dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Crop data. Crop area (in hectare, ha, for sown areas) and production (in kg) statistics on departmental level from 1900 until 1988 were collected from books of national agricultural statistics ('Statistique agricole annuelle' or ' Annuaire de statistique agricole') compiled by the French Ministry of Agriculture; detailed references are provided in the supplementary information. Numbers were manually digitized from photocopied versions of the original paper documents. Data from 1989 to 2018 were derived from digital statistics from the Agreste database ('Statistique agricole annuelle' compiled by the Service de la Statistique et de la Prospective (SSP), Secrétariat Général du Ministère de l' Agriculture, de l' Agroalimentaire et de la Forêt (MAAF), France); details are provided in the supplementary information.
Yields were calculated from total production and sown area for each department to avoid apparently often incorrect yield values printed in the old statistics books. Yields are given in kilogram per hectare (kg/ha, for sown area) for dry mass with 10-16% moisture content, depending on the crop. Data are available for ten crops: soft wheat (spring and winter separately), durum wheat, maize, oats (spring and winter), rapeseed (spring and winter), barley (spring and winter), potatoes, sugarbeet, sunflower and wine. The split into spring and winter crops eventually results in 18 distinct crop-cultivar types. Time frames with available data and the correspondence between French and English names are provided in Table  1. The shapes of French departments have changed over time. We use the 96 mainland (Metropolitan France) departments in their current form and subsume historical values to modern departments as follows.
Data records Time series length, the number of data points and outlier numbers are provided in Table  1. All results presented afterwards refer only to the filtered data set without outliers. The most complete time series are available for soft wheat, oats, barley, potato, maize and wine. National yield (area-weighted), area and production trends as aggregates over all departments are displayed in Fig. 1. Trends for the bottom and top 5% percentiles as well as the difference between them, i. e. the 90% confidence interval for expected yields, are shown in Fig. 2. All data described here are available via GFZ Data Services, under https://doi. org/10. 5880/PIK. 2021. 001 and with a CC-BY 4. 0 license 7 (see Usage Notes). There are two g-zipped tar balls, one with filtered data ("FILTERED") and one with unfiltered ("RAW") data (see Methods).
technical Validation Nationally aggregated yield time series were compared with FAO yield data, available from 1961 to 2018. Yields were aggregated from departments with area weighting. For crops with distinct spring and winter types only total yields were compared. Barley, maize, oats, potatoes, rapeseed, sugarbeet, sunflower and soft wheat were available in both data sets; the other crops are not listed by the FAO. All correlation coefficients (Pearson's r) for yield, area and production are at least 0. 99, with only five exceptions; all are above 0. 95 (Table  2 ). All correlations are significant with p < 1e-5. These high correlations indicate the subnational data are reasonable. It has to be considered, though, that FAO statistics are compiled from subnational data in France -thus the two data sets are not independent.
Acknowledgements We thank the French statistical offices in all Metropolitan departments for their support and  Tamara Ben-Ari  for additional data provision. BS acknowledges funding from the  German National Academic Foundation. TK, HK and DW thank for financial support by  JSPS Kaken  (No. 15KT0113 , in  Grant-in-Aid for Scientific Research ) and by the  2015 Fusion-H program  of  University Research Administrator (URA) office, Hokkaido University. This work is a contribution to the CLAND convergence institute funded by the  French government. PC acknowledges support from the  ANR CLAND Convergence Institute   16-CONV-0003.
2% of total fatty acids. b Sum of polyunsaturated fatty acids (PUFA) includes C18:3 n-6, and C20:3, for which the combined percentages are ≤ 0. 5% of total fatty acids. Sequence analysis. Demultiplexed raw sequences were processed through a snakemake protocol  32, 33  that uses fastx and trim galore to trim primers and perform quality control  34, 35. Another snakemake protocol based around QIIME2 was used to join and denoise sequences with Dada2  33, 36, 37. Annotation was done with Vsearch using a RESCRIPT built version of the 138. 1 SILVA database focusing on the V3-V4 region of 16S  [38] [39] [40]. Sequences were filtered to remove amplicon sequence variants (ASVs) assigned to mitochondria, chloroplast, eukaryotes, and unassigned identification (see Supplementary Table  1  for summary of per sample sequence changes).
Statistical analysis. Growth, feed utilization, whole-body composition, hepatic peroxide and antioxidants, and plasma health data were statistically analyzed using SPSS version 27 (SPSS, Chicago, IL, USA), after being validated for normality and homogeneity of variances by Shapiro-Wilk and Levene's tests, respectively. Thereafter, they were subjected to one-way analysis of variances (ANOVA). If a significant difference was identified, differences among means were compared by Tukey's HSD test. All results were considered significantly different at the level of p < 0. 05. Results are expressed as mean ± standard error (SE). The relationship between graded dietary clam meal levels and final body weight were subjected to a second-degree polynomial regression analysis using SPSS program. Permutational analysis of variance (PERMANOVA with 9999 permutations), including a posteriori pair-wise comparisons, was performed on fatty acid profiles of diet and juvenile fish.
DESeq2 was used to run indicator analysis at the genus level between the 0% and 30% diets  49, 50. ANOVA, Tukey, and Spearman microbial statistical tests were corrected for multiple testing using Benjamini-Hochberg and all p values less than 0. 05 were considered statistically significant. The snakemake, conda environment, R scripts, and R session files can be found on GitHub (https:// github. com/ aqua-omics/ clam_ meal_ study). Sequences were uploaded into the NCBI database (PRNJ Accession number: PRJNA776864). Statement for use of experimental animals. The use of experimental fish was under scientific research protocols of Florida Atlantic University and Institutional Animal Care and Use Committee (IACUC-A20-29) and complied with all relevant international animal welfare laws, guidelines, and policies.
Acknowledgements The authors are very grateful to the staff of  Aquaculture and Stock Enhancement, Harbor Branch, FAU , for their help during fish stocking, feeding, and sampling. The authors would also like to extend a special thanks to  Brandon McHenry  for his help in feed making and feeding fish during off-days. This work was supported by the  U. S. Department of Agriculture, Agricultural Research Service  by Cooperative Agreement Number  59-6034-9-007  with  Florida Atlantic University 's  Harbor Branch Oceanographic Institute. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the  U. S. Department of Agriculture (USDA). USDA  is an equal opportunity provider and employer.
Then, the presence of a referenced RSF in a sample is ascertained when the distance between the kinetic fingerprints of the sample and the referenced RSF is smaller than the resolution limit (see section 1. 7 in Supplementary Information); (ii) For different uncertainties on the {τ i }, we compute the probability that a sample is identical to each referenced RSF and assign the identity of the referenced RSF with the maximum probability to the sample (see section 1. 8 in Supplementary Information). LIGHTNING data collection. In order to explore the LIGHT-NING capability for discrimination, we used reversibly photoswitchable fluorescent proteins (RSFPs)  16  , which have been popularized by super-resolution microscopy  9. We engineered and selected a series of Skylan 17 mutants differing by the α-amino acids at positions 62, 157, and 173.
K. , Hamamatsu City, Japan) used to measure fluorescence signals and on a CMOS sensor of a camera (3060cp, iDS, Obersulm, Germany) used to control the alignment of the beams and image the illuminated sample. Triggering and synchronization of light illuminations and data acquisitions is controlled by a Labview 2010 program (National Instruments, Austin, TX). The beams of the laser diodes at λ 1 = 488 nm (LBX serie, Oxxius, Lannion, France) and λ 2 = 405 nm (06-01 Serie, Cobolt, Solna, Sweden) are first enlarged with a beam expander comprising two achromatic lenses (AC254-100-A, Thorlabs, Newton, NJ, f = 100 mm and AC254-050-A, Thorlabs, Newton, NJ f = 50 mm). A second afocal system made of two lenses (LA1213-A, Thorlabs, NJ, f = 50 mm and LA1289-A, Thorlabs, NJ f = 30 mm) is inserted in the 405 nm lightpath to correct possible chromatic aberrations of the imaging objective and improve the illumination homogeneity.
K. , Hamamatsu City, Japan) used to measure fluorescence signals while 15% of the collected fluorescence emission is focused on a CMOS sensor of a camera (3060cp, iDS, Obersulm, Germany) used to image the illuminated sample with a lens (AC254-150, f = 150 mm). Triggering and synchronization of light illuminations and data acquisitions is controlled by a Labview 2010 program (National Instruments, Austin, TX). In this system, the laser sources are triggered by a waveform generator (33612 A, Keysight Technologies) either in analog (488 nm; accepting an arbitrary 0-5 V input signal) or in digital (405 nm) mode. Acquisition of the LIGHTNING images. The second configuration of our dedicated optical setup was optimized to provide images of RSFP-labeled bacteria with a 20 μm-diameter circular field of view at 1 kHz frequency of image acquisition by fixing the illuminations at tailored light intensities (see Supplementary Figure  1b ).
The bacteria samples have been put on the metal holder, which is described above. LIGHTNING imaging requires excitation at rather high light intensities, which led us to only retain lasers for illumination in the imaging setup. Fluorescence images above 507 nm (FF-01-507/LP, Semrock) were acquired with a 50X objective (MPFLN, NA 0. 80, Olympus Corporation) and reconstructed onto the sensor of a sCMOS camera (Orca-Flash v3, Hamamatsu Photonics K. K. , Hamamatsu City, Japan) with a f = 150 mm tube lens (ACA254-150-A). Triggering of the camera acquisition was synchronized with the onset of the excitation light. Data acquisitions were controlled by HCImage (Hamamatsu Corporation).
Acknowledgements Virgile Adam ,  Ninon Zala , and  Kyprianos Hadjidemetriou  are acknowledged for their help in producing RSFPs. We also thank  David Bensimon ,  Camille Chartier ,  Jacques Fattaccioli ,  Arnaud Gautier ,  Zoher Gueroui ,  Pierre Neveu , and  Alison Tebo  for their supports and fruitful comments. IBS acknowledges integration into the  Interdisciplinary Research Institute of Grenoble (IRIG, CEA ). Funding: This work was supported by the  ANR  ( France BioImaging  - ANR-10-INBS-04  (L. J. ),  Morphoscope2-ANR-11-EQPX-0029  (L. J. ),  IPGG-ANR-10-IDEX-0001-02 PSL (L. J. ) ,  ANR-10-LABX-31  (L. J. ),  ANR-17-CE11-0047-01  (D. B. ),  ANR-19-CE11-0005 (T. L. S. ) , and  ANR-19-CE29-0003-01 (A. E. ) ), the  Fondation de la Recherche Médicale  ( FRM DEI201512440 ) (L. J. ),  Sorbonne Université  (Emergence 2019-2020, LIGHTNING) (L.
Code availability The data were collected with Labview 2010 (to acquire photochemical information) and HCImage 4. 4. 2. 7 (Hamamatsu Corporation, for imaging). The data were processed and visualized with Igor Pro 8, Mathematica 12. 3. 1. 0, Matlab 2019b, Python 3. 6, and Inkscape 1. 1. The code for processing the images of bacteria is available at https://doi. org/ 10. 5281/zenodo. 5684342.
a b s t r a c t This data set was obtained from two ROMS model simulations in the region of Brazil located at 60 °W-15 °W / 25 °S -15 °N. One of the simulations takes into account the tide (obtained from the TPXO7 product) and the other one does not. The rest of the configuration was similar for both simulations, taking bathymetry from ETOPO2 and surface forcings from COADS climatology. Moreover, all boundaries were considered open and lateral conditions were taken from SODA, while initial conditions are derived from WOA09 and the river discharge climatology was obtained from Dai and Trenberth. In both experiments the KPP parameterization was used as vertical mixing scheme. The output files are in NetCDF format and are separated by months with a frequency of daily averages, containing 12 files for the simulation with tide and 12 for the simulation without tide, which are organized in two directories: Tide and.
Data Description The "High-resolution Ocean Hydro-thermodynamics of the Brazilian Tropical Atlantic (HOH-BRAT)" dataset is a ROMS model output of daily mean hydrodynamic parameters and the TS structure of the region located at 60 °W -15 °W / 25 °S -15 °N. It is composed of 24 files of which 12 are in the "Tide " directory and the remaining 12 in the "noTide " directory, these directory names correspond to 2 ROMS model simulations, one made taking into account the tide and the other not. The names of all files are according to the standard AVG output names of the ROMS model, defined with the pattern "roms_avg_Y < Month_number >. nc " (in NetCDF format particular to the ROMS model), where Month_number varies from 1 to 12. Both simulations are performed with 1/12 °horizontal resolution, have a grid of 541 ×494 nodes, 50 vertical levels, a frequency of daily averages and each month was considered to be 30 days.
The ROMS AGRIF model is a freeware developed in FORTRAN language by  [2] [3] [4]  for numerical simulations of hydro-thermodynamics and is available at https://data-croco. ifremer. fr/CODE _ ARCHIVE/ Roms _ Agrif/Roms _ Agrif _ v3. 1. 1 _ 07 _ 07 _ 2014. tar. gz. To prepare the model input files (surface forcing, initial and boundary conditions, and river discharge), the ROMSTOOLS tool developed by  [5]  is used; it is a set of MATLAB scripts and is distributed as freeware; as an integral part of it, ROMSTOOLS also contains several scripts for visualization of the input data and numerical outputs. ROMSTOOLS is available for download at https://data-croco. ifremer. fr/UTILITIES/ Utilities _ ROMSTOOLS _ v3. 0 _ 21 _ 12 _ 2012. tar. gz ( Fig. 1  ). This dataset has stored grids in two, three and four dimensions.
A detailed description of the dimensions and parameters of the ROMS model output was made by  [6]. In the HOH-BRAT dataset, the coordinate system of the current components is different from the rest of the parameters, where lon_u and lat_u are the longitude and latitude respectively of the zonal component of the ocean current and lon_v and lat_v are the longitude and latitude respectively of   1  ), for the remaining parameters lon_rho (for longitudes) and lat_rho (for latitudes) are used. The only parameters with two dimensions are the Coriolis parameter ( f ), the bathymetry ( h ) and the mask ( mask_rho ), the latter is a binary parameter and is used to differentiate land from sea, taking the value 0 for land and value 1 for ocean. All other parameters have three or four dimensions and are described in Table  1.
In addition, it is also possible to integrate modules of disciplines depending on the hydrodynamics, like the interaction waves-mean current, sedimentology and biogeochemistry. In the present numerical experiments we employed the ROMS version designed at the 'Institut de Recherche pour le Développement' (IRD) by Patrick Marchesiello (IRD Toulouse), Pierrick Penven (IRD Brest), Gildas Cambon (IRD Brest) and Laurant Debreu (INRIA, Institut National de Recherche en Informatique et en Automatique). In it the ROMSTOOLS package  [5]  was implemented and allows any user to easily create, within the Matlab environement, the initial conditions of a given simulation and as well to analyse/ visualise its output. Furthermore, this version enables the nesting of various grids ones into the others, which allows an increase of their individual resolutions, thanks to the AGRIF library (Adaptative Grid Refinement in Fortran)  [4]  adoption.
And giveh that this numerical code was already satisfactorily implemented on the IDRIS clusters (Ada, Turing, etc. ), we chose to run our simulations on these supercomputing facilities. In  [1]  we have tested the tides' effect on the local ocean dynamics, running a series of numerical simulations with and without the input of tides ('Tidal' and 'No-tide' setups). In bothg configurations, we used a mesoscale-resolving, horizontal resolution of 1/12 °( ∼7. 3 km) and a geographical domain encompassing the whole North-eastern Brazilian coast (10 °N to 20 °S of latitude and 30 °to 60 °W of longitude). On this squared domain, we adopted 100 vertical levels, aiming at a fair representation of the physical processes happening along the water column, and test the sensitivity of the model to the study region by modifying the tuning parameters that regulate their distribution along the vertical.
Moreover, we reckon necessary to check the sensitivity of the model to other initial parameters: wind forcing and surface fluxes from climatology; different values of the slope parameter controlling the smoothness of the bottom topography and the inclination of the continental slope; a realistic or idealised stratification of the water column; the eventual activation of the diffusive component of the advection scheme, designed to reduce artificial effects of spurious diapycnal mixing that could appear in the tracers fields (T and S). In the interest of comparability, in both the ' Tidal ' and ' No-tides ' configurations the following datasets and conditions were used to create the grid file, forcings at the open boundaries and the initial state: • Bathymetry is derived from the ETOPO2 dataset  [13]  and smoothed under the constraint that: h 2 h < 0.
2 (1) with the minimum depth represented at 10 m; • At the surface, wind forcing, heat and fresh waters sources are extracted from the COADS climatology  [14]  ; • At the lateral open boundaries, an active, implicit, upstream biased radiation condition connects the model solution to the surrounding ocean  [15]  and forced with Simple Ocean Data Assimilation dataset (SODA) release 2. 1. 6; • The ocean is initially at rest, with temperature and salinity fields taken from the World Ocean Atlas 2009 dataset (WOA09) at 1 °of resolution; • For the river discharge, the climatology of  [16]  , which includes monthly means' values for the biggest South-American rivers' runoff, has been adopted; • The 9 tidal components were obtained from the TPXO7 product  [17]  , encompassing altimetry data from several satellites in order to validate the results derived from the hydrodynamic model; • The vertical mixing scheme is based on the KPP parametrization of  [18]  , while the horizontal mixing is parametrized as a linear combination of Laplacian and biharmonic mixing scaled with the grid size.
Acknowledgments TAC acknowledges the  CAPES PNPD Brazilian scholarship  ( Portaria 086/2013 ) for having supported the author's research as post-doctoral fellow at  UFPE. HLV acknowledges the  TRIATLAS  project, which has received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under grant agreement no. 817578. MA acknowledges the support of the  Brazilian Research Network on Global Climate Change -Rede CLIMA  ( FINEP grants   01. 13. 0353-00 ). All authors thank to the  International Joint Laboratory TAPIOCA (IRD-UFPE-UFRPE ).
Sense of smell is mediated by diverse families of olfactory sensing receptors, conveying important dietary information, fundamental for growth and survival. The aim of this study was to elucidate the role of the sensory olfactory pathways in the regulation of feeding behavior of carnivorous rainbow trout (RT, Oncorhynchus mykiss), from first feeding until 8 months. Compared to a commercial diet, RT fed with a total plant-based diet showed drastically altered growth performance associated with feed intake from an early stage. Exhaustive examination of an RT genome database identified three vomeronasal type 1 receptor-like (ORA), 10 vomeronasal type 2 receptor-like (OLFC) and 14 main olfactory receptor (MOR) genes, all highly expressed in sensory organs, indicating their potential functionality.
The efficiency of all qPCR reactions was 96-100% and R 2 was 0. 95-1. Data were extrapolated from standard curves and normalized to the housekeeping elongation factor 1α gene (eef1α)  [32, 33]. Relative expression of the target genes was determined by the ∆∆CT method  [34]. Mean ± standard error of mean values for each group are expressed in fold changes relative to the C diet (olfactory rosette) for all genes in Figures  2, 4  and 6, ora1a gene C diet (olfactory rosette) for all genes in Figure  2 , olfcg6 gene C diet (olfactory rosette) for all genes in Figure  4 , mor103 gene C diet (olfactory rosette) for all genes in Figure  6  and C diet for all genes in Figures  8  and 9. Accession numbers obtained and used in this study are available in NCBI and Ensembl genome browser. All gene sequences of RT used were identified by in silico.
, 2008). Meteorological model uncertainties were taken into account using stochastic schemes  (Buizza et al. , 1999; Shutts, 2005). The ECMWF ensemble is issued twice a day (0:00 and 12:00 UTC) with a forecast horizon extending up to 15 days at a six-hour time step. Forecasts are archived using a reduced Gaussian grid that is transformed to the latitude-longitude system during TIGGE extraction by bilinear interpolation  (Gaborit et al. , 2013). The database was originally stored into two spatial resolutions: 0. 25 • (in either direction) for lead times up to 10 days and 0. 50 • for lead times between 10 and 15 days. However, the declining resolution on day 10 is no longer experienced since March 2016. In this study, the resolution was reduced to 0. 1 • , which has proven to be suitable to the selected catchments  (Thiboult et al.
The latter case could also serve to determine if the use of a postprocessor may be avoided and how this compares to the use of a multimodel framework. Is a single calibrated model with reforecasts better than a multimodel approach? Since model C presented a good option, it would be interesting to determine if this system with a hydrological post-processor that corrects the models' bias would improve the forecasting performance without resorting to sophisticated precipitation post-processor techniques. Code and data availability. All tools used in this study are open to the public. The software used to build the forecasting systems is available in a GibHut repository (https://github. com/AntoineThiboult/HOOPLA). ECMWF precipitation data used in this study can be obtained freely from the TIGGE data portal (https://www.
Acknowledgements. Funding for this work was provided to the first and second authors by FloodNet, an  NSERC Canadian Strategic Network  (Grant number:  NETGP 451456-13 ), and by  NSERC   Discovery Grant   RGPIN-2020-04286. The authors thank the  Direction d'Expertise Hydrique du Québec  for providing hydrometeorological data and ECMWF for maintaining the TIGGE data portal and providing free access to archived meteorological ensemble forecasts. Special acknowledgments go to  Dr. Michael Scheuerer  for sharing the R codes of the CSGD processor and offering many insights on CSGD.
Background: Often missing from or uncertain in a biomedical data warehouse (BDW), vital status after discharge is central to the value of a BDW in medical research. The French National Mortality Database (FNMD) offers open-source nominative records of every death. Matching large-scale BDWs records with the FNMD combines multiple challenges: absence of unique common identifiers between the 2 databases, names changing over life, clerical errors, and the exponential growth of the number of comparisons to compute.