dataesr/scientific-paragraphs-categorization huggingface dataset:
The SNIIRAM contains exhaustive individual medical detailed data from different sources: reimbursement data (doctors and other health professionals visits, prescribed drugs, medical devices); socalled ''long-term diseases'' (serious chronic diseases exempt from co-payments and user fees); hospital discharge records, including for each hospitalization principal and associated diagnoses, medical and technical procedures. Table  2  shows the main data extracted from the SNIIRAM database. Finally, vital status and causes of death are obtained from the National Death Registry-CepiDc  [30].
Each month the data exported from the site and imported in the CON-STANCES database are sorted out from the database to perform quality controls. When discrepant data are encountered, the monitor identifies the origin of the discrepancy and the concerned data have to be corrected and reintegrated into the CONSTANCES database. For each category of data, the monitor identifies the source document (where the data has been recorded for the first time) and verifies the consistency between the site data and the CONSTANCES database extraction. Finally, we perform a permanent statistical monitoring of the inter-operator and inter-site variability. As medical data extracted from the national databases are not always accurate, particular attention is paid to validation of the diagnoses extracted from the health-related administrative databases, which are routinely verified using a specific procedure.
Potential cases are first identified from the available sources: self-reported diseases in the annual follow-up questionnaire, diagnoses extracted from the SNIIRAM database (''long-term diseases'', hospital diagnoses). Participants having given a specific consent for being contacted or for contacting their doctor or hospital (97. 4 % of the participants gave such a consent) are then contacted by telephone. A short questionnaire allows for confirming that the person really reported a disease and for collecting additional data (date of the occurrence of the disease for instance); the participant is asked to send medical documents allowing for collecting specific data and for diagnosis validation (pathology report, electrocardiogram…). When the subject cannot be reached or when he/she cannot provide the documents, the hospital or the general practitioner is contacted.
Data management CONSTANCES' data are centralized in a unique database stored in a highly secured environment. We describe here the main features of the data management process. The first step is sampling of the eligible people within the CNAV database (see below); after sampling, identification data (names, postal addresses, telephone numbers) are encrypted and kept by a thrusted third party, an independent organization in charge of sending the invitations to participate to CONSTANCES; thus only a study number is stored in the central CONSTANCES database. After the inclusion of the participants in the study medical centers, several data streams (where the participants are identified by their study number) coming from multiple sources feed regularly the database using different media: paper questionnaires are computerized; data from the study centers are sent to the CONSTANCES database through internet or by postal mail depending on their nature; data extracted from the administrative databases are encrypted and sent to CONSTANCES by internet by CNAMTS and CNAV; inclusion questionnaires are completed on paper, while for follow-up questionnaires participants can chose between paper or internet using a specific internet platform.
Consent forms are stored by the thrusted third party. For the validation of the diagnoses, as it implies a direct contact with the participants and/or their doctor or hospital, the thrusted third party send the contact data (names, telephone, consent forms) to an external telephone platform. The CONSTANCES database itself is divided into two parts. Raw data from the different sources are stored in a working database where different controls and data cleaning are performed and the raw data are archived. Cleaned data are then transferred into the study database, which is used for research. Safety is ensured by various provisions (encryption of data, passwords, regular back-ups…). Encrypted data are transferred to external researchers for their studies by internet; data potentially allowing identification of the participants are impoverished (zip code instead of address, age group instead of date of birth, etc.
Periodicity of follow-up The periodicity of follow-up varies according to the sources. A self-administered mail questionnaire is sent annually, thus allowing close follow-up, by collecting numerous data without asking subjects for too much work each year. At the same time, it will facilitate rapid response for setting up new studies and establish a sense of loyalty in the participants; too long a delay between two questionnaires is a factor that promotes dropping out  [52]. Some data will be collected annually (health status and reported morbidity, life events and characteristics of place of residence, smoking, alcohol, etc. ), while others will be collected at longer intervals, according to a planned calendar (health scales and questionnaires for a specific health area or specific risk factors).
On the other hand, the problem of attrition during follow-up may cause substantial bias if the probability of continued follow-up is different in exposed and unexposed subjects or in those who do or do not become ill, as it is often the case  [57]. For descriptive studies of the frequency of health problems and exposures, the parameters of interest must be estimated in a representative sample of the target population. In this regard, the potential concerns for CON-STANCES are mainly incomplete geographical coverage of the recruitment centers and factors associated with voluntary participation. We have verified that the structure of the population of the ''departements'' where the CON-STANCES HSCs are located is essentially identical to that for France as a whole for the principal demographic, social, and occupational characteristics; we should thus be able to generalize the CONSTANCES results to the French population (data not shown).
Using volunteer subjects inevitably produces selection effects, even in studies that use random drawing from an appropriate sampling base, as it is the case of CON-STANCES, as eligible individuals may refuse participation (become non-participants), a potential source of bias. To compensate, researchers usually attempt to collect a minimum data set for the non-participants (mainly age, sex, and social category), to facilitate subsequent adjustments for estimating the relevant parameters. This approach nonetheless has some limitations. First, it is not always possible to collect the adjustment data for non-participating subjects. Nor is it always clear whether these data are sufficient to control for potential bias, because we know, for example, that within the same socioeconomic category there are many important differences in terms of health, behavior, lifestyles, social networks, etc.
[58, 59]. Finally, it is rarely possible to control completely for potential selection bias because it is rare to have the relevant data collected simultaneously for the participants and the nonparticipants. To obtain a representative sample of the target population and to minimize the biases associated with selection effects at inclusion and during follow-up in CON-STANCES, we took the following steps. The sampling base at inclusion is composed of all persons aged 18-69 years and covered by CNAMTS in the catchment areas of the CONSTANCES HSCs. Sampling is done within the CNAV database which includes exhaustively all the persons in France affiliated to the CNAMTS. The random drawing is stratified according to unequal inclusion probabilities, based on data from participation in previous surveys involving invitations to HSCs  [60].
We also drawn a ''parallel cohort'' from a random sample of 400,000 nonparticipants for whom we prospectively collect data from the same national databases than for the participants: social and occupational characteristics (sex, age, work status, occupation, social category), through the CNAV database, health and health-care utilization from the SNIIRAM and the National Death Registry. Auxiliary data extracted from CNAV and SNIIRAM cover three years before inception for both the participants and the sample of non-participants. We are thus able to estimate the probabilities of participation in CONSTANCES associated with sociodemographic and health variables using logistic regression models, to compute weights for correcting unit nonresponse and to estimate adjusted prevalence of questionnaire variables.
As in epidemiological surveys auxiliary health and social data are usually not available for nonrespondents, this approach has rarely been used to correct the prevalence estimates for nonresponse bias, with few exceptions which proved to effectively correct for nonresponse  [61]. A major concern of long-term prospective cohorts is attrition, potentially inducing biases and affecting the power of the study  [52]. We can assume that almost none of the people included in CONSTANCES will be permanently lost to follow-up, since the participants will be followed passively through the SNIIRAM, CNAV and National Death Registry files. There will nonetheless be attrition due to the failure to return the annual questionnaire. Coefficients of adjustment for attrition are calculated by a method similar to the one used to calculate the coefficient of adjustment for initial non-participation based on the data collected at inclusion for participants as well as the SNIIRAM and CNAV data.
Advancement After a field pilot during a four to five-month period in seven centers, including about 3500 subjects  [62] , the recruitment started in late 2012. Currently (September 2015), more than 82,000 participants are included in the cohort. The participation rate to the annual follow-up questionnaire of subjects who were included in 2012, 2013 and 2014 was higher than 80 % each year. The preliminary analysis of the available data showed that this sample is close to the general population of adults in France regarding the main socioeconomic variables (Table  3 ). There was quite a diverse distribution of occupations and working conditions, lifestyle factors, and the prevalence rates of various diseases and symptoms were close to those from other available French surveys (data not shown).
We also verified that the use of auxiliary data from national databases on both respondents and non-respondents for correcting unit nonresponse and to estimate adjusted prevalence of questionnaire variables was efficient. As shown in Table  4 , reweighting techniques used in a previous work  [61]  proved to meaningfully improve the estimates of prevalence in the population of different conditions related to health and behaviors. When adjusting only for stratification variables (age, sex and socioeconomic status), changes were observed in the expected direction reflecting the classical underrepresentation of people with a poor health status (low self-rated health, alcohol abstinent which are often persons having a severe disease) or overweighed, and the over participation of exsmokers. We further adjusted on health data extracted from the SNIIRAM database for both participants and non-participants which were associated with the probability of participation in logistic regression models (disability, chronic diseases and hospital discharge diagnosis, number of visits to a doctor, expenses for ambulatory care).
Research in CONSTANCES Every group in France or in other countries, public or private, is entitled to apply to develop a nested project within CONSTANCES and to access to its database. Projects are evaluated by the CONSTANCES Scientific Committee on feasibility and scientific quality criteria. A Charter describes the rules that have been established for using the CONSTANCES infrastructure, regarding legal aspects, data confidentiality and security, ethics, access to the database in the case where only available data are required or when the collection of supplementary data directly from the cohort participants is needed, as well as sharing of these supplementary data, access to the biological and genetic material, responsibilities of the CON-STANCES infrastructure and of external groups, dissemination of data and results, publications and authorship, acknowledgments, follow-up of the project and funding.
Discussion Considering its large size, the extensive coverage of the French adult population, the wealth of data collected from different sources, and its openness to the scientific community, the CONSTANCES cohort should constitute a powerful tool for public health information and epidemiologic research in many different fields. CONSTANCES has several strengths. It was designed both to help answer research questions in diverse areas and to provide public health information needed by the health authorities. To facilitate the latter aim, we devised a specific sampling scheme including a non-participants cohort and developed complex statistical procedures in order to take into account selection effects at inception as well as during the follow-up of the cohort. Once completed CONSTANCES will be a large cohort, including persons living and working in diverse settings, from large cities to small villages in different regions of France, with a broad range of socioeconomic status and trades.
Numerous data are collected at inception, including an extensive medical, physiological and biological examination, and a large biobank will be set up. The follow-up is extensive, relying both on active participation of the volunteers through annual questionnaires and regular visits to the HSCs, and on passive methods through the regular linkage to health and socioeconomic national exhaustive databases. Of particular importance is the high frequency of measurements from many different sources, allowing for analyses of life course trajectories of health in relation to personal, social, The French CONSTANCES population-based cohort: design, inclusion and follow-up occupational factors and major life events. Specific efforts were put into the quality of data collection and the validation of main outcomes in order to provide a highly phenotyped cohort.
Acknowledgments The CONSTANCES cohort is supported by the  Caisse Nationale d'Assurance Maladie des travailleurs salaries-CNAMTS , and was funded in its pilot phase by the '' Direction generale de la sante '' of the  Ministry of Health  ( CPO 2007 ( CPO -2009 )), and by the  Institut de Recherche en SantePublique-Institut Thematique Sante Ṕublique , and the following sponsors: Ministere de la santeet des sports, Ministere delegueala recherche, Institut national de la sante ét de la recherche medicale, Institut national du cancer et Caisse nationale de solidaritepour l'autonomie (AMC10003LSA). CON-STANCES is accredited as a '' National Infrastructure for Biology and health '' by the  governmental Investissements d'avenir program  and was funded by the  Agence nationale de la recherche  ( ANR-11-INBS-0002  Grant).
CONSTANCES is conducted in partnership with the  National Health Insurance Fund  administered by  CNAMTS , and with the  National Retirement Insurance Fund  administered by the  Caisse nationale d'assurance vieillesse-CNAV. Quality control procedures are taken in charge by ClinSearch for the data collected in the HSCs, and by  Asqualab and EuroCell  for the biological data. We also gratefully acknowledge the major contribution to the protocol of numerous colleagues, in France and abroad, who helped in the general design of the cohort, and of the participating HSCs. The authors express also their thanks to  Dominique Polton  from the  CNAMTS  for her help, and to  Christophe Albert  and  Joe ¨l Brulard  for the drawing of eligible persons and the access to the CNAV database.
3 mg/L in all the investigated sites  (Ayraud et al. , 2006; Aquilina et al. , 2015 ; Ploemeur monitoring database http://hplus. ore. fr/), we hypothesized that heterotrophy remains limited at all depths. We also hypothesized that nitrate and oxygen are the major drivers in the weathered zone and that chemolithotrophic metabolisms linked to iron and reduced sufur oxidation or sulfate reduction may occur in the fractured zone which represents an interesting situation of very slow nutrient renewal, i. e. , a transition state close to full isolation. The investigation of three aquifers at the regional scale, with different hydrological conditions which are well constrained, allowed us investigating a more general case to determine the relationships between microbial diversity and hydrology.
We used a principal component analysis (PCA) to assess the variability of hydrochemical parameters over a time period of 5 years, including samples from the two groundwater bodies not included in the high-throughput sequencing (P-O and SB-O). Hydrochemical measurements carried out prior 2012 were performed using the same protocol as described in this paper. The hydrochemical data are available at the website of the H + network of hydrogeological research (hplus. ore. fr). PCA was performed using hydrogeochemical parameters with correlation coefficients calculated using R and lower than 0. 8 (Supplementary Table  S1 ).
With each RNA extract, two independent RT-PCRs were performed with fusion primers containing distinct MIDs. Amplicons from each RT-PCR were quantified and multiplexed. Library preparation, emulsion PCR, and pyrosequencing on a Roche/454 GS FLX Titanium instrument were performed according to manufacturer's protocol (454 Life Sciences/Roche Applied Biosystems, Branford, CT, USA) at the Environmental Genomics facility at OSUR. These sequence data have been submitted to the ENA databases under the accession No. PRJEB8879.
Community Sequence Analyses Sequence analyses were performed using Mothur (v1. 31. 0, Schloss et al. , 2009)  and DNACLUST  (Ghodsi et al. , 2011). Raw reads were filtered to remove those with (i) homopolymers longer than six nucleotides, (ii) undetermined nucleotides, (iii) anomalous length, (iv) one difference, or more in the primer and barcode sequence. Reads were also trimmed based on quality score associated to each nucleotide. Quality filtered reads were then clustered into operational taxonomic units (OTU) with a 97% identity threshold. We used the distribution of sequences in samples and replicates of amplification to remove spurious OTU that could inflate diversity estimates. Only OTUs containing at least two 100% identical sequences, appeared independently in different replicates were retained for the subsequent analyses.
Chimeric sequences were identified with the chimera. uchime command of Mothur and eliminated from the dataset. Valid sequences in OTUs were compared to the SSU rRNA Silva database  (Quast et al. , 2013 , version 115 of August 23, 2013)  to determine taxonomic affiliations. We computed richness (Sobs) and rarefaction curves as well as diversity (Simpson's index) with Mothur. The community structures were compared with non-metric multidimensional scaling (NMDS) after calculating a Bray-Curtis dissimilarity matrix with vegan (R-package vegan,  Oksanen et al. , 2015). The fitting of environmental variables to the ordination plot was performed with vegan and the significance was obtained with a 1000 permutations test. Tested parameters were NO 3 -, SO 4 2-, O 2 , Cl -, Na + , Mn (total) , Fe (total) , Br -, Mg 2+ , K + , Ca 2+ , Cu 2+ , Zn 2+ , PO 4 2-, and total organic carbon concentrations, temperature, conductivity, redox potential, pH, groundwater residence time, and depth.
To address more directly how environmental parameters and individual OTUs were related, we performed a regularized Canonical Correlation Analysis (rCCA) between relative frequencies of OTUs and environmental parameters with the R package FRCC (Cruz-Cano and Lee, 2014). We visualized results of the rCCA analysis using the approach developed by Patel et al. (2010) A Network was built from the rCCA results. Each node represents either an OTU or an environmental parameter. Edges connecting nodes are weighted by calculating the dot product between the structural correlations of the first and fifth dimensions of the rCCA  (Patel et al. , 2010). Only edges with value weights above 0. 3 were used to build the network. Network visualization were performed with Cytoscape  (Shannon et al. , 2003). Additional information were mapped on each node to facilitate the interpretation of the network.
This study identified a large proportion of nitrate reducing bacteria in the aquifer although no clear and complete denitrification processes could be detected. These results and the present study indicate that denitrification remains probably a key function in the ecosystem although it does not accounts for a complete nitrate consumption. The strong link between NO 3 -concentration and the proportion of denitrifiers in our dataset suggests that perturbations of the nitrogen cycle in surface catchments can substantially impact microbial community structure in groundwater. Theses results suggest a limited denitrification capacity that could constitute a limit to groundwater remediation.
Their inability to oxidize Fe when O 2 concentration is high  (Druschel et al. , 2008)  explains why Gallionellaceae are restricted to deeper isolated groundwater. However, the distribution of Gallionellaceae also suggests minimal renewal of old groundwater with oxygenated recent groundwater from surface, creating chemical conditions (neutral-pH, Fe-rich, O 2 -poor groundwater) favoring the development of a Gallionellaceaedominated community. This interpretation corresponds with the geochemical data indicating that contribution from old groundwater bodies makes up a very small percentage of near-surface groundwater. Each year, the percentage of recent groundwater flushing into the old groundwater body is about 1 to 5% of the old groundwater body  (Aquilina et al. , 2015). The prevalence of Gallionellaceae in our dataset raises the question of the Fe origin in old groundwater.
, 2008; Kjeldsen et al. , 2010; Pagani et al. , 2011). Together, the simultaneous occurrence of Fe and reduced sulfur oxidizers and reducers in old groundwater hints at the existence of a complete microbial redox cycle for both elements. Due to the different proportions observed in the amplicon datasets, the oxidative portion of the cycle performed by Gallionellaceae appears to be predominant, thereby controlling the flow of carbon in old groundwater. Yet, we cannot rule out that iron-reducing bacteria might be underrepresented in our dataset because of a sampling bias, as they have the ability to thrive attached onto the aquifer substratum rather than suspended into groundwater  (Flynn et al. , 2008).
The top of the oceanic basement (TOB) defines the base of unit 3, and is undulating and offset as much as ~900 m by some faults (red arrows in Fig. 2 ). *E-mail: jgeersen@geomar. de 1 GSA Data Repository item 2015129, high-resolution image of the seismic transects without interpretation, is available online at www. geosociety. org/pubs/ft2015. htm, or on request from editing@geosociety. org or Documents Secretary, GSA, P. O. Box 9140, Boulder, CO 80301, USA. 26S 25S 24S 23S 22S 21S 20S 19S 18S 17S 16S 15S 14S 13S 12S 11S 10S 9S 8S 7S 6S 5S 4S 3S 2S 1S.