dataesr/scientific-paragraphs-categorization huggingface dataset:
Match-Up Analysis A match-up is defined as a concomitant and collocated occurrence of measurements from a satellite-borne sensor and an in situ device  (Mazeran, 2017). Both measurements should pass their own Quality Control procedure. Here, the quality control on OLCI data is defined in section OLCI Remote Sensing Reflectance. The concomitance of OLCI and ProVal measurements was, in average, within 25 min (max 1 h 10 min). For the comparison presented below, both OCLI and ProVal reflectance data were fully normalized (i. e. , corrected for bi-directionality) as defined by  Morel et al. (2002)  using Chla concentrations estimated from reflectance spectra. In the Mediterranean Sea, 25 valid OLCI measurements were retained over the 81 days of possible match-ups, i. e. , a data availability of 31%.
In the Southern Ocean, during the operating window and with 2 floats, a total of 254 days of OLCI images were collected, of which only 7 passed the quality control (data availability of 2. 7%). For these 32 ProVal profiles collected in both areas, verifications were made that radiometric data were not affected by passing cloud shadows in the upper 25 meters, and that the polynomial fit, as described in section Ascent and Surface Processing, visually retrieved the correct surface value. In the present case, 30 ProVal profiles were retained for the comparison (one rejected in both area) showing that OLCI flags (including cloud and White Caps) provided a good indication of their quality. Corresponding irradiance and radiance profiles are shown in Supplementary Material (section C). The number of match-ups for the Southern Ocean, 6, for 254 available satellite overpasses may appear low.
The use of several floats during the commissioning phase of a new OCR sensor has a clear advantage for producing the sufficient number of match-ups needed to achieve accurate and stable g-factors within a relatively short time frame. The use of multiple platforms in a SVC framework involves a rigorous management of the system, i. e. , sensors and platforms, with knowledge and traceability of uncertainties. For this latter concern, the legacy of Argo and BGC-Argo programs, which both provide a high level of data quality control and assurance, demonstrate that such management is feasible and may provide a SVC data base characterized by high consistency and fidelity to international standards. Platform improvements are possible and probably necessary in the future. Radiometers with higher spectral resolution and sampling frequency should increase the utility and data quality of ProVal floats.
ACKNOWLEDGMENTS The authors acknowledge  Météo-France and HyMeX  for supplying the  Bouée côte d'Azur data. We thank all the administrative staff of the LOV and in particular  Linda Féré ,  Corrine Poutier ,  Anne Silbermann  and  Isabelle Maire. We also thank the crew of R/V Sagitta III and Téthys II as well as  Melek Golbol  and  Guillaume De Liège. Sabine Marty  is kindly acknowledged for his help and expertise for the calibration of ProVal's radiometers. Finally, we are grateful to  Joséphine Ras  for proofreading and to the two external reviewers and the editor for providing constructive comments to the manuscript.
FUNDING The authors acknowledge spatial agencies for funding: CNES (TOSCA) for the initial funding, ESA, and NASA. Following projects also provide support and funding: NAOS (Investissements d'avenir ANR-10-EQPX-40), SOCLIM (BNP Paribas Foundation, DOI:10. 17600/16003300), remOcean (ERC advanced grant N • 246777), BOUSSOLE (CNES TOSCA and ESA/ESRIN/Contract N • 4000119096/17/I-BG). Support of EB was provided by NASA OBB program (NNX14AP86G).
Omics approaches (e. g. transcriptomics, metabolomics) are promising for ecological risk assessment (ERA) since they provide mechanistic information and early warning signals. A crucial step in the analysis of omics data is the modelling of concentration-dependency which may have different trends including monotonic (e. g. linear, exponential) or biphasic (e. g. U shape, bell shape) forms. The diversity of responses raises challenges concerning detection and modelling of significant responses and effect concentration (EC) derivation. Furthermore, handling high-throughput datasets is time-consuming and requires effective and automated processing routines. Thus, we developed an open source tool (DRomics, available as an R-package and as a web-based service) which, after elimination of molecular responses (e.
g. mortality, growth, photosynthesis inhibition). Those endpoints mostly follow a sigmoidal CRC and appropriate approaches (PROAST software: www. rivm. nl/proast, DRC package 5 ) were developed to model such responses and calculate ECs. In the recent years, toxicogenomics approaches were established to support the ERAprocesses. The data obtained from such approaches showed that the sigmoidal trend is rather the exception than the rule  6, 7. Molecular responses can be monotonic but following other than sigmoidal trends or even non-monotonic. Hence, estimating ECs from omics data requires a robust tool that can automatically handle high-throughput data, capture and model various and complex trends, and calculate ECs from such complex responses. Obtaining ECs based on omics data, comparable to those obtained from apical endpoints, is a challenge.
Other methods proposed for analysis of concentration-response data  14, 15  only consider monotonic CRCs and hence, would miss/discard molecular responses displaying common biphasic (U shape, bell shape) CRCs. Often, the CRC modelling step compares different potential models by curve fitting based on non-linear regression and the model with the best fit is selected to describe the CRC  [6] [7] [8] 11. However, not all of the approaches allowed deriving an EC, which would represent a crucial step in case of the use of toxicogenomics data for subsequent ERA. Furthermore, the robustness of an EC derivation from such data was never studied. The aim of this study was to develop a workflow and a tool to facilitate the use of high-throughput omics data in ERA by providing reliable ECs. This workflow was designed to handle the various steps of omics data processing in a concentration (as well as dose)-response framework and is already provided as an open source software (DRomics) via an online interface and an R package.
Our objectives were to provide a tool that enables to (1) normalize data, (2) select both monotonic and biphasic responses, (3)   model CRCs for significant responses and choose the best fitting model (based on a reduced, essential set of models with a limited number of parameters, but allowing to capture a great diversity of trends), (4) characterize the shape of CRCs and (5) derive an EC from each CRC whatever its shape is. The DRomics tool is inspired from a previous workflow developed by Smetanova et al. 7  , but proposes the improvement of steps 2 and 3 and the addition of steps 4 and 5. We especially paid attention to the repeatability of the results to assess the robustness of the whole approach. To exemplify our approach, we worked on a microarray-based transcriptomic dataset of the green algae Scenedesmus vacuolatus exposed to triclosan.
Scenedesmus vacuolatus transcriptomics response analysis Cultures of chlorophyte S. vacuolatus were exposed for 14 hours to five concentrations of triclosan in the range of 0. 69-6. 63µg/L (SI section S1). After sampling, the RNA was extracted, labelled and hybridised to Agilent (Amadid #067797, Böblingen, Germany) microarrays (SI section S1). As no commercial microarray was available for this species, the microarray was designed based on a former RNA sequencing (SI section S1). Briefly, 21,495 contigs (minimum length 100 bases) were selected to design an 8X60 K microarray using eArray (Agilent Technologies, Böblingen, Germany). Each contig was represented by one or two probes in the microarray and overall 18,562 probes were replicated. The final dataset contains the fluorescence value of 61,535 probes (18,562 probes present twice and 24,411 present only once) for 6 treatments in 5 replicates.
DEVELOPMENT OF THE DROMICS TOOL The TOC illustrates an overview of the main steps handled by the DRomics tool. DRomics has been released as an R package (https://lbbe. univ-lyon1. fr/-DRomics-. html) and a user-friendly online application is also available (http://lbbe-shiny. univ-lyon1. fr/DRomics-shiny/). Its workflow is organized in four steps described thereafter.
Step 1: Inter-array normalization As a first step, the log2 fluorescence values of the microarray dataset were normalized (between arrays) in order to counterbalance for differences in hybridization efficacy. The limma R package  16  , which is dedicated to deal with microarray data, proposes three options to normalize single-channel data using the function "normalizeBetweenArrays": quantile, scale and cyclic loess. Briefly, the quantile normalization forces the entire empirical distributions of each array to be identical while the scale method forces only the medians of each array to be the same. The cyclic loess method, which is more sophisticated and more time consuming, is based on the repetition on each pair of arrays of a loess regression between the difference of expression between the two arrays and the average expression 17.
However, the DRomics tool would nevertheless offer the opportunity to apply one of the other methods. Step 2: Selection of the responsive molecular data Datasets obtained from omics-approaches are typically large, representing thousands of genes and not all them respond to a concentration gradient of an exposure chemical. Therefore, we implemented a filtering step which aimed to identify significantly responding genes to the stressor, in order to focus only on the genes of interest, to reduce computational effort for modelling CRCs, and as recommended by Webster et al. 18  to improve the biological relevance of derived EC values. Our purpose was to provide an approach adapted to handle dose or concentration-response data which (1) does not require many replicates for each tested concentration (and hence was more powerful than the classically used ANOVAbased method  13  ) and (  2 ) is able to detect both monotonic and biphasic CRCs.
Tukey et al. 19  proposed a trend test to detect monotonic trends by testing the significance of a regression line linking the response to the concentration in raw scale, log-scale or rank-scale. Assuming that the tested concentrations are well placed within the design, we favored the rank scale. Furthermore, in order to extend Tukey's trend test to the detection of biphasic trends, we tested the global significance of a first order polynomial curve (regression line) and of a second order polynomial curve (quadratic curve). The 3 approaches (ANOVAtest, linear trend test and quadratic trend test) were implemented in DRomics using the limma R package and compared. A Benjamini-Hochberg correction on the p-values was applied in order to control the false discovery rate associated to such a big dataset.
A default threshold value of 0. 05 was fixed for the false discovery rate. Based on the microarray dataset consisting of 61,535 probes with 5 replicates per condition, the quadratic trend test detected the highest number of responsive probes (n=13,182) compared to the ANOVA test (n=8,755) and the linear trend test (n=5,790) (Figure  1A ). In Figure  1  the numbers of selected genes are reported in the caption. More than ninety percent of the probes which were detected by both ANOVA and linear trend test were also detected by the quadratic trend test. In contrast, the quadratic trend test allowed detecting a considerably higher number of probes if compared to the two other tests. In order to compare the three approaches in situations with a low number of replicates per condition, the same procedure was applied but on only three or two out of the total of five replicates (Figure  1B  and Figure  1C ).
Recommendation regarding the use of the workflow At each step of the DRomics workflow, the user is free to choose several options (all the options tested within this work), both in the R package and the online version. However, we wish to provide some recommendations. For the selection step, the power of the ANOVA-based selection will increase with the number of replicates, which should not be favored in a concentration-response framework as explained before. In order to select only monotonic dose-response trends, a linear trend test may be a relevant choice. In contrast, a quadratic trend test proved efficient in our study where we wanted to select both monotonic and biphasic trends. With a very large dataset like ours (e. g. 60,000 probes), a selection of data (e. g. probes) is necessary not only to reduce the amount of information to be further processed (fitting and post-treatment/interpretation) but also to eliminate too noisy signals that may impair the quality and biological relevance of results  18.
Our tool provides as an output the cumulated distribution of the BMD of the data of interest (here probes, Figure  3 ). This sensitivity distribution echoes the species sensitivity distribution concepts and thus exemplifies a first step (among the various possibilities of the tool) toward the use of omics data in ERA. Effect concentrations could also be a common language between biological level and omics. That might promote the establishment of links between levels and support mechanistical understanding using for example the functional annotation of the data. In this context, the tool can run on any other type of data that can be fitted directly by least-square regression and do not require a normalization step or that have been adequately processed in advance (e. g. metabolome data). Thus, DRomics provides comparable management for various omics data and facilitates multi-omics analysis.
In the future, this tool will also be able to process RNA-seq data for which we plan to adapt the two first steps of the workflow taking into account the nature of RNA-seq data  29. It will thus offer a simple way to compare the results obtained from different genomics platforms and evaluate their respective biological relevance as done by Webster at al. 18  in toxicogenomics. The developed procedure is now available both as an R package named DRomics (https://lbbe. univ-lyon1. fr/-DRomics-. html) and as an online Shiny application (http://lbbeshiny. univ-lyon1. fr/DRomics-shiny/) also named DRomics based on the previous package. (coded GP. U for U-shape -16. 8% of curves) or 0 f > (coded GP. bell for bell-shape -12. 5% of curves) and the log-Gaussian-probit model with 0 f < (coded lGP. U for U-shape -7.
9 µg L -1 pooled mRNA from all exposure conditions. Preparation of cDNA library was conducted according to the manufacturer's instruction of the Roche 454 GS-FLX Titanium platform. Briefly, 0. 5 µg pooled mRNA was converted to double-stranded cDNA using a cDNA synthesis system Kit and a cDNA Rapid Library Prep Kit (Roche) and sequenced afterwards using the Roche 454 GS-FLX Titanium platform. The sequencing resulted in 934,866 reads with a length of up to 400 bps. 97% of the reads could be assembled into 22,000 contigs using Newbler software  1. Out of this data set 21,495 contigs were selected to design a 8X60 K microarray using eArray (Agilent Technologies, Böblingen, Germany). Remaining gaps on the array were filled randomly with already represented probes. The probes sequences and the related contigs are presented in an excel file.
Microarray analysis In total, 40 microarrays were used in this study. The microarrays were disposed on 5 slides, meaning that each slide was composed of 8 microarrays (Figure  S1 ). On each slide, each microarray was associated to one of the different treatments (from the control to the highest concentration tested). Thus, each slide represented one replicate. Fifty ng of purified RNA were labeled using the Low Input Quick Amp Labeling Kit, one color (Agilent Technologies). Finally 40 µL cy3-labeled fragmented cRNA per sample were hybridized to each microarray for 17h at 65°C. Then, the 40 microarrays were washed and scanned, using an Agilent DNA Microarray scanner. Fluorescence intensities were extracted using the Agilent Feature Extraction software. One of the criteria used to assess the quality of microarray analysis was based on the spot finding at the four corners of the arrays (on image per array=40 images) and the Agilent SpikeIns concentration-response.
In Aquatic Ecotoxicology; Elsevier, 2015; pp 25-49. 32 (4) Posthuma, L. ; Traas, T. ; Suter II, G. General Inroduction to Species Sensitivity Distributions. In 33 Species Sensitivity Distributions in Ecotoxicology; Boca Raton: Lewis Publishers, 2002; pp 3-10. 34 (5) Ritz, C. ; Streibig, J. C. Bioassay Analysis Using R. J. Stat. Softw. 2005, 12 (5). 35 (6) Gündel, U. ; Kalkhof, S. ; Zitzkat, D. ; von Bergen, M. ; Altenburger, R. ; Küster, E. Concentration- 37 38 (7) Smetanová, S. ; Riedl, J. ; Zitzkat, D. ; Altenburger, R. ; Busch, W. High-Throughput Concentration- 39 Response Analysis for Omics Datasets: Concentration-Response Analysis for Omics Datasets. 40 Environ. Toxicol. Chem. 2015, 34 (9), 2167-2180. 41 (8) Yang, L. ; Allen, B. C. ; Thomas, R. S. BMDExpress: A Software Tool for the Benchmark Dose 42 Analyses of Genomic Data.
range of 0. 69 -20. 5 µg/L covering a full concentration response curve from no inhibition to 100% inhibition of algal growth and revealing an EC 50 of 6. 6 µg/L. Additionally, the quality of exposure conditions was analysed revealing a recovery of triclosan in the media of 81% during the exposure duration of 14h. With a focus on mode-of-action-related responses in the transcriptome at low concentrations we chose an exposure range below the EC 50 value, hence the two highest exposure concentrations (11. 65 and 20. 47 µg/L) were not considered for the transcriptome analysis. triclosan in the Genome information (cDNA sequences) have been deposited in NCBI's Sequence Read Archive and are accessible through the SRA accession number PRJNA498405 (https://www. ncbi. nlm. nih. gov/sra/PRJNA498405).
Implementing GenerationPMTO Once a program survives the rigorous tests to be designated an EBI, it is time to take it to the community. Implementation research builds a bridge between outcomes obtained in the scientific environment in which programs are developed and the challenging community contexts in which they are practiced. The process is complex, requiring many steps to build the infrastructure necessary to support initiation and maintenance of the program with fidelity to the model  (Fixsen, Naoom, Blase, Friedmen, & Wallace, 2005; Forgatch, Patterson, & Gewirtz, 2013; Saldana, 2014; Weisz, Ng, & Bearman, 2014). The first full transfer approach to implementation by GenerationPMTO was initiated in Norway in 1999 in a nationwide program that has continued to grow and develop  (Forgatch & Kjøbli, 2016; Ogden & Amlund-Hagan, 2008; Ogden, Forgatch, Askeland, Patterson, & Bullock, 2005; Tømmeraas & Ogden 2017).
Since then, the full transfer approach to implementation of GenerationPMTO has been carried out in several locations in North America, including Michigan and Kansas in the USA, and British Colombia in Canada, and in Europe (see  Forgatch & Domenech Rodríguez, 2016; Forgatch & Gewirtz, 2017; Forgatch et al. , 2016). As in the Norway implementation, the three European nations in the present study have used the full transfer approach that is offered by GenerationPMTO. Full transfer entails establishing a governing authority that assumes responsibility for ensuring that all aspects of the program are carried out with fidelity, including training, coaching, fidelity rating, certification, and recertification of their practitioners. When training is completed and a document specifying the roles and responsibilities for leadership positions is accepted, then the reins of program leadership are turned over to the site.
As the team expands over subsequent generations, the roles become more specialized. Once the full transfer process has been completed, the site is able to shift expenditures from the purveyor to their own staff, who are now qualified to carry the program forward. Further details in carrying out the full transfer approach can be found in the following papers and chapters:  Forgatch and DeGarmo (2011) ;  Forgatch and Gewirtz (2017) ;  Forgatch and colleagues (2013, 2016). Forgatch, 2004). In an Icelandic effectiveness RCT with a clinical sample, GenerationPMTO led to greater reductions in behavior problems with children aged 5-12 relative to the comparison group receiving service as usual  (Sigmarsdóttir, Thorlacius, Guðmundsdóttir, & DeGarmo, 2015)  and was found to prevent expected damaging effects of depression on maternal parenting  (Sigmarsdóttir, DeGarmo, Forgatch, & Guðmundsdóttir, 2013).
Previous research in Norway has demonstrated that high GenerationPMTO fidelity at certification was maintained for the first three generations of therapists  (Forgatch & DeGarmo, 2011). There was a small but significant drop in fidelity scores from G1, trained by the program purveyor, to G2 who were trained by the adopting community. However, the G3 practitioners achieved scores equivalent to those attained by G1. In this paper, we will elaborate on these results by evaluating the implementation in three European countries that are the focus of this study in Iceland, Denmark, and the Netherlands. We examine each country's ability to sustain fidelity for up to 14 years and across as many as eight generations.
The positive effects from that study led to extending the reach of GenerationPMTO. Today the program has morphed from one community to rolling out the program to other sites nationwide. The first generation of therapists (G1) was trained by the purveyors from the USA. The second generation (G2) was trained by Icelandic G1 GenerationPMTO specialists with coaching from the purveyors, and further generations were trained by Icelandic specialists with some consultation from the purveyors. A new generation is in training and will be certified in 2018. The implementation process in Denmark and the Netherlands was nationwide from the beginning, which accelerated progress. The implementation in Denmark, which began in 2004, differed from the process in Iceland. The Danish G1 practitioners were trained in Norway by Norwegian trainers and the Danish G2 practitioners were trained by Norwegian and Danish GenerationPMTO specialists in Denmark.
Generations G3 and G4 were trained by Danish GenerationPMTO specialists with some coaching from Norway; since G5 started training there has been some involvement from the program developers. The Icelandic and adapted by G1 professionals with support from linguistic and graphical experts. A similar process was followed in the Netherlands. The process differed in Denmark, where G1 was trained by Norwegian PMTO trainers using Norwegian materials. Danish materials were later developed based on Norwegian and American PMTO manuals and used in training starting with G7.
Research Procedures and Analytical Strategy Fidelity results were based on the FIMP scores of candidates submitting certification sessions. Reliable FIMP coders rated the certification sessions, which for each therapist consisted of four videorecorded sessions of individual family therapy. Data were available for six generations in Iceland, eight in Denmark, and four in the Netherlands. Information about the number of therapists who started, completed, and their duration of practice in the program was gathered from national databases for the programs and yearly reports. We used descriptive and inferential statistics to analyze the data. Inferential statistics were used to evaluate the sustainability of fidelity scores across generations and countries. Due to the nested nature of the data (i.