differences in species richness estimates between the two datasets, the results of the statistical testing were congruent between the datasets including and excluding the unclassified bacteria.
As the unclassified bacterial may include undetected chimeras, artifacts produced from 454-sequencing, or partial archaeal sequences or host DNA, we chose to focus on the conservative dataset that excluded these unclassified bacteria, which was composed of 557 OTUs assigned beyond the domain level. However, statistical analyses that include unclassified bacteria are detailed in the supporting materials (S6-S8 Tables, S5 and S6 Figs) and show similar results obtained with the dataset that excludes the unclassified bacteria.
Bacterial communities over the life cycle Our examinations of the bacterial composition within eggs, juveniles and adult stages from Logatchev identify that specific symbionts dominate, depending on the age of the host. The bacterial community present in eggs is dominated by Gammaproteobacteria, but also hosts Espsilonproteobacteria and very few Mollicutes (Fig 3, S10 Table ). These results are in line with some findings reported by Guri et al. [19] , with the exception of Mollicutes, which were not recovered. The present dataset shows Epsilonproteobacteria as the most common class in juveniles and adult guts, which was also reported by Durand et al. [20, 24] , but without quantitative consideration. Additionally, adults exhibited higher species richness when compared to the other life stages at Logatchev (Table  1 ), which suggests an increased role of symbionts in adults, although further testing at other vents is needed to confirm this observation regarding the influence of life stage.
Acknowledgments We would like to thank the expedition leaders, captains and crews of the oceanographic cruises EXOMAR 2005, SERPENTINE 2007, MoMARDREAM 2007 and MoMAR08 2008, as well as the crews of the vessels N/O Pourquoi pas?,  L'Atalante , and the  HOV Nautile  and ROV Victor 6000. Additionally, we would like to thank  Valerie Cueff Gauchard  and  Olivier Soubigou  for their contributions to this project.
Although the ANI data suggest that strain Ferrero could be defined as a new species, a comprehensive multiphase taxonomic study using the type strains of all existing Curtobacterium species is warranted to decipher its precise taxonomic status. Accession number(s). This whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession no. NXIA00000000. The version described in this paper is the first version, NXIA01000000. Strain Ferrero has been deposited at CIRM-CFBP, the French Collection for Plant-Associated Bacteria (http://www6. inra. fr/ cirm_eng/CFBP-Plant-Associated-Bacteria), with the identifier CFBP 8586.
ACKNOWLEDGMENTS This work was supported by grant  ANR-2010-GENM-013  from the  French Agence Nationale de la Recherche. The LIPM is part of  Labex TULIP  ( ANR-10-LABX-41 ). E. O. thanks the  Iranian Ministry of Science, Research and Technology  for financial support during his sabbatical stay at IRD, Montpellier, France.
To achieve our 1 PIAF (Programme Interdisciplinaire sur les Indicateurs Autochtones de la Flore et de la faune) is a project funded by the ANR Young Researcher Program ANR-13-JSH1-0005 from 2014 to 2018: http://www. anr-piaf. org. PIAF brings together a research team from 6 research institutes and 10 research laboratories and is coordinated by Anne Sourdril (CNRS, UMR7533 Ladyss). The team is composed of (by laboratory and alphabetical order): A. Sourdril; Émilie Andrieu, Cécile Barnaud, Marc Deconchat, Wilfried Heinz, Sylvie Ladet (Inra UMR1201 Dynafor); Nadia Bélaïdi (MNHN UMR7206 Écoanthropologie et Ethnobiologie); Éric Garine, Émilie Guitard, Jean Wencélius (Université Paris Nanterre, UMR7206 LESC); Christine Raimond (CNRS, UMR8586 Prodig); Michel de Garine-Wichatitsky (Cirad); Sylvain Aoudou Doua (Université de N'Gaoundéré, Cameroon); Brian J.
Phase 2: data collection The objective of this phase was to collect new data through the use of a common protocol to assure data comparability. Our research protocol includes provisions for multiple researchers and technicians in and across field sites. As a result, we drafted a methodological manual À a shared handbook based on a common sampling strategy and similar interview guidelines, commented transects and freelist elicitation methods formalizing both data collection (samplings or strict guidelines) and data management (timelines, table to be filed). These data are to be integrated into a common database where, without attempting a complete standardization of the data, we will place them in similar form across the different sites (Tasks 0, 2, 3 and 4). The protocol was built by a small working group of PIAF researchers consisting of one political ecologist, one social geographer and two cultural anthropologists, and then shared and validated with all participants during dedicated meetings.
Fieldwork was conducted in all 12 sites between February 2015 and June 2016. The PIAF field team is composed of 14 interns À mainly sociologists, geographers and anthropologists À and 8 project investigators À permanent and postdoctoral researchers. The PIAF dataset is now filled with more than 450 transcribed semi-structured interviews, 420 informant files, 250 Excel documents with freelist information on birds, trees, mammals and plants À which translates to more than 900 individual freelists; we also have hundreds of participant observation descriptions, and thousands of pictures to be further analyzed and compared.