dataesr/scientific-paragraphs-categorization huggingface dataset:
Source detection and photometry Source extraction and photometric analyses are two crucial steps to optimize the detection of faint sources in deep and crowded regions and to estimate the colors required for the photometric redshifts. As mentioned by  Weaver et al. (2022) , photometric redshift estimates are more sensitive to the input flux catalogs than to the photo-z codes themselves. Hereafter we perform these two steps with two different codes: the one implemented in hscPipe  (Bosch et al. 2018) , which has been adapted to handle images from other facilities (e. g. , U and NIR images), and the well tested and widely used SExtractor software  (Bertin & Arnouts 1996 ). In the following part we describe the main steps followed by each software to generate the multiband catalogs and we compare the photometry obtained by the two catalogs as well as with the external catalog from COSMOS2020  (Weaver et al.
Sources identified by the hscPipe as duplicated are removed. The masks for bright stars used by the pipeline are the same as those used in  Aihara et al. (2019)  and thus suffer from the same issues as discussed in Sect. 2. 4. 1. We thus apply the updated star masks presented in Sect. 2. 4 as well as the satellite trail masks using the VENICE code  (Coupon et al. 2018). We check the consistency of our photometry with that obtained in  Aihara et al. (2019)  for the HSC PDR2 release. This comparison is done in the center of the E-COSMOS field where all the bands are available and where pipeline issues are most likely to happen, in particular in the processing of non-HSC data. The photometry of the hscPipe is in excellent agreement with the HSC PDR2 one. No bias is observed with magnitudes, and the scatter remains below ∼0.
SExtractor We also produce a version of the HSC-CLAUDS catalogs with the detection and photometry based on the SExtractor software  (Bertin & Arnouts 1996). The source detection is performed on a detection image constructed by a nonlinear combination of the U + grizy bands. When near-infrared observations are available, the detection image also includes the K s band. To compute the detection image we adopt the χ mean combination proposed by  Drlica-Wagner et al. (2018)  as an alternative to the standard χ 2 image  (Szalay et al. 1999) , because it minimizes the discontinuities between regions with a different number of input images. The detection image, χ mean , is defined as : χ mean = i≤n f 2 i σ 2 i -µ n -µ 2 , ( 2 ) with µ = √ 2 Γ((n + 1)/2) Γ(n/2) and f i is the image in the band i, σ 2 i its variance and n the number of input images used in the combination.
The χ mean detection images are shown in Fig. 2. Before constructing the χ mean images, we first checked the homogenization of the variance maps, as the procedures used to generate the images and their associated weight maps are different for the different datasets (CLAUDS, HSC, VIDEO or UltraVISTA). To ensure that the weight map properly scales as a variance map, we measured the standard deviation, σ(Im), of 5000 randomly thrown 2 ′′ apertures in the background of the image, Im, and compared with the median value, median(Var), A82, page 6 of 25 obtained in its associated variance map, Var. For each patch and passband, we rescaled the variance map by the factor: σ 2 (Im) /median(Var). For CLAUDS and HSC bands, the scaling factors are around ∼2. 5-3, which means that without the rescaling we would underestimate the photometric errors by a factor ∼1.
Comparison and quality assessment The HSC-CLAUDS photometry is assessed by comparing the measurements of our hscPipe and SExtractor catalogs. We  also compare to a third and distinct method, delivered in the COSMOS2020 catalog  (Weaver et al. 2022) , based on Farmer software  (Weaver et al. , in prep. )  which uses The Tractor  (Lang et al. 2016)  to measure the photometry. Farmer performs model photometry of individual sources with a simultaneous optimization of a group of nearby sources with additional constraints from the multi-channels. The COSMOS2020 catalog uses, among others, the same CLAUDS, HSC and UltraVISTA data as we did. The comparison is thus performed in the central E-COSMOS field, which is also the only region where all filters are available. In Figs. 4, 5 and 6, we compare the magnitudes.
Photometric redshifts The photometric redshifts (photo-zs) are estimated with two template-fitting codes for the two photometric catalogs based on the 2 ′′ aperture flux in all bands. The hscPipe catalog is processed with the Phosphoros code, created to be part of the Euclid photo-z pipeline (Paltani et al. , in prep. ; Euclid Collaboration 2020), while the SExtractor catalog is processed with the well-tested code, Le Phare  (Arnouts et al. 2002; Ilbert et al. 2006). As this is the first application of Phosphoros on deep imaging surveys destined to be released, we also run Phosphoros on SExtractor photometry to distinguish the differences related to photometry issues from template-fitting code issues; this is detailed in Appendix D. For both codes, several configurations were considered and tested.
The PDZs are defined with a fixed redshift grid (z = 0-6) with a step δz = 0. 01. The photo-zs point estimates are defined as the median values of the PDZs. To deliver a consistent dataset across the entire HSC-CLAUDS survey, we provide a complete set of photo-zs with their PDZs based on the Ugrizy photometry alone, and an additional set based on UgrizyY JHK s in the regions where near-infrared bands are available. We also provide a star/galaxy separation by fitting the sources with stellar templates from the Pickles (1998) library. In the catalog, all object with χ 2 star < χ 2 gal are flagged as potential stars. Objects that are both potential stars and compact according to Eq. (  1 ) are flagged as stars.
To assess the relevance of the errors, for each source we compute the absolute scaled residual, D z (Eq. (  6 ), Sect. 4. 3. 1). Figure  13  presents the cumulative distribution of D z for the two implementations (HPH and SLP) and the two imaging datasets (optical, optical+NIR). If the photo-z uncertainties are correct we expect the cumulative curves to reach the 68% level at D(z) = 1. Indeed, all the cumulative distributions cross the 68% level at higher D(z), suggesting that the estimated photo-z uncertainties are underestimated. In both configurations, the effect seems even worse when the near-infrared photometry is included, probably because of the additional constraints it provides on the PDZs. Despite the fact that scaling factors have been applied to enlarge the photometric errors (see Sects.
4. 1 and 4. 2), they seem insufficient to provide realistic photo-z uncertainties. We note that SLP configuration seems to provide more reliable error estimates than HPH. As it is still the case when Phosphoros is run on SExtractor photometry, this difference is probably due to the code configurations, and in particular the rescaling of the errors, and not only to the photometry. We, therefore, caution A82, page 14 of 25 that the current photometric-redshift uncertainties are on average underestimated. Alternatively, instead of the 68% confidence interval, we can directly estimate the quality of the PDZs with a PIT plot (see  Sect. 4. 3. 1). In Fig. 14 , we show the histogram of the HPH C i values. The concave shape of the histograms shows that the PDZs are too narrow, leading to an underestimation of the errors.
This catalog will also benefit from source physical parameters computed in a similar way as for the COSMOS2020 catalogs  (Picouet et al. , in prep. ). The HPH catalog provides different types of photometry, not present in the SLP one. For instance, it includes cmodel photometry and PSF photometry, that take into account PSF variation across the fields. On the photo-z side, it includes individual PDZ for each object in the catalog. Another interesting feature it the similarities between the HSC pipeline and the LSST pipeline. With its depth and wavelength coverage, the CLAUDS+HSC combination is close to the expected LSST ten-year dataset  (Ivezić et al. 2019). Thus, the HPH catalog is most similar to the future LSST data. The catalogs presented in this work will be publicly released and available for download on several sites 8.
Acknowledgements. We thank the anonymous referee for providing helpful comments and suggestions that improved the quality of this work. GD acknowledges the support from the  Sinergia program  of the  Swiss National Science Foundation. This work was supported by the  Spin(e) ANR  project ( ANR-13-BS05-0005 ), the  DEEPDIP ANR  project ( ANR-19-CE31-0023 ), and by the  Programme National Cosmology et Galaxies (PNCG) of CNRS/INSU  with INP and  IN2P3 , co-funded by  CEA  and  CNES. M. S. acknowledges funding support from the  Natural Sciences and Engineering Research Council (NSERC)  of  Canada Discovery Grant and   Discovery Accelerator programs , and from the  Canada Research Chairs program. C. C. is supported by the  National Natural Science Foundation of China , Nos. 11803044 ,  11933003 ,  12173045 , and in part by the  National Key R&D Program of China  grant  2017YFA0402704.
C. C. acknowledges the science research grants from the  China Manned Space  Project with No. CMS-CSST-2021-A05. This work is sponsored (in part) by the  Chinese Academy of Sciences (CAS) , through a grant to the  CAS South America Center for Astronomy (CASSACA). The  Cosmic Dawn Center  is funded by the  Danish National Research Foundation  under Grant No. 140. JRW and ST acknowledge support from the  European Research Council (ERC)   Consolidator Grant  funding scheme (project  ConTExt , Grant No. 648179 ). These data were obtained and processed as part of the  CFHT Large Area U-band Deep Survey (CLAUDS) , which is a collaboration between astronomers from  Canada, France , and China described in Sawicki et al. (2019). CLAUDS is based on observations obtained with MegaPrime/ MegaCam, a joint project of  CFHT  and  CEA/DAPNIA , at the CFHT which is operated by the  National Research Council (NRC) of Canada , the  Institut National des Science de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii.
Funding was contributed by the  FIRST program  from  Japanese Cabinet Office , the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) , the  Japan Society for the Promotion of Science (JSPS) ,  Japan Science and Technology Agency (JST) , the  Toray Science Foundation ,  NAOJ ,  Kavli IPMU ,  KEK ,  ASIAA , and  Princeton University. This paper makes use of software developed for the Large Synoptic Survey Telescope. We thank the  LSST  Project for making their code available as free software at http://dm. lsst. org. The Pan-STARRS1 Surveys (PS1) have been made possible through contributions of the  Institute for Astronomy , the  University of Hawaii , the  Pan-STARRS Project Office , the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh, Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No.
NNX08AR22G  issued through the  Planetary Science Division  of the  NASA  Science Mission Directorate, the National Science Foundation under Grant No. AST-1238877, the University of Maryland, and Eotvos Lorand University (ELTE), and the Los Alamos National Laboratory. This work is based on data products from observations made with ESO Telescopes at the La Silla Paranal Observatory under ESO program ID 179. A-2005 and on data products produced by CALET and the Cambridge Astronomy Survey Unit on behalf of the UltraV-ISTA consortium. Based on data products from observations made with ESO Telescopes at the La Silla Paranal Observatory as part of the VISTA Deep Extragalactic Observations (VIDEO) survey, under program ID 179. A-2006 (PI: Jarvis). A82 , page  19  of  25 A&A 670 , A82 (2023).
Omics technologies has opened new possibilities to assess environmental risks and to understand the mode(s) of action of pollutants. Coupled to dose-response experimental designs, they allow a non-targeted assessment of organism responses at the molecular level along an exposure gradient. However, describing the dose-response relationships on such highthroughput data is no easy task. In a first part, we review the software available for this purpose, and their main features. We set out arguments on some statistical and modeling choices we have made while developing DRomics (an R package released in its first version in 2019) and its positioning compared to others tools. The DRomics main analysis workflow is made available through a web interface, namely a shiny app named DRomics-shiny. Next, we present the new functionalities recently implemented.
, 2019 , Murat El Houdigui et al. , 2019 , Meier et al. , 2020 , Zhan et al. , 2021). In a second time, some perform a DR modeling on differentially expressed items to estimate a BMD per item and summarize the sensitivity of each pathway for example by the median of BMDs of corresponding pathways  (Meier et al. , 2020 , Zhan et al. , 2021). The purpose of this paper is to present the new version of the DRomics ("Dose Response for Omics") R package and its two companion interactive web applications, DRomics-shiny and DRomicsInterpreter-shiny. We first review the software available to tackle a DR analysis of high-throughput omic data and explain how DRomics distinguishes from other tools. Then, we present the various functionalities we added to DRomics from its first version published in 2018 and especially explain the way it can be used to make sense of DR (multi-)omics data in environmental risk assessment.
, 2007). It was since updated and augmented in  BMDExpress-2 (Philips et al. , 2019). FastBMD  (Ewald et al. , 2021)  implements the same methods, as framed by the US National Toxicology Program (NTP, 2018), with the sake of being faster and friendlier to end-users than BMDExpress, and through a web-based interface. Again in the context of toxicogenomics, BBMD  (Ji et al. , 2022)  has the specificity to use model averaging to account for BMD uncertainty related to the underlying DR model, and BMDx  (Serra et al. , 2020)  allows a comparison of BMD values of a transcriptomics experiment at different time points or from different experiments. We developed DRomics in 2018  (Larras et al. , 2018)  for DR analysis of any omics data (e. g. transcriptomics, metabolomics), including non-sequenced organisms or communities (meta-omics), biological models commonly used in the field of ecotoxicology.
The main characteristics and functionalities of those five tools are summarized in Table  1. DRomics is the only tool which is composed of both an R package and a free web application (see Table  1 ). We chose to develop it in the R language among others i) to facilitate the interoperability with the Bioconductor packages that implement state-of-the-art bioinformatics methods and ii) to add a Shiny application (interactive web app. straight from R) that can be launched both from the package and from a server freely accessible without registration. The companion DRomics-shiny application was thought for users who do not want to work in the R environment, but also to help new users to take the package in hand. For that purpose, the R code of the whole performed analysis is provided in the last page of the Shiny application.
r-project. org/web/packages/DRomics/vignettes/DRomics_vignette. html#models or  Larras et al. , 2018). BMDExpress and DRomics results were compared in the case study of a microarray dataset, as reported in the supporting information of  Larras et al. (2018). DRomics models were shown to give a better description of data (smaller AIC values) and more repeatable and conservative BMD estimations for biphasic responses. Unlike the other tools (Table  1 ), DRomics does not only provide a BMD estimation, but also a characterization of the DR response in four classes (increasing, decreasing, U-shape, bell-shape) which we thought may be of great interest in an AOP perspective, when the DR analysis not only aims at defining BMD values but also at making sense of DR (multi-)omics data in environmental risk assessment.
New features in the DRomics DR modeling workflow Figure  1  maps the DRomics workflow and the functionalities offered to explore the results. First developed for microarray data, DRomics is now able to handle RNAseq data, metabolomic or other continuous omic data (e. g. proteomics data) or even continuous non omic data (e. g. growth data) that could be used for phenotypical anchoring (respectively imported using RNAseqdata(), continuousomicdata() and continuousanchoringdata() functions). The same modeling workflow, choosing the best fit models among our complete family of models (as described previously), was declined for each type of data (e. g. intensities, counts) to ensure the comparability of results (e. g. transcriptomics vs. metabolomics, transcriptomics vs. anchoring). Writing in the R language ensures the interoperability with functions of the Bioconductor packages.
Thus, functions of the DESeq2 and limma packages are internally called within the package to normalize and/or transform omic data and to implement the trend tests for selecting significantly responsive items. The call to additional R functions can be added for example to correct omics data for a potential batch effect (see an example using ComBat-seq in the package vignette: https://cran. r-project. org/web/packages/DRomics/vignettes/DRomics_vignette. html#insitudata). Various functions were also added to the package (highlighted in bold type in Figure  1 ) to respond to user requests. Among them we can cite PCAdataplot() for a visualization of data after the importation step and detection of potential outlier samples or batch effect, targetplot() to visualize the response of targeted items whatever they are selected or not in the DRomics workflow, bmdboot() and bmdplot() to compute and visualize confidence intervals on BMD values using bootstrap.
New functions and the new Shiny application to help biological interpretation In toxicology, while working on sequenced and well-annotated organisms, items (e. g. genes) can be functionally annotated prior to DR analysis. BMDExpress integrates this annotation step for 6 model species on the basis of GO or reactome databases, and FastBMD for 14 model species on the basis of GeneID, GO, Reactome or KEGG databases (see Table  1 ). Such an annotation of all the items whatever they respond or not to the dose gradient exposition, enables the classical enrichment analysis  (Wu et al. , 2021). This analysis consists in highlighting gene sets/pathways which are the most overrepresented among the responding ones. In ecotoxicology, one often works on non-model organisms or even on samples from environmental communities (freshwater biofilm for example -  Creusot et al.
 for selected groups/pathways, all the DR curves represented as curves (using the curvesplot() function, see a multi-level example on Figure  3 , right part), Those functions can also be used to compare the response at one molecular level but measured under different experimental conditions (different time points, different pre-exposure scenarios, in vitro/in vivo, etc. ). The selectgroups() function was also developed to help the user to focus its interpretation on the most represented and/or the most sensitive biological groups (see an example of use in the package vignette: https://cran. rproject. org/web/packages/DRomics/vignettes/DRomics_vignette. html#selectgroups). The signal was shifted by putting the initial theoretical value at 0 and scaled so as to make it vary in the same amplitude to focus on the shape of the responses and not on the amplitude of the signal (it is an option of both functions).
Perspectives In the report of the National Toxicology Program (NTP, 2018) model averaging is mentioned as an interesting feature to make the BMD estimate less dependent of the choice of the best DR model. BBMD  (Ji et al. , 2022)  and the next version of BMDExpress in preparation (BMDExpress-3https://github. com/auerbachs/BMDExpress-3/) include a Bayesian model averaging procedure. However, we do not plan to implement model averaging in DRomics because the BMD estimation is not our only purpose. We also want to characterize each response by its trend, which is itself dependent of the model choice and not averageable. Instead, we plan to add an alternative to our current bootstrap procedure, enabling the fit of a different model at each bootstrap iteration, to be able to pass the uncertainty due to the model choice both on the BMD uncertainty and on the trend uncertainty.
Funding This work has been financially supported by the  Graduate School H2O'Lyon  ( ANR-17-EURE-0018 ) of  Université de Lyon (UdL) , within the program " Investissements d'Avenir " operated by the  French National Research Agency (ANR) ", by the " Ecosphère continentale et côtière" (EC2CO) interdisciplinary program  from the  Centre National de la Recherche Scientifique (CNRS, France) , as parts of the  DROMADERE  and the  ECORA  projects, and by the  " Agence Nationale de la Recherche " as a part of the  Chroco project  ( ANR-21-CE34-0003 ).
Date of last revision (as on 02/02/2023) 2023 2020 BMDExpress 3 in prep. https://github. com/auerbac 2022 2022 2022 hs/BMDExpress-3 Dependencies to other statistical R packages limma, DESeq2 (Bioconductor packages) not mentionned limma, vsn, edgeR, DESeq2, genefilter and preprocessCore (Bioconductor packages) not mentionned drc, bmd, alr3, jtools (alr3 and bmd no more available on CRAN) Import/ Visualisation of data.
Selection of significant responses to the dose gradient yes yes yes yes yes Filter on the fold change for selection Shapes of proposed models monotonic and biphasic monotonic, biphasic, multiphasic (3rd and 4th order polynomials) monotonic, biphasic, multiphasic (3rd and 4th order polynomials) monotonic (Linear, Hill, Pow, Exp2, Exp3, Exp4, Exp5) monotonic, biphasic, multiphasic (3rd order polynomials) User selection of models No yes yes yes yes Dose-response modeling Proposed criterion for best fit model choice Characterization of the response AICc (default), AIC, BIC increasing (up-regulated), decreasing (down-regulated), U-shape, bell- AIC and comparison of nested models for polynomial models none AIC none Model averaging increasing, decreasing AIC none shape Toxicity threshold provided BMD (-zSD and -xfold) BMD-zSD BMD-zSD BMD (-zSD or -xfold) BMD-zSD Method for computing confidence intervals on the toxicity threshold bootstrap based on the likelihood but not precisely described profile likelihood method model averaging not found Includes the annotation step No yes (6 species) yes (14 species) yes (6 species) yes (3 species) Biological interpretation Annotation available databases none GO, reactome KEGG, GO (BP, MF, CC), reactome GeneID, GO, reactome, KEGG KEGG, reactome, GO No optional (by default yes, > 2) optional (by default yes > 1) yes mandatory (> 1.