dataesr/scientific-paragraphs-categorization huggingface dataset:
Four distance matrices were constructed for each sampling date: (i) spatial distances among pairs of sampling points using the xey coordinates; (ii) distances in abiotic measures of the environment (soil moisture, bulk density, texture, pH, soil organic C, soil total N, EOC, EON, NH 4 þ , NO 3 À , and mineral P); (iii) distances in biomass of different plant functional groups (grasses, forbs and legumes); and (iv) distances in the PLFA profiles of soil microorganisms. The environmental variables were scaled to unit variance and zero mean to account for the different units of measurement. Mantel statistics were then calculated for all pairs of distance matrices using the default setting of 999 permutations in the R package 'vegan'  (Oksanen et al. , 2013).
Acknowledgments This work was funded by the  DFG   Priority Program 1374  " Infrastructure-Biodiversity-Exploratories " ( KA 1590/8-2 ). Field work permits were given by the responsible state environmental offices of  Baden-Wuerttemberg , (according to x 72 BbgNatSchG). We thank the manager of the  Swabian Alb Exploratory ,  Swen Renner , and  Jörg Hailer  for their work in maintaining the plot and project infrastructure;  Simone Pfeiffer  and  Christiane Fischer  for giving support through the central office,  Michael Owonibi  for managing the central data base, and  Markus Fischer ,  Eduard Linsenmair ,  Dominik Hessenmöller ,  Jens Nieschulze ,  Ingo Schöning ,  François Buscot ,  Ernst-Detlef Schulze ,  Wolfgang W. Weisser , and the late  Elisabeth Kalko  for their role in setting up the  Biodiversity Exploratories  project.
The research was financially supported by the National Natural Science Foundation of China (No. 31172387), the Major Science and Technology Program for Water Pollution Control and Treatment of China (No. 2012ZX07105-004), and the National S&T Supporting Program (No. 2012BAD25B08). We thank T. Pool for editorial improvements and X. Chen for technical assistance. Z. G. , J. C. , and S. L. are members of the lab EDB, part of the 'Laboratoire d'Excellence (LABEX) entitled TULIP (ANR-10-LABX-41)'. J. C. received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement number PERG08-GA-2010-276969. LITERATURE CITED Amundsen PA, Gabler HM, Staldvik FJ (1996) A new approach to graphical analysis of feeding strategy from stomach contents data -modification of the Costello (1990) method.
Acknowledgements. The research was financially supported by the National Natural Science Foundation of China (No. 31172387), the Major Science and Technology Program for Water Pollution Control and Treatment of China (No. 2012ZX07105-004), and the National S&T Supporting Program (No. 2012BAD25B08). We thank T. Pool for editorial improvements and X. Chen for technical assistance. Z. G. , J. C. , and S. L. are members of the lab EDB, part of the 'Laboratoire d'Excellence (LABEX) entitled TULIP (ANR-10-LABX-41)'. J. C. received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement number PERG08-GA-2010-276969. LITERATURE CITED Amundsen PA, Gabler HM, Staldvik FJ (1996) A new approach to graphical analysis of feeding strategy from stomach contents data -modification of the Costello (1990) method.
Nano LC MS/MS analyses All experiments were performed on a linear ion trap Fourier Transform Mass Spectrometer (FT-MS) LTQ Orbitrap Velos (Thermo Fisher Scientific, Bremen, Germany) coupled to an Ultimate s 3000 RSLC Ultra High Pressure Liquid Chromatographer (Dionex, Amsterdam, The Netherlands) as previously described  [4]. Raw data files were converted to MGF as previously described  [4]. The identification of proteins was established using MASCOT search engine (v 2. 3, Matrix Science, London, UK). The peptide and fragment masses obtained were matched automatically against IPI chicken (version 3. 81) and against the chordata section of nr NCBI database (1601319 sequences, downloaded on 2012/03/ 22) and UniprotKB SwissProt (535248 sequences, downloaded on March 2012). Enzyme specificity was set to trypsin with two missed cleavages using carbamidomethylcysteine, oxidation of methionine and N-terminal protein acetylation as variable modifications.
The tolerance of the ions was set to 5 ppm for parent and 0. 8 Da for fragment ion matches. Mascot results obtained from the target and decoy databases searches were incorporated in Scaffold 3 software (version 3. 4, Proteome Software, Portland, USA). Peptide identifications were accepted if they could be established at greater than 95. 0% probability as specified by the Peptide Prophet algorithm  [5]. Peptides were considered distinct if they differed in sequence. Protein identifications were accepted if they could be established at greater than 95. 0% probability as specified by the Protein Prophet algorithm  [6]  and contained at least one sequence-unique peptide for global inventory and at least two sequence-unique peptides for quantitative analysis. A false discovery rate was calculated as o1% at the peptide or protein level.
The abundance of identified proteins was estimated by calculating the emPAI using Scaffold 4 Q þ software (version 4. 2, Proteome Software, Portland, USA). Additionally, to perform label-free quantitative proteomic analyses based on spectral counting method, Scaffold 3 Q þ software (version 3. 4, Proteome Software, Portland, USA) was used to quantify the proteins at the three different stages of calcification. All proteins with greater than two sequence-unique peptides, identified in database with high confidence were considered for protein quantification. To eliminate quantitative ambiguity within protein groups, we ignored all the spectra matching any peptide which is shared between proteins. Thereby, quantification performed with normalized spectral counts was carried out on distinct proteins.
Data mining and bioinformatic analysis For proteomic inventory, a total of 256, 298 and 98 proteins were identified using nr NCBI database, IPI chicken database, and UniProtKb/Swiss-Prot database, respectively. For quantitative analysis, a total of 85 proteins were identified using the nr NCBI database, 92 proteins using the IPI chicken database, and 50 proteins with the UniProtKb/Swiss-Prot database. Data originating from both proteomic inventory and quantitative analyses were treated as follows. Trypsin and keratin from mammals were eliminated from the lists as they appeared to be contaminants or resulting from the digestion process. Protein sequences from the three databases (NCBI nr, UniprotKb SwissProt and IPI chicken) were aligned to eliminate all redundancies. Protein groups were determined using Clustal Omega multi-alignment algorithm  [7].
Data set analysis of avian uterine fluid proteome A total of 308 non-redundant uterine fluid protein sequences were identified. The resulting file (Supplementary Table  1 ) is made of EntrezGene Ids, GI numbers, protein symbols, short descriptions, and information on their previous identification in the shell. To discern the relative abundance of these proteins among this sample, we calculated the emPAI from the proteins identified in IPI database. This calculation was made from the proteins identified in IPI database which allows us to identify the largest number of proteins. Nevertheless, a total of 35 proteins were not reported in IPI and consequently their emPAI were not determined. Furthermore, we also mentioned in Supplementary Table  1  the proteins common with those revealed in a parallel study of the uterine fluid  [9].
This list of uterine fluid proteins was analysed to suppress redundant proteins and compared to our list. In order to determine the potential functions of the 308 uterine fluid proteins, we have determined in this list, Gene Ontology terms (GO) which are widely used for the overall interpretation of the functions of proteins in proteomic or transcriptomic studies (www. geneontology. org). A total of 4277 GO terms were extracted from the 308 non-redundant uterine fluid proteins. CateGOrizer, previously known as "GO Terms Classifications Counter", was used as a tool (http://www. animalgenome. org/tools/ catego/), to analyse GO term data sets in terms of GO classes  [10]. The 4277 GO terms were grouped in 98 different parent term categories using GoSlim2 method. Additionally, GO terms enrichments were determined using Gene set enrichment tools from Genomatix suite (www.
Quantitative dataset at the three main stages of shell calcification In a second approach, GeLC-MS/MS analyses combined to label free quantitative analysis based on spectral counting quantitative method were used to compare the respective abundance of distinct proteins at the initiation (I), rapid growth (G) and termination (T) phases of shell mineralization. A total of 96 non redundant proteins were identified. Supplementary Table  3  describes these proteins and is made of EntrezGene Ids, GI numbers, protein symbols, short descriptions, p-values, information on their previous identification in the shell, the calculated emPAI and their hierarchical ranking (reported from Supplementary Table  1 , according to the 308 proteins constituting the uterine fluid proteome and identified in IPI database).
Finally, the last three columns represent quantitative data, at the three stages of shell calcification. Moreover, one way ANOVA was performed for protein abundance in each database, in order to reveal proteins which were significantly different between the three stages of shell mineralization. Statistically significant differences were considered for p-value o0. 05. Amongst the 96 proteins, 64 showed differential abundance according to the three mineralization stages. Potential functions of the 64 proteins were examined according to the literature descriptions, data annotations and functional domain databases, with particular emphasis on three potential roles in the uterus during shell formation (involvement in mineralization, regulation of mineralization process and antimicrobial properties)  [1].
Uterine fluid samples were collected at three stages of mineralization process and in gel digested using trypsin. Resulting peptides were analyzed by LC-MS/MS and further treated using data mining and bioinformatic analysis Data source location Nouzilly, France, INRA Centre Val de Loire Data accessibility Data have been deposited to the ProteomeXchange Consortium Vizcaino et al. , Nat. Biotechnol. 32 (2014) 223-226, via the PRIDE partner repository with the dataset identifier PXD000992 [2].
Acknowledgments This research was funded by the  French National Research Agency ANR  ( ANR-13-BSV6-0007-01  and  ANR-13-BSV6-0007-02 ). The high resolution mass spectrometer was financed ( SMHART  project  35069 ) by the  European Regional Development Fund (ERDF) , the  Conseil Régional du Centre , the  French National Institute for Agricultural Research (INRA)  and the  French National Institute of Health and Medical Research (Inserm). The authors are grateful to the experimental units ( UE-PEAT ) for care of birds, and to the genotoul bioinformatics platform  Toulouse Midi-Pyrenees  for providing help and/or computing and/or storage resources. The authors thank and acknowledge  Dr. Maxwell Hincke ,  University of Ottawa  for a critical reading of the manuscript.
Uterine fluid samples were collected at three stages of mineralization process and in gel digested using trypsin. Resulting peptides were analyzed by LC-MS/MS and further treated using data mining and bioinformatic analysis Data source location Nouzilly, France, INRA Centre Val de Loire Data accessibility Data have been deposited to the ProteomeXchange Consortium Vizcaino et al. , Nat. Biotechnol. 32 (2014) 223-226, via the PRIDE partner repository with the dataset identifier PXD000992 [2] Type of data Raw and processed/analyzed mass spectrometry data obtained by nanoliquid chromatography combined to high resolution tandem mass spectrometry,. xls tables with identified/validated and quantified proteins tables How data was LC-MS/MS using a LTQ Orbitrap Velos mass spectrometer acquired Data format Raw data: raw.
2010; Sinigaglia et al. 2013 ). The Nematostella Six3/6 binding site for miR-2025 in the coding sequence (CDS) is conserved in the orthologous transcripts from the anemone species Anemonia viridis and Anthopleura japonica, despite poor overall sequence conservation of the mRNAs (Fig. 5C ). The miRNA binding sites in the Six3/6 transcripts of A. viridis and A. japonica and the HoxD transcript of M. senile were the only conserved sites we detected between Nematostella and these three species. However, this might be explained by the limited tran- scriptome data sets available and the relatively large evolutionary distance between the species studied ($250 million yr). The conservation of near-perfect binding sites for miRNAs in various species separated for several hundreds of millions of years strongly suggests that they are functional and that miRNA-mediated cleavage is also likely to take place in other cnidarians.
To further test this idea, we used the same method employed in Nematostella to predict miRNA cleavage targets in H. magnipapillata, which is separated from Nematostella by more than 600 million yr  (Erwin et al. 2011; Park et al. 2012 ). The data is currently limited; Hydra and Nematostella share only two miRNAs, their targets are not conserved, and the low coverage of the EST data set allowed prediction of only 28 putative targets for Hydra-specific miRNAs at a prediction score threshold of 2. 5. Nevertheless, we detected specific cleavage products for two of the five mRNAs tested by RLM-RACE (Supplemental Table  S4 ). This further supports the idea that miRNA-mediated target cleavage is widespread in the phylum Cnidaria.
High-throughput sequencing Size-selection, oxidation, and sequencing were performed as described  (Ghildiyal et al. 2008; Seitz et al. 2008). The resulting libraries were seeded at 2 pM on each Illumina lane (UMass Medical School Deep Sequencing Core). Read counts were normalized to the library sequencing depth, excluding abundant noncoding RNAs (rRNAs, tRNAs, and snRNAs). Abundant noncoding RNAs (rRNAs, tRNAs, snRNAs, and snoRNAs) were annotated to the N. vectensis genome using BLASTN (version 2. 2. 18) to identify homologs of (1) Drosophila melanogaster annotated rRNAs, tRNAs, snRNAs, and snoRNAs (downloaded from FlyBase: http://flybase. org/ and GenBank: http://www. ncbi. nlm. nih. gov/Genbank/ index. html), (2) the most abundant rRNA variants found in previous studies involving sequencing of fly small RNAs  (Ghildiyal et al.
2008; Seitz et al. 2008 ), (3) Homo sapiens rRNAs, snRNAs, and snoRNAs (downloaded from GenBank, and the LBME human snoRNA database: http://www-snorna. biotoul. fr/), and (4) the complete set of tRNAs found in the Genomic tRNA database (http://lowelab. ucsc. edu/GtRNAdb/). N. vectensis genomic sequences with a significant BLAST hit to those databases (E-value < 10 À4 ) were flagged as ''abundant noncoding RNAs,'' and deepsequencing reads showing perfect identity to one of those were discarded.
Transcriptome data sets We used the following data sets for all analyses. H. sapiens: All mRNA from NCBI RefSeq (accessed 01-08-2013), D. melanogaster: FlyBase release 5. 46 (FB2012_04, dated 2012-07-06), A. thaliana: TAIR 10 gene models (ftp://ftp. arabidopsis. org/home/tair/Genes/ TAIR10_genome_release/TAIR10_gff3), N. vectensis: NveGenes2. 0 (http://www. cnidariangenomes. org/). Any precursor miRNA transcripts were removed from these sequence sets by intersection with miRNA annotation prior to target search.
Comparative analysis of miRNA:target complementarity We used published human, D. melanogaster, and A. thaliana miRNAs and associated data from miRBase release 19 (Kozomara and Griffiths-Jones 2011) and the Nematostella miRNAs described here. We calculated the relative expression value for each mature small RNA based on the number of supporting reads for each, and used this to classify each sequence as either miRNA or miRNA*, with the miRNA being the dominant product from each precursor; miRNA* sequences were not further analyzed. The resulting miRNAs for each species were aligned to the corresponding transcripts (with any miRNA precursors removed) from the same species, and the alignments scored as detailed above in the target prediction method to obtain the best scoring match for each miRNA.
PCR products 250-500 bp were purified for sequencing on the HiSeq 2000 using the 50-nt read protocol (Illumina), and two biological replicates were sequenced. Degradome reads were trimmed to remove the adapter sequence and then mapped to the nonredundant transcriptome using BWA 0. 5. 9  (Li and Durbin 2009). Two data sets were generated: reads aligning to the sense strand with no mismatches  (52,301,385 and 49,642,119  reads in each library); and reads aligned to the sense strand with $6 consecutive 59 end matches, a total alignment length $30 bp, and mapping quality $20  (8,761,317 and 8,440,491 reads, respectively). For each set, we compared the starting position of degradome reads on transcript sequences with the predicted miRNA-guided cleavage sites. For all statistical analyses, we required a minimum of two reads from each library starting at a predicted cleavage site to regard it as supported by the degradome.
Acknowledgments This work was funded by a  Marie Curie IEF  fellowship and  EMBO postdoctoral  fellowship  ALTF 1096-2009  to Y. M. , FWF  P22618-B17 grant  to  U. T. , and  HFSP Career Development Award  # CDA00017/ 2010-C  and a  CNRS ATIP-Avenir  grant to H. S.
Acknowledgements We are grateful to  Keith Larson ,  Johan Nilsson ,  Andreas Nord ,  Lars Ra  ˚berg and  Martin Stjernman  for help with capturing birds. Permissions for this work were given by the  Malmo ¨/Lund Ethical Committee  for Scientific work on animals (no. M172-06  and  M204-06 ), the  Swedish Nature Protection Agency and the Swedish Ringing Centre  to SA ˚. Biology Open  to the  Center for Animal Movement Research at Lund University (CAnMove). Financial support came also from the grant  OTKA K-105054  ( Full-Sky Imaging Polarimetry  to  Detect Clouds  and to Study the  Meteorological Conditions Favourable for Polarimetric Viking Navigation ) received from the  Hungarian Science Foundation  by GH, who also thanks the  German Alexander von Humboldt Foundation  for an equipment donation and a three-month research fellowship (3.
, 2013; Troupin et al. , 2010)  do not provide sufficiently detailed or sufficiently reliable mapping at the intermediate water levels. This is particularly critical for mapping of the MW spreading near the Iberian Peninsula. The main source of the data is the World Ocean Database freely provided by the National Oceanographic Data Center (http://www. nodc. noaa. gov/). The data cover the period from 1950 to 2012 and include OSD (bottle, XCTD, low-resolution CTD casts), CTD (high-resolution CTD casts) and PFL (mainly ARGO float casts) instrument types. All data underwent a rigorous quality control, as well as preliminary filtering for the instrumental and eddy related noise. The gridding is performed along 53 neutral density surfaces (Jackett and McDougall, 1997) using Barnes' Optimum Interpolation technique  (Barnes, 1964).
Following the data density distribution, the radius of the Gaussian gridding function decreases towards the Iberian coast. The shape of the gridding function takes into account the influence of bottom topography on the characteristics of oceanic flows. The results are further interpolated into fixed depth levels down to 2000 m depth. The results suggest that the MEDTRANS climatology brings significantly more details of the distribution of the temperature-salinity structures related to the MW spreading in the Atlantic, of the southeastward penetration of the subpolar/polar water along the eastern slope of the Azores plateau and the King's trough/rise, and of the northwards penetration of the Antarctic Intermediate Water along the African coast. The temperature-salinity maps were used for computation of the geostrophic currents in the upper layer referenced to 1900-m level, revealing several features, all in close correspondence with the ocean circulation theory (Cushman-Rosin, 2010): (i) periodic intensification of the Azores Current (AzC) as it turns south in the quasi-stationary meanders; (ii) inflows and outflows from/to the AzC as it meanders; (iii) strong recirculation of the AzC west of Cruiser-Great Meteor seamounts.
Deployment of devices Between 2003 and 2009 a total of 79 elephant seals Mirounga leonina (42 post-moult adult female and 37 post-moult juvenile males) from the Kerguelen Islands were equipped with conductivity-temperaturedepth satellite-relayed data loggers (CTD-SRDLs, Sea Mammal Research Unit, University of St Andrews). Seals were caught with a canvas head-bag and then anaesthetized with a 1:1 combination of tiletamine and zolazepam (Zoletil 100 0. 8 mg/100 kg) injected intravenously  (McMahon et al. 2000). Data loggers were attached on the head of seals using a 2component epoxy (Araldite AW 2110) after cleaning the hair with acetone  (Bailleul et al. 2007a ). All seals were captured between December and February, before their post-moult foraging trip, except for one animal that was equipped in September during a brief winter visit.
CTD-SRDL features The CTD-SRLD devices provided Argos locations, diving behaviour and high resolution CTD data (see  Fedak et al. 2001 for details). Between 4 and 14 locations per day were determined during satellite uplinks by the Argos system. Given the limited bandwidth available in Argos transmission, temperature sampled every 4 s was compressed into a single temperature-depth profile every 6 h by summarizing the dive into 12 inflections using a broken stick algorithm (see  Fedak et al. 2001  for details). The temperature resolution was ± 0. 01°C, and the dive data accuracy was ± 3 m at 100 m, decreasing to ± 24 m at >1400 m (for details see  Fedak et al. 2001). Depth was recorded on 5 channels (i. e. 5 levels of granularity); the first operated from 0 to 99 m with an accuracy of ± 3 m, the second operated from 99 to ~290 m with an accuracy of ± 6 m, the third operated from ~290 to 700 m with an accuracy of ±12 m, the fourth operated from 700 to 1400 m with an accuracy of ± 24 m and the fifth operated from 1400 to 2000 m with an accuracy of ± 48 m.
Path analysis Raw Argos data contained locations of varying uncertainty. We fit firstdifference correlated random walk switching state-space models (DCRWS SSM) to the raw location data  (Jonsen et al. 2005)  using the freely available software WinBUGS (www. mrc-bsu. cam. ac. uk/bugs/ winbugs. contents. shtml) and the R package R2WinBUGS  (Sturtz et al. 2005). Unlike ad hoc filters that remove uncertain locations  (Austin et al. 2003) , DCRWS SSM track estimations retain all locations, but account for the uncertainty in the data  (Jonsen et al. 2005). Importantly, SSMs also fit multiple random walks to animal movement paths con sisting of ordered sets of step lengths and turning angles to infer switches between 2 behavioural states. Rapid and directional movement was interpreted as transit behaviour between haul-out sites and habitat patches, as well as between habitat patches.
In contrast, comparatively slow and contorted movement was considered indicative of ARS be haviour  (Mo ra les et al. 2004). ARS behaviour (search) is considered a useful proxy for foraging behaviour and, consequently, was used to identify likely habitat patches along the seal's track  (Biuw et al. 2003 , Thums et al. 2008). Conversely, transit behaviour was provisionally regarded as movement between habitat patches. To fit the SSM to each individual seal location dataset, 2 Mar kov Chain Monte Carlo (MCMC) chains of 30000 iterations were run with a burn-in of 20000. Each chain was thinned so that 1 in every 5 samples was retained, for a final MCMC sample size of 4000. The model fit provided location points at 6 h intervals along the movement path. At-sea movement at the population level Most at-sea activity occurred on the northern plateau between the Kerguelen and Heard Islands (Fig.