dataesr/scientific-paragraphs-categorization huggingface dataset:
Bottom-up vs. top-down control of microbial communities In order to assess the roles of top-down and bottom-up control over microbial group abundances along the transect, we used a combination of approaches based on previously published models. The model described by  Gasol (1994)  was used to assess top-down vs. bottom-up control of HNF abundance (Fig. 6 ). Specifically, this approach compares observed ratios of bacteria to HNF with HNF abundance maxima estimated from empirical data and theoretical interactions between bacteria and HNF. The main assumption of the model is that bacteria to HNF ratios nearer to theoretical maxima imply increased bottom-up control of HNF by bacterial abundance. This difference is quantified with the parameter d, which is calculated as the difference between theoretical Regressions between abundances of bacteria and HNF were measured for Z u and Z l across biogeochemical conditions (data not shown).
This effect was particularly pronounced at GY, where increases in Z l PPE biomass compensated for reductions in Z u Prochlorococcus biomass, resulting in Z e biomass totals being similar to those at MA. To compare our results to those from other ocean basins, we conducted a meta-analysis of datasets reporting Prochlorococcus, Synechococcus, and PPE abundances alongside Chl a concentrations. Mean depth-integrated abundances of Synechococcus and PPE measured along the OUT-PACE transect were similar to those reported elsewhere, as were those of Prochlorococcus at MA and GY (Table  4 ). However, mean depth-integrated abundances of Prochlorococcus at LDB were considerably higher than mean values for other regions. Some of this variation may result from the instrumentation used, with earlier cytometers generally being thought to underestimate weakly fluorescent Prochlorococcus cells near the surface.
Regardless, with Z e -integrated Prochlorococcus abundances at LDB being greater than any others encountered in the literature, these results highlight the importance of transient, localized blooms to cyanobacterial abundance in the region. Table  4. Mean depth-normalized abundances for Prochlorococcus (Pro), Synechococcus (Syn), picophytoeukaryotes (PPE), and depthintegrated concentration of Chl a. %pro calculated as pro/(pro+syn+ppe). Values for different biogeochemical conditions sampled during the OUTPACE transect in bold. Depth-normalized values provided to account for differences in the depth of integration between sampling sites (data not shown). OUTPACE Chl a values correspond to fluorometric data collected at each station of the transect, with the exception of SD 1-3. Chl a values from other datasets correspond to discrete fluorometric or HPLC data.
Acknowledgements. This is a contribution of the  OUTPACE  project (https://outpace. mio. univ-amu. fr/,last access: 7 May 2018) funded by the  French research national agency  ( ANR-14-CE01-0007-01 ), the  LEFE-CyBER program  ( CNRS-INSU) , the  GOPS program (IRD)  and the  CNES. We thank  Thierry Moutin  and  Sophie Bonnet , chief scientists of the OUTPACE cruise. We are indebted to  Olivier Grosso  and  Sandra Helias-Nunige  for nutrient measurements, to  Sophie Bonnet  for N 2 fixation measurements, to  Joséphine Ras  for HPLC pigment measurements, and to  Gilles Rougier  and  Marc Picheral  for their help in CTD rosette management and data processing. We are grateful to the crew of the  R/V L'Atalante  for outstanding shipboard operation. The  National Science Foundation (NSF )  OCE-1434916  award to  Solange Duhamel  supported  Solange Duhamel  and  Moïra Dion ; the  NSF   OCE-1458070  award to  Solange Duhamel  supported  Nicholas Bock.
2005). 2 Hawaii Ocean Time-series; HOT-Data Organization and Graphical System, http://hahana. soest. hawaii. edu/hot/hot-dogs (last access: 7 May 2018). 3 Bermuda Atlantic Time-series, http://bats. bios. edu/bats-data/ (last access: 7 May 2018). 4 Atlantic Meridional Transect; British Oceanographic Data Centre, http://www. bodc. ac. uk/ (last access: 7 May 2018). 5 C-MORE Data System, http://hahana. soest. hawaii. edu/cmoreDS (last access: 7 May 2018). 6 LEFE-CYBER Database, http://www. obs-vlfr. fr/proof/ (last access: 7 May 2018).
Site and Data During the 2014-2015 winter, a ground-based L-band radiometer measurement campaign was conducted at the Kernen Crop Research Farm (KCRF; 52. 149 • N; 106. 545 • W), a 380 ha property within the city of Saskatoon owned and operated by the University of Saskatchewan, Canada. L-band radiometer measurements and coincident snow pit and meteorological observations were performed. The study area and the campaign and datasets are described in detail in Reference  [17]. At KCRF, tree scenes were located adjacent to each other within the same field to ensure similarity in background emission, with only the overlying snow conditions altered. The three scenes include: Scene 1-Undisturbed snow: A scene of naturally accumulating snow-covered ground; Scene 2-Snow free: snow was removed on a weekly basis to maintain bare ground; Scene 3-Artificially compacted snow: a scene with deep and dense snow.
Additional snow was manually added to Scene 3 and compacted on December 10th, 19th, 2014 and January 11th 2015 and then left to evolve naturally for the rest of the season. As this study focuses on snow emission modelling, Scene 2 was not used in this study. The scenes were characterized by silt-loam bare soil conditions. Wheat residue was noted and not disturbed during the study. Surface roughness was derived using a terrestrial Light Detection and Ranging (LiDAR) system using a surface roughness tool called Roughness from Point Cloud Profiles (RPCP)  [26]  implemented in Whitebox Geospatial Analysis Tools (GAT) software  [27]. Surface roughness had a root-mean-square height (RMSH) of 1. 78 cm and 1. 64 cm within Scene 1 and 3, respectively, at the beginning of the study and remained almost unchanged throughout the study (RMSH = 1.
This algorithm is based on an equivalent porous medium concept and parameterized by a Cellular Automata approach in order to provide a realistic reconstruction of the karstic network. This algorithm is linked to the groundwater flow equation, reformulated in frequency domain, to simulate the amplitudes and phase shifts responses to the harmonic pumping tests. The inverse process was successfully applied on the dataset collected with both periods, in a separate and joint way. The results obtained allowed for a discussion on the efficiency of the harmonic pumping tomography for the characterization of the karstic structures.
Hydraulic data were obtained from the Terrieu experimental site located in Montpellier, in Southern France. At the same site, results of hydraulic tomography, in which hydraulic responses were generated with constant-rate pumping tests, have already been presented and discussed in our previous works  ( Wang et al. , 2016 ( Wang et al. , , 2017;; Fischer et al. , 2017c ). In this new work, we rely our analysis on a set of responses to harmonic pumping tests with different oscillation periods, to characterize the karst network. We describe in Section 2 the experimental study site, the harmonic pumping investigation led on it, and the processing made on the measured field responses for the later interpretation. Then, in Section 3 we briefly introduce the numerical model setup and the Cellular Automata-based Deterministic Inversion (CADI) algorithm.
Harmonic pumping investigation The main dataset used in this study was collected from an investigation using harmonic pumping tests performed at the Terrieu site. We have conducted pumping sequentially in four different boreholes while Fig. 1. Maps of localization of the Terrieu site in France (left) and well pattern on the site (right). Boreholes used as pumping and measurement points are indicated using red triangles, and boreholes used only as measurement points are indicated using grey circles. Boreholes indicated by solid black points were not used during the investigation. The blue dotted line delineates a preferential flow path identified by previous studies ( Jazayeri  Noushabadi, 2009 ; Dausse, 2015 ) , which shows a connectivity between P2, P8, P11, P12, P15 and P20. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.
The FFT permits to extract the main oscillatory components of a signal, to denoise it, and to interpret its parameters. Fig. 3  presents the FFT results for the oscillatory signals of three representative boreholes (P10, P11, P2) during a pumping in P15 with the two periods (2 min and 5 min). The interpretation results of amplitude and phase offset for the entire dataset are presented in Appendix Table  A1.
Modeling strategy We have applied the CADI method on the 2D parameterized model presented in the previous section, in order to find a structured property distribution able to reproduce the measured oscillatory responses (amplitudes and phase offsets) presented in the Section 2. 3. We considered the oscillatory responses interpreted as negligible (amplitude < 1 mm) to be null for the inversion. We have coded the CADI algorithm with Matlab and used Comsol Multiphysics to solve the model in the frequency domain (see Eq. (  5 )). This resolution was led using a finite element method on a triangular mesh, refined around the boundaries between the matrix and the conduit network represented by the equivalent porous media properties distributed over the model. This mesh refinement is performed with the Comsol mphimage2geom function on the conduit network image generated by the cellular automata.
4. A first set of inversions were led by separating the 2 min and 5 min period responses, and with a 6 × 4 subspaces partitioning of the model (with a conduit thickness of 2 m). The results of these separate inversions have then been repartitioned in 12 × 8 subspaces models (with a conduit thickness of 1 m), which were used as initial models for a new inversions with the same datasets. This partitioning of the models per-Fig. 6. Schematization of the complete multi-scale inversion process. Starting from an initial model, firsts inversions were led for a 6 × 4 partitioning (shown by the grid). The results were refined to 12 × 8 subspaces and used for new inversions. Finally, joint inversion were led starting from the results of the previous separate inversion.
The models of networks produced by inversions of pumping tests of different periods are very different, indicating that the both sets of responses provide different hydraulic information of the aquifer. The reconstructed network for the dataset of a period of 5 min is denser than that for a period of 2 min. The 5 min period dataset seems to carry information about karstic structures of different scales (conduits, fractures, fissures) around the measurement points, while the 2 min period dataset tends to characterize more specifically the most conductive karstic structures over the field scale. This hypothesis is supported by the maps of the amplitude in the models. Amplitude responses to a 2 min pumping signal in P15 quickly decease around the borehole but remain visible in the coarse network over almost the entire field, while the ones to a 5 min pumping signal stay high in the dense karstic network around P15, and decrease beyond ( Fig.
Effect of the period of pumping signal on the inversion results It appears, in fact (see Table  3  ), that the separate models can badly simulate the responses associated to the signal period not used in each Fig. 8. Maps of simulated spatial amplitude (Amp. ) and phase offset (P. -O. ) with the models in Fig. 9  for a pumping in P15 with a signal period of 2 min and 5 min. inversion (i. e. 5 min responses simulated through the model generated with 2 min responses, and conversely), suggesting that each set of response contains different and complementary information for the characterization of the field. Therefore, new inversions were started from the results presented in Fig. 9  as initial models, by joining the miss-ing responses to the 'observed responses' dataset in the inverse process (see the joint inversion results in Fig.
6  ). For a better understanding, we will mention as '2 min' and '5 min' separate results the model results in Fig. 9  produced from the inversion of the responses to the 2 min and 5 min periods separately. The models produced by inversions of the joint datasets started with the '2 min' model and with the '5 min' model will be respectively mentioned as the '2 min ( + 5 min)' and '5 min ( + 2 min)' joint results. The results of the joint inversions are presented in Fig. 10. The '2 min ( + 5 min)' result, solution to the inversion started from the '2 min' result, is very close to its initial model. The '5 min ( + 2 min)' result, solution to the inversion started from the '5 min' result, shows some modifications on the periphery of its network (P4, P5, P10, P11). Both joint solutions, however, do not permit a reproduction of the measured signals as good as the one generated by the '2 min' and '5 min' separate results (see Table  3  ).
Acknowledgments We would like to thank  Todd C. Rasmussen  and two anonymous reviewers for their relevant comments and propositions which permitted to significantly improve the quality of this article. We thank the  Normandy region  for providing financial support for the Ph. D. of Pierre Fischer. We also thank  Michel Simon  for his precious contribution during the field investigation. The hydraulic data collection and hydrogeological characterization of the Terrieu experimental site are part of work of the  French research network SNO Karst.
He suggested that the value of 0. 6 obtained by Hack is only relevant for small (<20,000 km 2 ) catchments and an exponent of 0. 46 is more suitable for large basins. From an extensive study based on datasets spanning eleven orders of magnitude in basin area, this last conclusion is questioned by Montgomery and Dietrich  11. They obtained Hack's coefficient and exponent of 2. 02 and 0. 49 (for L and A expressed in km and km  2  , respectively), regardless of the catchment size, suggesting a geometric similarity of the drainage network. More recent estimates  [12] [13] [14] [15]  based on regional studies give n and c between 0. 45 and 0. 7 and 1 and 6, respectively. This wide range of values may be partly due to the spread within dataset or to local variations in climatic or geological properties.
It can also be related either to various methods assuming different definitions for L or to the use of a non-homogeneous dataset, which combines stream channels and basins either manually digitised from topographic maps or automatically extracted from digital elevation models (DEM). While the significance of the exponent n has been the focus of numerous studies, the significance of Hack's coefficient has seldom been addressed and remains an open question. Here, we reevaluate these two coefficients using a consistent processing of DEM data in various settings, which include mountain ranges and plains as well as semi-arid to humid climates. We specifically focus on the role of the basin shape on Hack's parameters using refinements of established methods. We analyse the morphology of drainage basins in the Bhutan Himalaya, where climate and geology are well established  [16] [17] [18]  and show how the size and the geometry of river drainages influence c and n.
6a ). We divide our basin dataset into six rainfall classes from dry (<0. 5 m yr -1 ) to very humid (>4 m yr -1 ) following common practice for the Himalayas  16  and limit our analysis to basins characterised by a single rainfall class to avoid spatial variations that may affect results for large catchments (Fig. 6a ). To maintain consistency with our Fig. 3  'Length-Area' dataset from ca. 22,000 river basins in Bhutan Himalaya as a function of basin shape. Colour code is for the Gravelius compacity coefficient  19  (GC, see Fig. approach, we divide each of the six rainfall classes into eight subclasses based on GC values (as in Fig. 5 ). For a given GC class, we observe that both estimated n exponent and c coefficient are independent of the mean annual precipitation (Fig. 7d, f ) and conclude that mean annual rainfall does not play a significant role on Hack's Law.
3-2. 2 vs. 1. 5-2. 5). These results, though significantly different from previously reported parameters (n = 0. 7 and c = 1. 2)  12  , are derived from a significantly larger dataset (ca. 21,000 vs. 20 basins) and are statistically more robust. This finding leads to a fractal dimension D = 2n = 1 for channel length  5  and questions the fractal character of river networks in orogenic zones  24. This may suggest a similarity of river networks in these specific settings, in spite of very different rainfall and uplift rates. Compared to Bhutan, basins from Iowa and South-East Africa exhibit higher exponents and lower coefficients. These results are consistent with previous studies  12, 14  and suggest a different behaviour for low-relief landscapes. Despite these differences, the comparison with other regions confirms the main findings obtained in The consistency of our results across such very different settings indicates that river basins reach a shape that appears to be unrelated to external influences such as lithology, tectonic uplift or rainfall distribution.
Together, these results lead us to confirm the geometric similarity of river networks with basin size-independent Hack's parameters  11. Our findings reveal that basin shape gives a physical meaning to the data dispersion observed in previously published L-A data sets. This parameter needs to be considered in Hack's Law to better describe the main stream length-area relationship. We hence propose a modification of Hack's law (given by Eq. (  1 )): L ¼ c φ ð Þ A n ; ð4Þ where n is a constant, ranging between 0. 5 and 0. 6, and c is a coefficient, which depends on the basin shape φ (for this study, the Gravelius Compacity coefficient). In summary, when applied to five selected regions of low to high relief, our analysis shows that auto-similarity is confirmed for mountainous areas, and that both c coefficient and n exponent are independent of lithology and rainfall, and finally that Hack's coefficient is related to basin shape.
Methods Data and study areas. The topographic dataset used in this study is the 30-mresolution ALOS-World 3D graciously provided by JAXA. The standard deviation of elevation is estimated to be 5 m  25  , which can be considered as negligible with respect to the scale of morphometric properties analysed in this study. Overall, we applied our approach to five regions in order to provide a wide range of both climatic and geologic settings (Supplementary Fig. 1 2 3 4 5 ). Areas are chosen for their well-established geology and rates of tectonic activity. The Bhutan Himalaya is located in the eastern part of the Himalayan arc. This active mountain belt area presents several specific features such as high tectonic uplift (up to 1 cm. yr -1 ) and high mean annual rainfall (up to 6 m. yr -1 ) concentrated during the monsoon period.
( 2 )LFig. 1 ' 21 Fig. 1 'Length-Area' dataset from ca. 22,000 river basins (grey circles) in Bhutan Himalaya. Green and blue lines show the original Hack's law 2 and the law proposed by Montgomery and Dietrich 11 , respectively. Inset shows topographic map of Bhutan region with the limits of the studied region. Scale bar: 100 km.
R-MATRIX CALCULATIONS Inelastic electron collision calculations with molecular ions OH + and SH + were performed using the R-matrix method  (Tennyson 2010)  within the Quantemol-N  (Tennyson et al. 2007 ) expert system to run the UK molecular Rmatrix codes (UKRMol)  (Carr et al. 2012). Details follow closely the calculations performed in our previous paper  (Hamilton et al. 2016) , denoted I below, and are not repeated here. The calculations produce T-matrices which are processed by electron-impact rotational excitation code ROTIONS  (Rabadán & Tennyson 1998) , which employs the Coulomb-Born approximation to include the effects of high partial waves  (Norcross & Padial 1982). In particular, ∆N = 1 transitions (N is the molecular ion rotational angular momentum) are strongly influenced by the long-range dipole moment and ROTIONS uses the Coulomb-Born approximation to include the contributions of partial waves with ℓ > 4.
Cross-sections and rate coefficients Working in C2v symmetry, each of the above calculations produces eight fixed-nuclei T-matrices for each molecule: the four symmetries A1, A2, B1, B2 for both doublet and quartet states of the N + 1 electron systems. These T-matrices are used to calculate the electronic excitation cross sections using standard equations  (Tennyson 2010 ) and, once converted to the C∞v point group, rotational excitation cross sections using the program ROTIONS  (Rabadán & Tennyson 1998 ) using the rotational constants and isotope specific dipole moments given in Tables  2  and 4. ROTIONS computes the rotational excitation cross sections for each doublet and quartet state independently. The total rotational cross sections are thus obtained as the (weighted) sum of the doublet and quartet cross sections.
Data include all electronic states with thresholds below 5 eV. • Rotation excitation cross sections and rate coefficients for the three isotopes of OH + and the four isotopes of SH +. Rotational excitation datasets are published for transitions with starting values of N = 0 to N = 11. • Hyperfine de-excitation rate coefficients for 16 OH + , 18 OH + and 32 SH +. Hyperfine de-excitation datasets are published for transitions with starting values of (N, j, F ) = (0, 1, 3/2) to (11, 11, 21/2). Table  6. Hyperfine de-excitation rate coefficients in cm 3 s - 1  for SH + in initial levels (N, J, F ) = (1, 2, 5/2) and (1, 2, 3/2). Powers of ten are given in parentheses. These data will also be placed in the BASECOL database  (Dubernet & et al. 2013). Hyperfine data for 16 OH + , 18 OH + and 32 SH + have been also combined with the spectroscopic data from the Cologne Database for Molecular Spectroscopy  (Müller et al.
2005 ) in order to provide a full and consistent dataset adapted to radiative transfer studies (see below). Hyperfine data for the other isotopologues are not provided due to the lack of spectroscopic data (the recent entry 34 SH + at CDMS is currently limited to nine hyperfine transitions within N = 1 -0). N j F N' j' F' 10 K 100 K 1000 K 1 2 5/2 0 1 3/2 1.
ACKNOWLEDGEMENTS We thank  Yohann Scribano  for help at the start of this project and  François Lique  for helpful discussions. This work has been supported by an  STFC   CASE studentship , grant number  ST/K004069 , for JRH, the  Agence Nationale de la Recherche  ( ANR-HYDRIDES ), contract  ANR-12-BS05-0011-01  and by the  CNRS national program  " Physico-Chimie du Milieu Interstellaire ".
Our aim is to provide an overview of the OZCAR network, its objectives, components, scientific questions, and data management; the current status of instrumentation along with that of databases and metadatabases; and existing initiatives for linking data and models based on OZCAR data. The discussion builds on the current achievements to take a step forward and describe the ambitions of OZCAR and how this initiative can be related to others worldwide. Most of the ideas put forward here were discussed during the kickoff meeting of OZCAR held in Paris, 7 Feb. 2017. 6 Presentation of the OZCAR network.
The Tourbières Observatory The Tourbières (Peatland) Observatory is a network of four French instrumented sites and one Siberian mire aimed at studying the effect of global change on the C sink function and the hydrological budget of temperate and subboreal peatlands, which are ecosystems containing a third of the global surface C stock in an area accounting for only 3 to 5% of the land surface. The French sites were set up in 2008 to 2010 according to a climatic gradient (lowland to mountain climate) to ensure long-term monitoring of greenhouse gases (CO 2 , CH 4 , H 2 O, and N 2 O), dissolved and particulate organic C (DOC and POC) fluxes as well as environmental variables that impact greenhouse gases and DOC and POC fluxes, and to generate interoperable databases.