dataesr/scientific-paragraphs-categorization huggingface dataset:
2011 (Arzoumanian et al. , 2019)) , do not produce any break or kink in the Fourier power spectrum  (Panopoulou et al. 2017). Robitaille et al. (2014 Robitaille et al. ( , 2019) )  showed using the multiscale non-Gaussian segmentation (MnGSeg) technique 1 that filamentary structures are in fact dominating the scale-free Fourier power spectrum. This result demonstrated that dense coherent struc-1 Codes and tutorials are available at https://github. com/ jfrob27/pywavan tures of the ISM also have a hierarchical geometry (most likely multifractal and intimately linked to the formation of stars) and that this geometry is intertwined with the monofractal and diffuse component of molecular clouds. This finding challenges the recent results of  Roy et al. (2019) , who suggested that only filamentary structures with a sufficiently high area filling factor and/or high column density contrasts have an effect on the scalefree power spectrum of dust-continuum images.
The determination of this range of statistical order moments should not be arbitrary, but rather dictated by the availability of statistics in the analysed dataset to ensure statistical convergence. The range of q values can be robustly and objectively determined (see  Khalil et al. (2006)  Eq. 32 and Figures  8  and 25 ). The multifractal characterisation of a medium or a surface is a very sensitive analysis, and we advise care in the choice of the approach according to the type of data and the size of the sample exhibiting a similar statistic. Unfortunately, several multifractal studies use q values are high as |q| = 20 without justification  (Elia et al. 2018; Chappell & Scalo 2001) , which can lead to numerical instabilities that may very well create artificial multifractal signatures.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Grant  Agreement No. 750920  and from the  programme StarFormMapper  under grant agreement No  687528. This work was also supported by the  Programme National de Physique Stellaire and Physique et Chimie du Milieu Interstellaire (PNPS and PCMI) of CNRS/INSU  (with  INC/INP/IN2P3 ) co-funded by  CEA  and  CNES. This research has made use of data from the  Herschel Gould Belt survey (HGBS)  project (http://gouldbelt-herschel. cea. fr). The HGBS is a Herschel Key Programme jointly carried out by SPIRE Specialist Astronomy Group 3 (SAG 3), scientists of several institutes in the  PACS Consortium (CEA Saclay, INAF-IFSI Rome and INAF-Arcetri ,  KU Leuven, MPIA Heidelberg ), and scientists of the  Herschel Science Center (HSC).
The minimum data requirements are described in Annexes VI-X of the regulation. For toxicological testing, the standard requirements ask for in vivo or in vitro the first applications of the in EU-ToxRisk in vitro and in silico test batteries is applying the RAx hypothesis  (Graepel et al. , 2019; Escher et al. , 2019). With this goal, the project has developed ad hoc case studies to be reviewed by regulators from ECHA and EU member states, with the aim of further improving the understanding of what constitutes an acceptable RAx justification and how this justification can be supported and improved using NAM data. The integrated results were submitted to regulators from ECHA and EU Member State competent authorities as mock dossiers and discussed in another workshop with stakeholders from academia, industry, and authorities in May 2019 (manuscript under preparation).
The worldwide dimension of RAx The EU was the first group of countries to accept RAx as a tool to address the information requirements in lieu of standard testing, but for proper implementation of the new approach, greater international acceptance is necessary. RAx, and in more general terms, in silico methods are now included and promoted in many regulations across countries and sectors. As an example, the situation in some countries is considered here (Figure  2 ), without the aim of providing an extensive analysis. In the USA, there is a minimal application of RAx for official regulatory dossiers, and there are no formal protocols/procedures/guidance documents on its use. To date, the most well-known use cases in the USA are those within the US Environmental Protection Agency (EPA) that have been already captured in the current OECD guidance (OECD, 2014a), such as the experiences under the Pre-Manufacture Notice (PMN) process or High Production Volume (HPV) program  (van Leeuwen et al.
The Japanese Hazard Evaluation Support System (HESS) database and platform 11 which was started in 2012 and has been updated regularly is promising with respect to RAx application. HESS supports the evaluation of repeated dose toxicity by category approach. Linked to that, there are two databases. The first is a toxicity knowledge database, which contains information on repeated-dose toxicity and toxicity mechanisms. The second is a metabolism knowledge database containing rat metabolism maps and information on Absorption, Distribution, Metabolism and Excretion (ADME) in rats and humans. HESS allows chemicals to be categorized on the basis of structural, physicochemical and mechanistic similarities and supports the prediction of repeated-dose toxicity for untested chemicals by means of the category approach.
, 2018). A useful database which can facilitate read-across groupings based on sorting on chemical similarity is the Danish QSAR database. It contains information on more than 600,000 chemicals from over 200 QSAR models  19. Where models are used, they should be run also for category members for which experimental data exist. In this way, the applicability of the QSAR/QSPR model(s) to the specific set of chemicals under examination can be demonstrated. When available, study results should be compared with the output of the computational approach as a reference, to assess the accuracy of the prediction and claim better reliability for the output, when applied to fill a data gap. Another computational tool developed specifically for RAx purposes is EPA's Generalized Read Across (GenRA) add-in within the Dashboard 20  , which uses a similarity-weighted activity algorithm to compute a RAx prediction using both chemical (structural fingerprint) and biological (in vitro HTS data from the Tox21 program) descriptors.
5. A means of forming categories based on both intrinsic chemical and biological activity. 6. A basis for testable hypotheses which in turn leads to the development and use of in vitro databases for developing new profilers and to establish response-to-response relationships. 7. A means of developing and justifying targeted and efficient testing and assessment scenarios that save time and resources e. g. by identifying data gaps. 8. A means of supporting assessments of combined exposure to multiple chemicals within and across AOPs. 9. Greater biological context to what is currently a statistically based approach. Even though AOPs, as presented in the AOPwiki 22  , incorporate a useful framework for comparing two or more chemicals and also a starting point for building the experiments for the biological demonstration of similarity  (Nelms et al.
They showed that on average a given self-classification in REACH registration dossiers could be reproduced with a RAx-based QSAR model based on the nearest neighbors of that chemical in descriptor space with higher reliability than was found for traditional test results in the REACH database. Notably, this work focused on a simple GHS/CLP classification endpoint, whereas for the moment such work cannot be realized for complex, multiple-endpoint systemic toxicities, even though some authors see such methodology as promising in the future and this possibility should be kept open  (Berggren et al. , 2015). Many aspects of RAx are subjective and difficult to quantify. For instance, the evaluation of similarity is very challenging and minor differences that seem negligible may have a strong impact on the biochemical activity, as illustrated by the problem of activity cliffs.
While there is a growing mass of literature published in peer-review journals, and considerable guidance from bodies such as OECD, ECHA, ECETOC, etc. , there is no overall knowledge source. Books such as by  Cronin et al (2013)  provide a sound basis to the theory, and recent papers such as  Patlewicz et al. (2017; 2018; 2019)  include updates on the tools that may be applied. Aspects of computational toxicology have been included in under-and postgraduate education for many years, from Bachelor level degrees to PhDs, while RAx as a subject has only recently been included in some academic curricula. RAx will form the core of a new master's program in Computational Toxicology 26. In general, this is not enough to train all aspiring toxicologists. To make up for the shortfall in education, and the desire to train practitioners, a number of "occasional" courses are available.
It is also helpful to demonstrate what caused rejection of RAx cases, illustrating the reasons for non-compliance provided by the relevant authority, as is the case when ECHA does not accept a RAx. Both "best" and "worst" practice examples will be instructive and should be spread in the community of RAx performers and assessors, just for educational purposes. The preparation of templates for RAx submission can also have a role in guiding new users through a proper application of the RAx approach, in particular, if those templates are distributed with examples. Templates could also include the types of reporting formats available from software e. g. from the OECD QSAR Toolbox and how these can be adapted and the information content improved. Such templates tend to be useful to store the information from the data retrieval exercise, even though expert knowledge is required for the argumentation and justification of the RAx.
In addition, successful read-across examples should be stored to avoid duplication and to act as a resource. Taking inspiration from the AOP-Knowledge-Base, it may be that a well-designed web resource and wiki may help with this. In order to improve knowledge and expertise among users, the best opportunity is by improving communication among stakeholders with the creation of databases, either at international or regional levels. This international coordination may also be used to identify gaps and concentrate efforts on the development of new computational tools or experimental methods.
Principles and key elements for establishing a weight of evidence for Chemical Assessment are contained in an OECD guidance (OECD, 2019) following ECHA, which published a template for reporting WoE 30  , but guidance how to combine different lines of evidence, how to best assign differential weights and how to quantitatively combine those lines with the aim of minimizing residual uncertainty as well as how to manage discordant results is still missing. Other important hurdles lie in the legal aspects of the right to refer to proprietary data as is the case with the majority of study reports. Even the public ECHA database has restrictions in the right to use, although a note explicitly allows download and use "to improve the safe use of chemicals, enable innovation and help avoid the unnecessary testing of chemicals on animals" 31.
At the moment there are two main available platforms: the OECD QSAR Toolbox and the EPA CompTox Chemicals dashboard. The OECD QSAR Toolbox is based on a variety of publicly available databases for mammalian and environmental effects and includes the ECHA public database of registered substances. It is structured in a mechanistic way that enables the reconstruction of the grouping process relevant for each endpoint. The EPA CompTox Chemicals dashboard is compiled from sources including the EPA's computational toxicology research databases, and other USA public domain databases. The GenRA approach is included, this was developed with the aim of systematically and objectively evaluating the performance and quantifying the uncertainties associated with read-across predictions  (Shah et al. , 2016 , Williams et al 2017 , Helman et al.
, 2019). A fundamental pre-requisite of any tool is the clear definition of the approach, with defined integration of QSAR assessment and the possibility to backtrack data history and version of the tools as well as a simple format output, adhering to the OECD principles for QSAR validation  (OECD, 2007). A very interesting initiative is the VegaHub 33  that developed specific tools, such as ToxRead, a software that builds up graphs connecting the target chemical and the structural alerts with the most similar compounds, to be used in combination with ToxWeight that provides a weight of evidence assessment joining QSAR and RAx and ToxDelta focused on the molecular structure differences, namely the dissimilarity features. (Gini et al. , 2014; Golbamaki et al. , 2017). Another available platform and public tool is AMBIT2 34 , which is an open chemoinformatic system designed to support companies by facilitating chemical safety assessment.
The AMBIT2 system consists of a database including more than 450. 000 chemical structures, the REACH dataset of 14. 570 substances, as well as the EFSA OpenFoodTox database. It contributes to the safer use of chemicals and a reduction in testing and innovation cost by making it easier for companies to comply with regulations governing chemicals. Several in-silico prediction models (e. g. Toxtree, Vega) are integrated in AMBIT2. The import and export function is enhanced in AMBIT2, which allows communication with a variety of additional prediction models such as knowledge-based expert systems for toxicity and metabolism In actual fact, the use of several computational tools (both commercial and free) can support the different phases of the RAx workflow, e. g: ⎯ tools/databases to search for potential analogues -by structural similarity, by experimental data availability for the target-endpoint, combined search (structural similarity & target endpoint data availability), by mechanistic similarity; ⎯ tools to assess similarity between target and source compoundsstructural similarity, mechanistic similarity, physicochemical and ADME similarity; ⎯ tools to predict potential metabolism and metabolic products; ⎯ QSAR predictors to predict target endpoint-related properties.
RAx should be a valuable tool in mechanistic understanding as an ideal complement of pure computer modelling systems. Academia should also reason in a reverse way, i. e. to frame their research questions and present their data in a machine-readable way with reports that contain all useful information to enrich databases for RAx exploitation. Last but not least, the development and optimization of RAx tools represent a thriving research field for academic teams. There are emerging applications also inside regulatory institutions. For example, there are two EPA Research Centers engaged in the area of RAx. Within the National Center for Environmental Assessment (NCEA), RAx is routinely applied as part of the Superfund program for provisional peer-reviewed toxicity values (PPRTVs). The workflow is documented in  Wang et al.
Political action towards harmonization is necessary. Another important aspect is education. RAx should be part of the program in university courses in toxicology and other related subjects, in order to provide another useful tool in research, and implementing NAM approaches. RAx approaches are anyhow a dynamic and continually evolving development. The area of mechanistic elucidation of toxicity pathways along with the increase of available information and computational tools is expanding dramatically and each assessment should be updated in parallel with the commitment to incorporate new evidence into existing RAx packages. These technologies offer the possibility of refinement of RAx justifications, which may lead to greater openness towards acceptance on the regulators' side. International organizations such as the OECD have a leading role to play in support of the process definition.
The fourth sector that is affected by RAx is CROs (Contract Research Organizations) that are usually performing new tests on behalf of the industry and in many cases provide advice on test strategies. CRO should be aware of RAx possibilities and should implement NAMs in the list of their services. Widespread implementation will facilitate a decrease in the cost of in vitro tests, which is desirable to encourage their application to support RAx justification. In addition, due to a general lack of strict guidance/rules for GRAP, chances of regulatory failure of a RAx submission are considerably higher compared to e. g. submitting the "traditional" standard information dataset as required by REACH. Against this background, many companies will prefer higher initial spending with guaranteed regulatory acceptance over an initially smaller investment which might result in overall much higher costs when a RAx submission is rejected.
Last but not least, there is a need for the development of new tools for building RAx. This part is more complex. First of all, there should be an international agreement for data sharing and free access to high-quality reports. Then there is the need for new databases with mechanistic information, combined with TK and TD data. In fact, at the moment, the request is to apply RAx simply to waive an extensive in vivo test, but considering the limitations of in vivo tests, the future trend is to develop a tool that increases predictivity and reliability by combining RAx approaches with multiple NAMs.
Acknowledgement This work was supported by the  Doerenkamp-Zbinden Foundation , the  BMBF, EFSA , the  DK-EPA , and the  DFG (Konstanz Research School of Chemical Biology; KoRS-CB). It has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreements No. 681002  ( EU-ToxRisk ) and No. 825759  (ENDpoiNTs). The authors are grateful to  George Kass  (EFSA) and  Andrea Richarz  (ECHA), who actively participated in the discussion.
On average, the simulations lead to an underestimation of the drifter and FAD velocities, likely because the spatial resolutions of the forcing products were insufficient and the time frequency at which they were produced were too low to resolve the relevant oceanic processes properly. We found a low skill for all models to simulate FAD trajectories, possibly because of the devices vertical structure that prevent FADs from drifting like water parcels. Our results therefore suggest that in the Tropical Atlantic the FAD dataset may not be appropriate to use for corroborating Lagrangian simulations.
In tropical waters, there is therefore the possibility of using trajectories of FADs, not only drifters, to corroborate Lagrangian simulations. Bedi et al. (2019)  recently investigated that possibility in the Tropical Indian Ocean, mostly. We used an extensive dataset of trajectories of drifters and FADs of the Tropical Atlantic Ocean to assess their trajectories as simulated with a Lagrangian model forced by several current products and models. Precisely, the Ichthyop Lagrangian  (Lett et al. , 2008)  was forced by interannual outputs of a CROCO hydrodynamic model conguration of the Tropical Atlantic Ocean, and also by global operational current products derived from the GlobCurrent and OSCAR programs, over the period 1997-2014 for drifters and 2008-2014 for FADs. The objective was to make a comparative analysis of the solutions CROCO-Ichthyop, GlobCurrent-Ichthyop and OSCAR-Ichthyop, using the trajectories of in situ drifters and FADs as references, and also to study the possibility of using FAD data like drifters in Lagrangian studies.
Oceanographic surface drifters The surface drifters trajectories (https://data. nodc. noaa. gov/) are gathered by the National Oceanic and Atmospheric Administration (NOAA) through the Global Drifter Program (GDP, Dataset, https://doi. org/10. 25921/7ntx-z961) (Laurindo et al. , 2017). These drifters are made up of a surface buoy and are drogued at 15 m depth. The drifters measure properties such as sea surface temperature, currents, air pressure and wind direction and send this information to passing satellites using an ARGOS transmitter  (Lumpkin and Pazos, 2007). All older drifters used ARGOS, but in 2015 more than half use Iridium; nearly all drifters are now Iridium with GPS. Their positions are given every six hours  (Hansen and Poulain, 1996; recently each hour, Elipot et al. , 2016).
The lower speed shift was less pronounced with GI for drifters. Similar results were obtained for drifters and FADs velocity uctua-    For drifters in the Tropical Atlantic, the mean skill varied much with time, 340 rising sharply in the rst days up to 0. 4 and then dropping (Fig. 8 ). The skills obtained with CI, OI and GI were comparable when calculated for the entire Tropical Atlantic dataset (Fig. 8a ) but CI performed better in the Gulf of Guinea (Fig. 8b ). The same trend was found for FADs (Fig. 9 ) but the skills were lower than for drifters, reaching a maximum value of 0. 1 only. For the drifters dataset, when particles were reset at the observed drifter locations every 10 days, the simulated mean speeds (Appendix Table  A. 1) were closer to the observed ones and the mean of the model performance over the tenday interval are similar in the Tropical Atlantic (Fig.
, 2019). The regularly gridded satellite data (GlobCurrent or OSCAR) are produced using optimal interpolation and merging techniques to ll the gaps between spatially sparse satellite ground tracks. This merging process results in the smoothing of the data in both space and time  (Ducet et al. , 2000; Meyer et al. , 2017)  and also the underestimation of the ocean current velocities. The underestimation obtained with CROCO is likely associated with insucient resolution. McClean et al. (2002)  showed that model velocities agreed better with those of drifters when the model resolution became higher. CROCO velocities were generally slightly slower than velocities of both satellite derived estimated currents (OSCAR and GlobCurrent). A possible explanation is that velocity was estimated using the straight-line distance from one point to the next.
Since the currents elds used in the work to force Ichthyop represented the mixed layer (0-30 m), their drogue structures could explain the small value of skill obtained for FADs. Imzilen et al. (2019)  already pointed that drifters moved faster than FADs in the Tropical Atlantic, unlike in the Tropical Indian Ocean. Our results reinforce that view and therefore suggest that in the Tropical Atlantic the FAD dataset may not be appropriate to use for corroborating Lagrangian simulations, because the FAD structures are such that FADs do not drift like water parcels. Phillips et al. (2019)  further suggested that changes in the transmission frequency of FAD locations can contribute to dierences between simulated and observed FAD distributions, and hence to model skills.
Acknowledgements This research is supported by the  French Institut de Recherche pour le Développement (IRD)  and  Sorbonne University  through the PDIMSC ( Programme Doctoral International de Modélisation des Systèmes Complexes ). The FAD buoy data were produced by ORTHONGEL and the shing companies CFTO, SAPMER, and SAUPIQUET. We are grateful to the  AOML's drifting buoy group  for making their drifter data available and acknowledge the  OS-CAR  and  GlobCurrent  Project Oce for providing the surface current data. We would also like to thank  Nicolas Barrier  for the plugin of GlobCurrent in Ichthyop,  Pierrick Penven  for his technical support for the CROCO model implementation, and three anonymous reviewers for their very useful comments.