dataesr/scientific-paragraphs-categorization huggingface dataset:
The objectives of this monitoring programme are to assess the influence of continental inputs on the marine environment and their implications for possible eutrophication processes. It also aims to estimate the effectiveness of development and management policies in the marine coastal zone by providing information on trends and/or shifts in pressure, state, and impact variables. The regular acquisition of data allows the establishment of long-term monitoring of the evolution of coastal water quality as well as the observation of the consequences of large-scale alterations mainly driven by climate change and modifications that are more related to local/regional anthropogenic activities. This paper provides an overview of the main characteristics of SRN data (descriptive statistics and data series main patterns) as well as an analysis of temporal trends and shifts.
We also propose to the data user a specific numerical tool available as an R package to optimize the data pre-processing and processing steps. Users will then have easy access to statistics, trends, and anomalies as proposed in this paper. The main results of several research projects based on SRN data and dealing with hydrology, phytoplankton blooms, HABs, phenology, and niches are also highlighted, providing the readers with examples of what can be done with such a data set. We hope that this synthesis will also save data users time by allowing them to jump right into a deeper analysis based on previous conclusions and perspectives or to investigate new scientific key challenges. These data should also be used at a wider geographical scale, combined with other data sources, to define more global patterns of environmental changes in a moving world subject to strong anthropogenic pressures.
The regular acquisition of data allows the establishment of long-term monitoring of the coastal waters along three transects located off Dunkerque, off Boulogne-sur-Mer, and in the Bay of the Somme, making it possible to pretend to be able to deconvolute the effects of large-scale changes from those linked to more local anthropic activities  (Lefebvre et al. , 2011). Since 1992, the SRN data set has included long-term time series on marine phytoplankton and physicochemical measurements along the eastern coast of the English Channel and the French section of the Southern Bight of the North Sea (Dunkerque). SRN data are complementary to the RE-PHY and REPHYTOX data sets  (PHYTOBS, 2021; REPHY, 2021; REPHYTOX, 2021). Phytoplankton data cover microscopic taxonomic identifications and counts, up to the species level, and pigment measures (chlorophyll a and pheopigment).
Objectives The objective of this paper is to present the SRN data set, from the sampling strategy to the data collection (including the associated quality assurance/quality control steps) and data investigation and storage. The characteristics of the different data sets as well as a general interpretation of their variability will be presented. Based on existing valorizations of the SRN data set, we will demonstrate that these data are of relevance not just for furthering understanding in marine phytoplankton ecology, but also for public policy needs such as assessment of environmental or ecological status as requested by EU directives or regional sea conventions. We also present some numerical tools based on an R package available for the scientific community and developed specifically to rapidly process such data and therefore to valorize the findings.
This leads to enhanced exchange possibilities between the two water masses  (Brylinski et al. , 1991). Three sampling areas along an inshore-offshore transect were studied by Ifremer from 1992 to 2021 (still ongoing) within the framework of the SRN, two of which are located in the eastern English Channel and one in the Southern Bight of the North Sea (Fig. 1 ). The database shows another sampling area in the Bay of the Somme, named "SRN Somme mer 1", but the survey was stopped in 2015. Therefore, these data will not be analysed in the present article, which is related to the 1992-2021 period. The coordinates of the sampling areas are given in the database in columns "Coordonnées passage: Coordonnées maxx" for longitude and "Coordonnées passage: Coordonnées maxy" for latitude (see the Supplement) and summarized in Table  1.
Phytoplankton Phytoplankton samples have been collected along transects and were preserved with an acid lugol solution (0. 25 %). Sub-samples of 10 mL were settled for 24 h in a counting chamber according to  Utermöhl (1958). Cell enumerations were performed by inverted microscopy using a microscope within a month after the sample collection to prevent any significant changes in phytoplankton size and abundance. Except for Phaeocystis globosa enumeration, over 400 phytoplankton cells in each sample were counted with a 20X Plan Ph1 0. 5NA objective, resulting in an error of 10 %. This species is reported as the genus Phaeocystis in the database due to national standardization. Nevertheless, for our area,  Rousseau et al. (2013)  confirmed that P. globosa is the relevant species. For assessment of P.
globosa counts, only the total number of cells is computed. A minimum of 50 solitary cells were enumerated from several randomly chosen fields (10 to 30) with a 40X Plan Ph2 0. 75NA. Abundance of cells in a colony was determined using a relationship between colony biovolume and cell number defined by  Rousseau et al. (1990). Some harmful algae are also subject to specific surveys during a given risky period. Therefore, those species are part of a pre-defined list that must be checked during enumeration. If any of these taxa are absent from a sample, their abundance is reported as 0 in the database. Phytoplankton identification is standardized using the WoRMS (2022) database and reaches the level of the species in many cases. However, when the identification is not easy or is subject to caution, a lower taxonomic level is kept in the Quadrige 2 database.
Database To manage coastal monitoring data, Ifremer has developed the Quadrige 2 information system (https://envlit. ifremer. fr/ Quadrige-la-base-de-donnees, last access: 24 January 2023), which combines a database with a variety of interpretation and information product development tools. As an information system, Quadrige 2 plays a crucial role in (1) storing basic monitoring data, such as the results of analyses from all monitoring networks, in a safe, optimal, supervised, and scalable manner and (2) interpreting and valuing the data. Once the data have been stored and a quality level assigned to them, they are ready for use in a wide range of applications. As a result, this system is the required link for monitoring data between data collection in the field and their availability in various formats.
Quadrige 2 has been approved as the national reference information system for coastal waters by the French Ministry in charge of the Environment. The data sets presented in this article are extractions from the Quadrige 2 database. The data come in two different files: one with hydrochemical parameters and chlorophyll-a concentrations, containing 57 columns and 54 578 rows, and another with phytoplankton abundance data, containing 55 columns and 98 904 rows. These data are available as a csv file with a semi-colon separator. They are American National Standards Institute (ANSI)-encoded and points are used as decimal separators. Moreover, the database header's columns are in the French language (Quadrige 2 is a French national database). The French-English translation is given in Supplement file S1.
In addition, a complete description of the header is given in  Ifremer (2017). Physicochemical and phytoplankton data are being given and made available over the 1992-2021 period because the database is up to date until 2021 at the time the article is written. Because the database is updated annually, future users are likely to download a data set with additional years of data than the actual one. Former data sets (associated with the oldest DOI as found in previous articles) are still available on user demand. The phytoplankton database contains three different kinds of cell enumeration: FLORTOT, FLORPAR, and FLORIND. In the column "Résultat : Code parameter", FLORTOT means that all taxa contained in the samples were identified, FLORPAR means that only some dominant taxa were identified when abundance > 100 000 cells L -1 , and FLORIND means that only indicative HAB taxa (Dinophysis sp.
Data validation The data are collected in the field and/or laboratory and then entered into the Quadrige 2 database using an application with the same name. The control entails modifying the data entered (results and metadata) to ensure that they are consistent with the benchbook (or field sheets). After this check is completed and any necessary corrections are made, the data are validated. -Confirmation of the technical validity of the data which correspond to the result of the analysis. -Data locking, so that they can no longer be edited, even by the person who entered them. -Data distribution: verified data may be taken and disseminated by all Quadrige 2 users who have access to the database.
The qualification is broken down into two main steps. Table  4. Statistical summary (minimum, first and third quantiles, mean, median, maximum, length of the data series) for the physicochemical and biological variables collected within the SRN monitoring programme in the time span 1992-2021 and for coastal stations only (DK1, BL1, Atso). Increasing or decreasing monotonous 1 /non-monotonous 2 trends are indicated in the "Sign. trend" column (1: one orange (green) arrow for an increasing (decreasing) monotonous trend -2: two arrows for a non-monotonous trend (shift in the time series) -a grey arrow indicates no significant trend). 1. An "automatic" qualification that consists in looking for "gross" and easily identifiable errors 2. An "expert" qualification, which consists in highlighting statistically aberrant data via adapted methods (time series analysis, statistical tests, etc.
Data availability The SRN database is publicly available via https://doi. org/10. 17882/50832 (SRN -Regional Observation and Monitoring program for Phytoplankton and Hydrology in the eastern English Channel, 2022). Some other data should be considered as a complement to the SRN data set. Indeed, coastal stations of the SRN local monitoring programme are part of the REPHY national network. This network shares the same protocol and considers the same parameters. REPHYTOX is complementary to the REPHY network, as it contains phycotoxin concentrations in mus-   data set. The package creates a Genuine User Interface (GUI) and is available via https://cran. r-project. org/web/packages/ TTAinterfaceTrendAnalysis/index. html (last access: 24 January 2023).
Discussion and conclusion The SRN data, which have been collected since 1992, represent one of the longest long-term data sets in the English Channel and the Southern Bight of the North Sea. This enables the study of phytoplankton dynamics and diversity as well as changes in particular composition in response to anthropogenic and/or climatic change. Indeed, these data have been used by scientists from several backgrounds and for a variety of research purposes. In the following paragraphs, the main outputs from these research activities will be highlighted. This review can help the scientific community interested in this SRN data set to identify topics that can be addressed or those that have not been addressed. It also allows for new topics to be discussed in more detail since the general patterns have already been identified.
A permutational multivariate analysis of variance (PERMANOVA) assisted in revealing the significance of seasonal variation in environmental and community factors. It was able to locate the harmful taxonomic niches among the rest of the community and quantify how the respective phenology influences the dynamics of the sub-niches by using methodologies such as the outlying mean index and the within outlying mean index. These results are comparable to those of Hernández Fariñas et al. (  2015 ), who, based on a similar approach but extended to the Dunkerque and Bay of Somme SRN sites, concluded that light, temperature, species richness, and nutrient concentrations are the main factors controlling phytoplankton dynamics and community structure. We also confirmed from SRN data that periods of Phaeocystis globosa dominance are generally associated with high concentrations of the Pseudo-nitzschia complex.
This interface is available on the CRAN website as the TTAinter-faceTrendAnalysis package. It allows quick definition of the main statistical characteristics of SRN series and proposal of classical time series analyses (data regularization and aggregation, detection of anomalies, breaks and seasonal or global trends). The results are presented in the form of summary tables or graphs that are automatically saved in the user's working directory. SRN data are used for validation of coupled hydrodynamic-biogeochemical models such as ECO-MARS 3D in addition to improving scientific knowledge about phytoplankton dynamics, biodiversity, and water quality  (Ménesguen et al. , 2019). Including retrieved chlorophyll a and suspended matter, SRN data are an essential source for the development and improvement of satellite water colour algorithms  (Gohin et al.
, 2008 (Gohin et al. , , 2019 (Gohin et al. , , 2020)). In terms of the latter parameter, they are the only data with such geographical and temporal coverage available in the coastal area of the eastern English Channel and the Southern Bight of the North Sea. Other data from the Coastal Observation Service (Somlit; https://www. somlit. fr/, last access: 28 April 2022) can supplement the SRN data for the Boulogne-sur-Mer coastal zone  (Lheureux et al. , 2021). In the context of the implementation of the Water Framework Directive (WFD 2000/60/EC), some coastal points of the SRN integrate the so-called Monitoring and Operational Control system. The new Marine Strategy Framework Directive (MSFD) extends the WFD approach limited to the first nautical mile from the baseline (for biological parameters) to the offshore waters (Exclusive Economic Zone).
Thus, the offshore stations of the SRN network also meet the diagnostic and monitoring expectations advocated by this European directive  (Lefebvre and Devreker, 2020). As part of the Oslo and Paris Convention's strategy to combat eutrophication (OSPAR, http://www. ospar. org/, last access: 24 January 2023), SRN results are used to define the eutrophication status of water bodies. The SRN data are also transmitted to the ICES working group Phytoplankton and Microbial Ecology (WG PME) in order to contribute to the drafting of the dedicated annual report (http://www. ices. dk/ community/groups/Pages/WGPME. aspx, last access: 24 January 2023). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-15-1077-2023-supplement. Author contributions.
Methods and findings We conducted a two-stage individual participant data (IPD) meta-analysis using data from 253,810 mother-child dyads from 16 general population-based cohort studies in Europe (Denmark, Finland, France, Italy, Norway, Portugal, Spain, the Netherlands, United Kingdom), North America (Canada), and Australasia (Australia) to estimate the association of GA with body mass index (BMI) and overweight (including obesity) adjusted for the following maternal characteristics as potential confounders: education, height, prepregnancy BMI, ethnic background, parity, smoking during pregnancy, age at child's birth, gestational diabetes and hypertension, and preeclampsia. Pregnancy and birth cohort studies from the LifeCycle and the EUCAN-Connect projects were invited and were eligible for inclusion if they had information on GA and minimum one measurement of BMI between infancy and adolescence.
Data access and federated analysis on DataSHIELD In this study, we used pseudonymised data stored on local secure data servers in their original location  [59, 60, 61, 62]  and harmonised according to protocols in the EU Child Cohort Network  [39]. Cohort-specific description about methods for ascertaining and defining variables are documented in the EU Child Cohort Network catalogue (https://data-catalogue. molgeniscloud. org/catalogue/catalogue/#/) and the Maelstrom Catalogue (http://maelstromresearch. org) for studies in LifeCycle and EUCAN-Connect, respectively. Data were analysed remotely through the R-based and open-source software, DataSHIELD, which allows federated analysis through one-stage and two-stage IPD meta-analysis approaches with active disclosure controls  [63, 64, 65, 66].
The meta-regressions were conducted to determine which study characteristics were independently associated with between-study heterogeneity. In addition, we undertook "leave-one-out" analysis for cross-validation to explore the influence of each study on the overall estimate  [75] , while subgroup analysis with sex (boys versus girls), maternal education (high versus low/medium), maternal smoking in pregnancy (no versus yes), and birth year (<2001, �2001) was performed to measure the robustness of our findings. Statistical analyses were performed using DataSHIELD and the Statistical Software R (v4. 1)  [76]. We mainly used the ds. getWGSR and ds. glmSLMA functions from the dsBaseClient (v6. 1. 0, https://github. com/datashield/dsBaseClient/) and the dh. makeStrata function from the ds-Helper package (https://github.
Ethical approval This study builds upon a federated analysis solution that facilitates cross-border sharing of harmonised and pseudonymised data in compliance with both European and national data protection, patient's rights, and research ethics regulation. Rather than sharing or transferring individual-level data, only nondisclosive low-dimensional summary statistics are made accessible upon request. Prior to analysing the data via DataSHIELD, the lead author obtained a waiver of consent (data access or data transfer agreement) from each cohort study via the partner in LifeCycle and EUCAN-Connect. The data from the 16 cohorts were only accessible as meta-data controlled through active disclosure protection, which mitigates the risk of identification of study participants. Further information about the infrastructural setup using Data-SHIELD, and previous international networks using similar methods, are describe in details elsewhere  [78, 79, 80].
The ELFE study was also approved by the national committee for statistical information (CNIS: Conseil National de l'Information Statistique); (G21) Generation XXI was approved by the Portuguese Data Protection Authority and by the Ethics Committee of Hospital São João, and data confidentiality and protection were guaranteed in all procedures according to the Declaration of Helsinki. Signed informed consent was obtained for all adults and children participants had it signed by their legal guardian at every study waves; (GECKO) The GECKO Drenthe study complies with the Declaration of Helsinki and was approved by the Medical Ethics Committee of the University Medical Center Groningen; (GEN R) The general design, all research aims and the specific measurements in the Generation R Study have been approved by the Medical Ethical Committee of the Erasmus Medical Center, Rotterdam.
All participants were provided with participant information sheets and parents (Gen1) provided informed consent, and the child (Gen2) provided assent. When the Raine study Gen2 participants turned 18 years of age, ethics approval was further received from the University of Western Australia HREC (RA/4/1/2100) to contact and obtain consent from Raine study participants for any data that was collected before they were 18 to be used for future research. A further UWA HREC was provided to all a single 'overarching' approval code that recognises all previous approvals under which previous data and/or bio-samples were collected. This approval was received on 29 April 2020 and provides a single consolidated approval (RA/4/20/5722) for use of research data and/or bio-samples held in the Raine study data collection; (SWS) The study had full approval at each wave from the Southampton and Southwest Hampshire Local Research Ethics Committee.
2% to 72. 7%) (Table  1 ). The mean GA was 39. 8 weeks, and overall 5. 5% were born preterm (range: 3. 1% to 7. 5%), 17. 8% (range: 11. 8% to 31. 6%) were born early term, 69. 9% were born full term (range 61. 1% to 73. 6%), and 6. 7% (range: 0. 2% to 15. 4%) were born postterm (Table  2 ). The majority of the cohorts had study participants included for analysis in all five categories of GA, except CHILD (34 to 43 weeks gestation). From infancy to age 19 years, 711,856 measurements of BMI were available for 253,810 children. The number of cohorts and participants with data on BMI and overweight varied across the six age-bands with most cohorts and participants in infancy and mid-childhood and fewest in adolescence, where four cohorts (ALSPAC, DNBC, NFBC1986, and the Raine Study) contributed with data on 36,895 individuals.
We used comprehensive obstetric and maternal data as well as multiple BMI measurements following birth through adolescence, which allowed us to adjust analyses. Additionally, the large sample size enabled us to assess associations successively using clinical categories of preterm birth to age 19 years. We also examined the robustness of our findings performing several sensitivity analyses. Furthermore, the federated analysis approach using DataSHIELD proves a key advantage since it enables identical and reproducible analysis across multiple cohorts  [39, 92, 93]. The limitations include considerable variations in both measurement and availability of exposures, covariates, and outcomes. However, this was explored by meta-regressions on multiple covariates showing that study characteristics were independently associated with between-study heterogeneity only in the associations of GA with BMI.
Acknowledgments The authors would like to acknowledge everyone in LifeCycle and EUCAN-Connect who have supported and contributed to each cohort included in the study. In addition, acknowledgments are sent to the DataSHIELD team. Please see S2 Appendix for list of cohort-specific acknowledgments. Also, the authors would like to acknowledge  Tanis Fenton  for her contribution to this manuscript, and data curation for AOF.
Literature review The literature review is based on existing scientific papers (e. g. Bürger et al. , 2019; Sarma and Bazbauers, 2016; Söderholm and Wårell, 2011; Wissner, 2014) , studies and reports (e. g. Castellazzi, 2017; Euroheat & Power, 2019; Patronen et al. , 2017) , MS plans (progress reports, national energy and climate plans) and databases (RES Legal 1  and IEA policy database 2  ). In addition, national acts and legal documents, national reports and websites from energy agencies, authorities and national associations or companies were included. These country-specific sources can be found in the country factsheets in  Bacquet et al. (2022a)  (project report 'DHC Trend'; The data collection took place in the framework of this project; see acknowledgements). Key words for the literature search were, for example, 'district heating regulation', 'district heating price regulation', 'regulation of heat metering' as well as phrases such as 'mandatory grid connection to district heating in Germany'.