dataesr/scientific-paragraphs-categorization huggingface dataset:
In practice, photographs were not available for the whole area in 1979, so the IGN used photographs taken between 1975 and 1985, meaning the date of the aerial photos used differs from one massif to the other. Aerial photos of the Massif du Mont-Blanc were taken in 1979  (Berthier and others, 2006; Berthier and Vincent, 2012)  but for the other massifs, this information is no longer available. The comparison of the glacier surface elevation on the DEM with available topographical measurements of Glacier d'Argentière in 1979 (on the same profiles presented in Fig. 2  but with fewer points than in recent years, i. e. 25 points in total, reason why the 1979 data are not plotted) showed an average difference of 1. 3 m (4 m in absolute values), confirming the date of the aerial photos used. For Glacier de Saint-Sorlin, in the Massif des Grandes Rousses, the difference reached 14 m (both the raw difference and the difference in absolute values) and the glacier surface elevation on the DEM was systematically lower than the one identified from the topographical measurements.
Regarding the causes of the variability of the end-ofsummer SLA in space and over time,  Rabatel and others (2013b)  showed that the mean end-of-summer SLA over the study period of each glacier is strongly controlled by its morpho-topographic variables, namely its average altitude, surface area, latitude and aspect. On the other hand, the interannual variability of the mean end-of-summer SLA of the glaciers studied is strongly controlled by climate variables. Indeed, the observed increasing trend in the mean end-of-summer SLA was mainly driven by increasing temperatures, and the nonlinear low-frequency fluctuations in the mean end-of-summer SLA time series were significantly correlated with winter precipitation anomalies. 1979-2011 and 2003-11    1979-2011. The mean annual glacier-wide mass balance of Glacier d'Argentière (Mer de Glace) derived from satellite data was -0.
68 m w. e. a -1 found for nine glaciers throughout the Alps for the period 1983-2008 using WGMS data  (Huss, 2012). Using all the available data in the WGMS database,  Zemp and others (2015)  also concluded that the last decades of the 20th century and the first decade of the 21st century were the most negative since the beginning of observational records, with mean annual mass-balance values ranging from -0. 5 to -0. 8 m w. e. a -1 ; values in line with our estimate.
31 ± 0. 20 m w. e. a -1 and 0. 33 ± 0. 27 m w. e. a -1 , respectively. These results confirm the efficiency of our method using only remote-sensing data to quantify the glacier-wide mass balance. Considering the results in more detail, it will be seen that in some years the difference between the two methods exceeded 0. 5 m w. e. a -1. For Glacier d'Argentière, this was the case in  1995, 2002, 2003 and 2013. For the first 3 a, the remote-sensing mass balances were too positive compared with the GLACIOCLIM values. This could be due to the low position of the end-of-summer SLA and the fact images were acquired before the end of the ablation season (on 31, 18 and 23 August, respectively). For the year 2013, the remote-sensing mass balance was too negative compared with the GLACIOCLIM value (a difference of 0.
7 m w. e. ), but the image was acquired on 1 September, so this difference cannot be explained by the date of the satellite image. For Glacier de Saint-Sorlin, differences between the two methods were >0. 5 m w. e. a -1 for 5 a  (1985, 1994, 2001, 2008 and 2010). In each one of these cases, the remote-sensing mass balance was too positive (or not sufficiently negative), but in all these years the images were acquired between 24 August and 23 September, i. e. not early enough in the season to explain the low end-of-summer SLA values. Figure  6  shows the glacier-wide annual mass-balance time series for the 30 glaciers in the French Alps for the period 1983-2014. Considering annual data for the entire set of glaciers and the entire study period  [Fig. 6(a) ] it is noteworthy that the standard deviation of the values for each year was almost constant over the entire period (±0.
ACKNOWLEDGEMENTS First of all, the authors wish to pay tribute to Louis Reynaud who died in February 2016, to his investment on glaciers' monitoring, particularly in the French Alps, and his implication in the development of the method presented in this study. This study was conducted in the framework of the French glacier observatory GLACIOCLIM (http://www-lgge. ujf-grenoble. fr/ServiceObs/index. htm). This work was made possible thanks to the  Labex OSUG@2020 (Investissements d'avenir - ANR10 LABX56 );  USGS-EDC ,  NASA/METI/AIST/.
Full Genome Assembly The data yield by the 454 Roche pyrosequencing were trimmed (1,139,478 trimmed reads) and assembled using both Mira assembler v3. 2  (Chevreux et al. , 2004)  and the Newbler 2. 8 software (Roche, 454 Life Sciences). To reduce the dataset, the contigs were combined together by Cisa  (Lin and Liao, 2013). Scaffolding was improved using the Opera software v1. 2  (Gao et al. , 2011) and GapFiller V1. 10 (Boetzer and Pirovano, 2012). The repeats, such as rRNA operons, were checked and compared with the current available genome of C. koseri ATCC BAA-895 using CLC Genomics software v4. 7. 2 (CLC bio, Aarhus, Denmark). The genome assembly was improved with manual refinements using the SOLiD run (about 1. 5 million of trimmed reads) mapped onto the final assembly. The remaining scaffolds were ordered using the C.
koseri ATCC BAA-895 reference genome and the MAUVE program version 2. 3. 1  (Darling et al. , 2004). The seventeen gaps between the remaining ordered scaffolds were filled by PCR and several designed primer sets  (Untergasser et al. , 2012). The circular nature of the plasmids was checked by PCR and by a local python script. The PCR products were sequenced by the Sanger capillarity ABI, and both assembly and sequence integrations were performed using the CAP3 assembler  (Huang and Madan, 1999). Finally, five scaffolds were obtained (one bacterial Chromosome and four plasmids). The Genome coverage was 30X using trimmed reads. The Citrobacter koseri strain URMITE (CKU) genome has been deposited at the NCBI under the accession number PRJEB6512.
Genome Annotation The GC-skew diagram was obtained using a GenSkew executable jar file (http://genskew. csb. univie. ac. at). The ORFs were predicted by Prodigal version 2. 50  (Hyatt et al. , 2010). The ORFs were annotated using BlastP algorithm similarity search against the NCBI Nr database  (Benson et al. , 2013)  and using Rps-Blast against the Pfam database  (Punta et al. , 2012)  of position-specific scoring matrices (PSSM) with E < 10 -5. The functional Clusters of Orthologous Groups (COG) were also identified by Rps-Blast against the COG database  (Tatusov et al. , 2001)  of PSSM with E < 10 -5. The ribosomal rRNA and tRNA were predicted by the RNAmmer 1. 2 server  (Lagesen et al. , 2007)  and ARAGORN  (Laslett and Canback, 2004). The prophage identification was realized by PHAST  (Zhou et al.
Comparative Genomics The genomes used for the genomic comparison with CKU were retrieved from the ftp genome project repertoire at the NCBI (https://www. ncbi. nlm. nih. gov/Ftp/). The comparative genomics of the Citrobacter species were performed by Get_homologs (Contreras-Moreira and Vinuesa, 2013) to identify the core/pangenome using the recommendations of Tettelin and collaborators  (Lagesen et al. , 2007). The software uses the principle of bidirectional best hit, COGsoft  (Kristensen et al. , 2010)  and OrthoMCL v1. 4 algorithm  (Li et al. , 2003). The strain-specific genes are defined as the genes found in only one species and not in all the other Citrobacter species of the study. The strain-specific genes were identified by interrogating the pangenome matrix built by Get_homologs and after removing split genes.
Phylogenetic Analysis of Single Nucleotide Polymorphisms The kSNP3 program  (Gardner et al. , 2015)  was used to identify pan/core-genome Single Nucleotide Polymorphisms (SNPs) in the set of Citrobacter genomes, and to estimate phylogenetic tree based upon those SNPs. The same method was performed on the pCitro2 plasmid with its best blast hits against nr and Whole Genome Shotgun (WGS) databases at the NCBI. The optimal value of K-mer size was obtained by Kchooser of the kSNP3 package. The kSNP3 was performed using the default parameter with the core and the maximum likelihood tree options. The branch lengths are expressed in terms of changes per number of SNPs.
Phylogenetic Analysis of the lpxL Gene and Omptin Family The Y. pseudotuberculosis lpxL gene was blasted against the Nr database. The sequences of the first 50 best hits were retrieved, aligned using MAFFT program  (Katoh and Standley, 2013)  and curated by Trimal  (Capella-Gutierrez et al. , 2009). The phylogenetic tree was inferred using MEGA 5  (Gardner et al. , 2015)  with the maximum likelihood algorithm, bootstrap resampling (500 replicates) and the Kimura2 model. The protein sequences of the omptin family were retrieved from the Pfam web site (http://pfam. sanger. ac. uk/); the same method was applied with the JTT correction for amino acids.
Phylogenetic Analysis of pCitro1 and Strain-Specific Genes Each of the all predicted proteins was used as seed for BlastP against Nr database. For each protein, the hits that exhibited a significant match (E < 10 -4 and coverage >70%) were selected. Single protein datasets were aligned by MAFFT and trimmed by trimal. One hundred datasets were produced from each dataset by bootstrap resampling using Seqboot in the PHYLIP suite version 3. 6 (Felsenstein, J. 2005). Phylogenetic analyses were conducted using the maximum likelihood method with PHYML  (Guindon and Gascuel, 2003)  and the WAG model  (Whelan and Goldman, 2001). The consensus function in PHYLIP was used to construct consensus trees with extended majority rule.
pestis, although the colonies from the latter were slightly smaller (Figure  S2A ). However, the phenotypic profile of our isolate showed major discrepancies with that of Y. pestis (Figure  S2B ). Contrary to Y. pestis, our isolate was positive for ONPG and showed the ability to ferment lactose and to use Citrate. Using the BioMerieux database, the biochemical profile of our isolate is finally associated to a Citrobacter koseri/amalonaticus rather than to a Y. pestis. In parallel, the subcutaneous infection of four Balb/c mice with the isolate strain did not produce any pathogenic effects after 2 weeks. Within these contradictions, we carried out the whole genome sequencing of our isolate named Citrobacter koseri strain URMITE (CKU). Citrobacter koseri, a gram-negative facultative anaerobic bacterium, is an environmental organism commonly found in human and animal gut microbiota, as well as in soil or water  (Lin et al.
The objective was to examine changes in temporal trends in suicide mortality in 26 Western countries by retrospective trend analysis of the WHO mortality database on causes of deaths. From 1990 to 2010, there was a median reduction in suicide mortality of 22. 7%, ranging from a 46% reduction in Estonia to a 26. 2% increase in Romania. Suicide mortality decreased by ≥20% in 15 countries, and the reduction tended to be greater in countries with higher mortality in 1990. In most of the central European countries mortality strongly declined. The median changes in the age groups were -25. 3% (range -62. 9% to 72. 6%) in people aged 15-24 years, -36. 9% (-60. 5% to 32. 4%) in 25-34 years, -3. 6% (-57. 1% to 92%) in 35-54 years, -12. 2% (-37% to 65,7%) in 55-74 years and -16. 1% (-54. 5% to 166. 7%) in ≥75 years.
Twenty-eight countries today are known to have national suicide prevention strategies, while World Suicide Prevention Day, organized by the International Association for Suicide Prevention, is observed worldwide on 10 September each year 1. To be effective, these policies need to rely on exhaustive and accurate deaths rates, in order to target specific populations and assess their effectiveness as well as the impact of social context 6. To help countries monitoring their death rates and carrying effective public health programs, the WHO created an international mortality database, with all-causes mortality data from 1979 to 2010. The WHO mortality database is a compilation of mortality data by age, sex and cause of death, freely available at http://apps. who. int/ healthinfo/statistics/mortality/whodpms/.
Methods Suicide deaths registered in the World Health Organization mortality database at June 2015 were extracted for European countries and USA  7. The quality of mortality data has been evaluated by the WHO  8. Data quality is checked annually according to the Health Facility Data Quality Report Card (DQRC). The DQRC examines completeness of reporting; internal consistency of reported data; external consistency of population data and external consistency of coverage rates (for more details see http://www. who. int/healthinfo/DQRC_Indicators. pdf). A return to the data collectors on the quality of their entered data is sent each year. Albania, Luxembourg, Monaco, Iceland and Malta were excluded for missing data. For Slovakia data was only available since 1992, for Denmark since 1994, for Switzerland since 1995, for Serbia since 1998, for Cyprus since 1999.
There were not included in our analyses. For almost all other countries, data up to 2010 were available. We used Group-based trajectory modeling  9, 10  to identify distinctive group of trajectories based on the assumption that the population is composed of mixture of distinct groups defined by their developmental trajectories, while recognizing uncertainty in group membership. Using SAS proc traj  9, 10  , we model pattern of change over time in the suicide mortality rate of 26 countries (dependent variable) between 1990 and 2010. Comparisons were made between models allowing for varying numbers of trajectory groups, as well as models including time as linear, quadratic, and cubic, using the Bayesian (BIC) information criteria. More precisely, we use the BIC log Bayes factor approximation 2 log(Bij) ≈ 2*(BIC i -BIC j ) -the BIC of the more complex (large number of group, or higher order equation) less the BIC of the null (simpler) model -to select the model that better fits the data.
Acknowledgements We thank  Dr Laurent ARNAUD , hôpital  La Pitié-Salpêtrière, Paris, France. This work was supported by  AP-HP  ( Assistance Publique des Hôpitaux de Paris ),  Fondation FondaMental  ( RTRS Santé Mentale ) http:// www. fondation-fondamental. org/, by programme  Investissements d' Avenir  under the references  ANR-11-IDEX-0004-02  et  ANR-10-COHO-10-01 , and by  INSERM  ( Institut National de la Santé et de la Recherche Médicale ).
Population Size History Inference Using the PSMC Effective population sizes in units of 4N e l g were estimated backward in time (in units of 2l y T) based on individual whole-genome sequences under the PSMC model using the program psmc (version 0. 6. 4) program  (Li and Durbin 2011). Briefly, the analyzed individual whole-genome sequences were obtained from the above described mpileup files (aligned onto the UMD 3. 1 cattle assembly) that were processed (individually) with bcftools (version 0. 1. 19) (Li and Durbin 2009)  using the -c option. The resulting vcf files were further converted into fastq files using the vcf2fq utility of the vcfutils. pl program available in the samtools (version 0. 1. 19) suite  (Li and Durbin 2009)  discarding sites located less than five nucleotides from an indel (-l 5 option) and covered by less than 3 or more than 30 reads (-d 3 -D 30 options).
As originally described, the psmc program was finally run with default options except for the pattern of atomic time intervals (-p option) that were set to "4 þ 25*2 þ 4 þ 6" on a reduced version of the (autosomal) genomes (psmcfa files), with consecutive sites grouped into consecutive bins of 100 nucleotides marked as "K" (at least one heterozygote), "T" (no heterozygote sites), or "N" (less than 90 called sites).
py (version 1. 2) for the IM-model, from the IMCoalHMM software package (https://github. com/mailund/IMCoalHMM). More precisely, the parameters underlying the I-model include the split time s D (in units of l y T) and also the ancestral effective population size (in scaled units of h ¼ 4N e l g ) and the recombination rate q (in scaled units of l g ) that both parameterize the underlying coalescent process. The parameters underlying the IM-model include the times s 1 (completion of the split) and the IM period length s M that both are units of l y T, the migration rate M (in number of migration per substitution) and h and q (as for the I-model). Note that the product M Â ð h=2Þ corresponds to the estimated number of migrant per generation (in the usual units of 2N e m, where m is the net migration rate) over the IM period.
Detection of Genes under Selection Based on the alignment of the BBO_3569 and BBO_3574 individual sequences onto the bovine UMD 3. 1 reference genome assembly (mpileup file described above), a consensus BBO sequence was derived for all the n ¼ 22,091 (bovine) Wisent Demographic and Adaptive Histories. doi:10. 1093/molbev/msw144 MBE ENSEMBL protein-coding sequences as defined in the UCSC genome database (http://genome. ucsc. edu/). At a given position, the retained consensus base corresponded to the most represented one among all the aligned reads from both the BBO_3569 and BBO_3574 individuals and after discarding bases with a BAQ < 30. Positions with a read depth DP < 3 and DP > 60 (three times the average coverage of BTA genome assembly by the combined BBO_3569 and BBO_3574 read sequencing data) were treated as N (not called) in the consensus sequences.
Functional Annotation of Candidate Genes under Positive Selection and Gene Network Analysis Among the 17,073 Ensembl transcripts ID corresponding to the protein coding sequences for which a K a K s ratio was estimated, 873 transcripts with a K a K s ratio above one were considered under positive selection. Functional annotation of these transcripts and networks analyses were carried out with the Ingenuity Pathway Analysis software (IngenuitySystems, www. ingenuity. com). Among the 873 transcripts with a K a K s ratio above one, 481 transcripts ID were mapped to the Ingenuity Pathway Knowledge Base (IPKB) and were representative of 425 individual genes ready for analysis (supplementary table  S3 , Supplementary Material online). A total of 405 of these transcripts were further identified as one to one orthologs between cattle and sheep, using Ensembl orthology information (http://www.
Acknowledgments The authors wish to thank  Anna Madeyska-Lewandowska  ( Private Veterinary School, Warsaw, Poland ) that provided the Bison bonasus samples in the early 1990s as part of a collaborative research project on the characterization of casein variants. They are also grateful to the four anonymous reviewers for their very helpful and constructive comments. This work was supported by an  Institut National de la Recherche Agronomique  ( INRA)-Animal Genetics Department Grant  ( AFROSEQ  project).
Researchers and Academic Social Networks: Potentials and Questions The majority (60%) of our respondents have heard about academic social networks although only 42% use them. They usually choose ResearchGate (by far the most popular (65%)), and then Academia. edu (24%). Other academic tools like Mendeley were mentioned by 2. 1% and 1. 9% for Google Scholar, with a long tail of other networks following behind. Since no predefined list was used, these answers suggest that some researchers cannot always precisely identify what an academic social network is. They may see them as The 58% who do not use academic social networks are primarily not convinced of their usefulness ('unuseful' for 41%), don't have time to use them or feel they lack information about them (14%). Some find academic social networks have a too limited offer of services and 4% fear they would lose control over their professional and personal data by engaging with them.
Academic social networks are mainly used in community contexts. A third of the respondents mentioned using them to interact with other communities, shop for new ideas and to stay up-to-date with other subjects other than their core themes of research. This practice seems quite widespread in the Life Sciences (59%) and in the Arts & Humanities (46%) whereas in Mathematics/ Computer Sciences (19%) academic social networks is first and foremost an 'internal' channel for research exchanges. The main use researchers assign to networking is to disseminate their publications (70%), especially in the Life Sciences and Social Sciences. We can also observe that sharing data is not negligible, particularly in the Arts & Humanities reporting more than 50%. It is somewhat paradoxical that researchers in this discipline share data via these networks as only 18% admit to knowing about the site's data-sharing policies.
g. PhD candidates) in research circles. They are first and foremost expected to boost the visibility of research works on the Internet. They are then appreciated as Liber Quarterly Volume 25 Issue 3 2016 collaborative tools. These results qualify the views an analyst of an American media consulting firm expressed about these networks: 'These are tools that people are using to raise their profiles and become more discoverable, not community tools of social interaction. ' (Van Noorden, 2014, p. 127) On a darker note, they have general terms of use where the privatization of data is a possibility, and for some they remain complicated to use and insufficiently secure for data storage.
These answers encompass HAL itself and the sub-portals it has created at the request of many research organisations and universities. ArXiv is the second most important repository cited (19%), although it is impossible here to determine whether ArXiV is directly used by researchers or rather more indirectly using HAL to push data to it. 10% of all responses mention institutional repositories (DSpace, E-Prints, Flora, home-grown software …) and some subject-based archives such as RePEc. This picture reflects the dominance of HAL, as a specific French answer to enhance Open Access. Open Access deposit also relates to what is the norm in scientific communities. It is Mathematics and Computer Science communities who particularly engage in depositing their outputs in Open Access as do the Physical Sciences (Figure  3 ).
In the Life Sciences, the rate of deposits is quite low (11%), compared with the high rate of respondents in this discipline who know about Open Access (76%). This is despite the fact that pushing publications to PubMed is easy via HAL. There are two explanations for this. Many medical journals have agreed to push publications to PubMed without researchers' intervention  (Creaser et al. , 2010, p. 155). Similarly, an agreement has been reached between some research institutions and the open access publisher BioMed Central, who automatically sends researcher metadata to HAL where the institution has a HAL sub-portal (Prime-Claverie & Mahé, 2013, 5 th part). These automatic workflows make deposits effortless for Life Sciences researchers. A more essential explanation lies in the importance of peer-review in the Life Sciences, which is considered a crucial part of the academic workflow.
This concurs with these disciplinary practices (see above) where some know about APCs and pay them. Mentioning Open Access journals brought up many critical remarks about publishers' position in the academic workflow with, again, a confusion between Gold and Hybrid. Some comments also expressed worries about the scientific quality of these journals. Beyond sharing publications, sharing data is becoming a relevant issue for researchers. Asked about the storage of their data, 83% of respondents keep them on their hard drive, and 40% use this as their sole storage solution. 22% mention data sharing sites (few cited sites: ArXiV, HAL, Github …); It was the Social Sciences and Arts & Humanities who mentioned sharing their data most (at around 20%).