dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, despite the need for some improvements, using a package for hydrological modelling within the R environment is greatly enhanced by the package follow-up maintained by a strong community of developers and users. Bugs and requests can be reported via e-mail for all the packages (see Table  8 ). Ensuring followup to help users understand package specificities and to deal with errors and misuse is a way for more reusable methods and more rigorous applications of the models. It also allows users to follow the latest developments through version controlling, thus ensuring R script durability (version controlling of airGR is accessible via the GitLab platform; WALRUS, hydromad and sacsmaR version controlling are accessible via GitHub; older versions of the four other packages are archived on the CRAN).
Acknowledgements. The authors thank  Météo-France  and the hydrometric services of the  French government (SCHAPI)  for, respectively, providing the climatic and hydrological data used to calculate the computation times. We also thank the  French National Geographic Institute (IGN)  for providing the digital elevation model data. We would like to thank  Charles Perrin ,  Vazken Andréassian ,  Maria-Helena Ramos ,  François Bourgin ,  Léonard Santos  and  Mattia Neri , for their useful remarks on, and suggestions for, the paper. Finally, we thank  Jan Seibert  (the editor),  Elena Toth ,  Anthony Ladson  and one anonymous referee for their comments that significantly improved the paper. Financial support. This work was supported by the  French Ministry of Environment  ( DGPR/SNRH/SCHAPI ), which provided financial support to the PhD grant of the first author.
Introduction BioPAX is a standard format encoding biological processes like gene regulation, metabolic pathways or signaling events, that facilitates the inter-operability between data sources and network analysis tools. However, this rich knowledge-oriented data format that finely captures the complexity of biological networks cannot be easily handled without appropriated tools. Software have been recently proposed to design, visualize  (Babur et al. , 2010; Shannon et al. , 2003) , parse  (Turei et al. , 2016) , validate  (Rodchenkov et al. , 2013) , query  (Babur et al. , 2014)  and analyze BioPAX files. However, an important missing feature to analyze BioPAX data sources is the ability to interpret BioPAX files into graph structures including the role of physical entities as substrate, product or regulator in the reactions.
An accurate format for representing the variety and complexity of the biological reactions is the concept of regulated reactions connecting regulatory, signaling and metabolic levels  (Blavy et al. , 2014). In this conceptual framework, both biochemical reactions and regulatory interactions are described homogeneously as regulated reactions involving substrates, products, activators, inhibitors and modulators as key elements. In the reaction graph generated from regulated reactions, the molecules and the reactions are represented as typed nodes, as shown in Figure  1. Thus, we propose to extend the BioPAX toolbox with a Python library able to interpret BioPAX files as graphs of regulated reactions. With PAX2GRAPHML, the graphs are represented in the. graphml format, allowing the manipulation of nodes and edges properties.
Format and package description PAX2GRAPHML is able to process all BioPAX files to generate regulated reaction graphs, which can be further interpreted into positive and negative oriented influences. It is available on pypi and as a docker image. In PAX2GRAPHML, PaxTools  (Babur et al. , 2014)  is used internally to extract sub-classes of patterns and further interpret them as regulated reactions. These extracted patterns form the building elements of a regulated reaction graph  (Blavy et al. , 2014). Each regulated reaction graph pattern is centered on a reaction node linked to one or several substrate nodes and product nodes. The reaction node can also be linked to modulator nodes (activators or inhibitors). Substrates and modulators are inputs of the reaction node, whereas products are outputs of the reaction node.
All nodes (reaction, substrate, product or modulator) are associated with their own metadata in the graph. PAX2GRAPHML is composed of four sub-packages. (i) The sub-package pax_import is dedicated to global or parametrized import of BioPAX files from Pathway Commons (PC) to be further interpreted as regulated reaction graph. (ii) The sub-package properties allow to manipulate nodes and edges properties of the generated graphs. All aliases contained in BioPAX have been incorporated in the. graphml format as node properties to represent genes, protein and compounds. Additional annotations can also be directly imported from specific files. (iii) The sub-package extract allows modifying either the generated reaction graph or the influence graph, including sub-graphs selection or graphs merging.
(iv) The subpackage graph_explore includes IO functions and analysis of the generated graphs. It also includes classical graph metrics (degree, betweenness, closeness, connected components) as preliminary steps. More sophisticated analyses can be further performed with graphtool or other advanced libraries  (Csardi and Nepusz, 2006). The PAX2GRAPHML website provides a complete documentation and the pre-processed database resources. Regulated reaction graphs and influence graphs produced from 16 data sources of PC can be downloaded as ready-to-use data for further analyses with PAX2GRAPHML. Files are automatically updated using databanks synchronization and a processing software  (Filangi et al. , 2008).
Application PAX2GRAPHML was first applied to the complete PC databank. The regulated reaction graph produced in. graphml format has a size of 363 MB (13% of the initial BioPAX file size). PAX2GRAPHML was also applied to each data source of PC considered independently. As shown in Table  1 , the regulated reaction concept used to unify the different BioPAX reaction types facilitates the comparison of the content of each resource. Notably, this revealed that Mirtarbase and CTD are the main contributors of PC in terms of nodes, edges, and especially inhibition reactions. Generating the regulated reaction graph from 16 BioPAX datasources with PAX2GRAPHML lasted 7 days on a virtual machine with 48 G RAM. Conveniently, the generated files can be downloaded on PAX2GRAPHML website as ready-to-use data resources, which is automatically updated.
Customized graphs can be produced for any subsets of the databases. To achieve this, users can either filter the overall regulated reaction graph, or can merge the regulated reaction graphs produced from two or more databases selected according to their specific interest. The two functionalities (filtering and merging) are available within the PAX2GRAPHML package. As an illustration, Table  1  shows that filtering out CTD and Mirtarbase from PC eliminates 32% of the nodes (36% of reaction nodes and 28% of entity nodes) and 74% of the edges. Table  1  also illustrates that the combination of PID with successively HumanCyc, KEGG and Reactome improves coverage of both reaction nodes (from 4495 to 10 398) and entities (from 4908 to 28 187). By managing BioPAX data extraction into regulated graphs, PAX2GRAPHML simplifies the implementation of many methods for regulation network analysis and understanding of the controlling steps of the biological pathways.
Financial Support: none declared. Note: Single datasources transformations were performed with the sub-package pax_import. Combination of several datasources was performed by filtering the PC* all sources graphml file with the sub-package extract. Nodes are either reactions or entities (proteins, small molecules, etc. ). The numbers of regulated reactions computed by PAX2GRAPHML, together with the number of substrates, products, activators, inhibitors, are indicated. All these graphs can be directly downloaded from the PAX2GRAPHML website. *'PC' version 12, September 2019. **KEGG, July 2011 (only human, hsa* files).
Methods. The optimisation procedure consists of three main steps: (i) definition of the optimal set of parameters for the PSFsubtraction techniques using the contrast as performance metric, (ii) optimisation of the RSM algorithm, and (iii) selection of the optimal set of PSF-subtraction techniques and ADI sequences used to generate the final RSM probability map. Results. The optimisation procedure is applied to the data sets of the exoplanet imaging data challenge (EIDC), which provides tools to compare the performance of HCI data-processing techniques. The data sets consist of ADI sequences obtained with three stateof-the-art HCI instruments: SPHERE, NIRC2, and LMIRCam. The results of our analysis demonstrate the interest of the proposed optimisation procedure, with better performance metrics compared to the earlier version of RSM, as well as to other HCI dataprocessing techniques.
The performance of both optimisation procedures is assessed using the exoplanet imaging data challenge (EIDC), which regroups ADI sequences generated by three state-of-the-art HCI instruments: SPHERE, NIRC2, and LMIRCam. The aim of the EIDC initiative is to provide tools, i. e. , the data sets and performance metrics, to properly compare the various HCI data processing techniques that have been developed recently. The EIDC first phase, which ended in October 2020, regrouped and compared the results from 23 different submissions for the ADI subchallenge  (Cantalloube et al. 2020) , making it a great tool to assess the performance of a new approach. The remainder of this paper is organised as follows. Section 2 describes the procedure used to optimise the parameters of the PSF-subtraction techniques.
Practical implementation After having presented the different steps of the proposed optimisation framework for the RSM map algorithm, these steps can now be merged into a single optimisation procedure, which is implemented in the PyRSM package 7. Two different modes of this optimisation procedure are proposed: the full-frame mode and the annular mode. The two modes share a common structure but their output dependence on the angular separation is different. In the full-frame mode, there is no dependence between the optimal set of parameters and the angular separation to the host star, with a single set of parameters being used for every annulus. In the annular mode, the frames are divided into successive annuli of pre-defined width, and a set of optimal parameters is defined for each annulus.
Description of the data sets As mentioned in the introduction, we base our performance analysis on the data set of the EIDC ADI subchallenge  (Cantalloube et al. 2020). This data set regroups nine ADI sequences, three for each considered HCI instrument, namely VLT/SPHERE-IRDIS  (Beuzit et al. 2019) , Keck/NIRC2  (Serabyn et al. 2017) , and LBT/LMIRCam  (Skrutskie et al. 2010). The ADI sequences were obtained in H2-band for SPHERE and Lp-band for the two other instruments. For each ADI sequence, a set of four fits files is provided: the temporal cube of images, the parallactic angles variation corrected from true north, a non-coronagraphic or nonsaturated PSF of the instrument, and the pixel scale of the detector. The ADI sequences are pre-reduced using the dedicated pre-processing pipeline for the three instruments (more details about the reduction are provided by  Cantalloube et al.
More details about the nine ADI sequences are provided in Table  F. 1. To assess the performance of the HCI data-processing techniques, fake companions were injected by the EIDC organisers using the VIP package (Gomez  Gonzalez et al. 2017 ). Between 0 and 5 point sources were injected into each ADI sequence for a total of 20 planetary signals within the entire EIDC ADI subchallenge data set. These point sources were injected using the opposite parallactic angles, avoiding any interference with potential existing planetary signals while keeping the speckle noise statistics 14. The separation, the azimuth, and the contrast of the injected fake companions were chosen randomly. The contrasts range between 2σ and 8σ based on a contrast curve computed with the regular annular PCA implemented in the VIP package (Gomez  Gonzalez et al.
Results We have now all the elements to apply the auto-RSM optimisation procedure described in Sects. 2 and 3 to the nine selected ADI sequences. Only PSF-subtraction techniques relying on an off-axis PSF (and not on forward models) are considered during the optimisation procedure in order to reduce the computation time, considering the large set of ADI sequences on which the method is tested, and the numerous parametrisations of the auto-RSM algorithm that we consider. This also allows a fair comparison with the results of the RSM algorithm already used in  Cantalloube et al. (2020) , which relied only on the PSFsubtraction techniques based on off-axis PSFs. We note however that the PyRSM Python package also accommodates the use of a forward-model version of KLIP and LOCI, where a parameter defines the maximum angular separation above which the forward model is no longer considered.
As can be seen from Fig. 4 , the contrast between detected targets and background residual probabilities is very high compared to standard S/N maps, demonstrating the ability of the proposed approach to easily disentangle planetary signals from residual speckles and ease the selection of a detection threshold. As an illustration, the ratio between the peak probability (or S/N) of the target and the mean of the background probabilities (or S/Ns) in the detection map of the SPHERE 1 data set is larger than 3000 for the auto-RSM FF_BU_F, and only 2 for the baseline 17. Following the EIDC procedure, a single threshold was selected for all data sets, for each parametrisation of the auto-RSM. This threshold allows estimation of the F1 score. As mentioned in Sect. 4. 2, in addition to the F1 score, the AUCs of the TPR, FPR, and FDR are also computed.
The different rankings for the four considered performance metrics are shown in Fig. 6. The light, medium, and dark colours correspond to the three instruments, with the VLT/SPHERE-IRDIS, Keck/NIRC2, and LBT/LMIRCam data sets, respectively. Figure  6  highlights the fact that the RSMbased approaches largely outperform the baseline with much higher F1 scores, a much larger AUC of the TPR, and much lower AUCs of the FDR and FPR. Regarding the five considered auto-RSM parametrisations, they all present a smaller F1 score compared to the RSM algorithm parametrised manually, except for the auto-RSM FF_BU_F, which performs slightly better. However, when considering the other performance metrics, the auto-RSM approach seems to perform better in most cases, especially when considering false positives.
2020). We observe an overall reduction of 76% and 33% compared to the RSM submission for the AUC of the TPR and the AUC of the FDR, respectively, as well as an increase of 19% and 2% for the AUC of the TPR and the F1 score, respectively. Fig. 5 : True positive rate (green), false discovery rate (red) and false positive rate (dash-dotted blue line) computed for a range of thresholds varying from zero to twice the selected threshold (represented by a dotted vertical line). These curves are computed for the nine data sets of the EIDC, relying on the detection maps estimated with the full-frame version of auto-RSM using the bottom-up approach for the selection of the optimal set of cubes of likelihoods, as well as the original forward approach for the computation of the probabilities. The green line representing the TPR should be as close as possible to 1 for the entire range of thresholds, while the red and dash-dotted blue line representing respectively the FDR and the FPR, should be as close as possible to zero.
FF stands for full-frame, A for annular, AFF for annular full-frame (annular approach used to optimise the PSF-subtraction parameters and full-frame approach used for the RSM parameter optimisation and the selection of the optimal set of cubes of likelihoods), and BU, TD, F, and FB as explained in Sect. 4. 3. 1. The light, medium, and dark colours correspond to VLT/SPHERE-IRDIS, Keck/NIRC2, and LBT/LMIRCam data sets, respectively. As the EIDC data set contains multiple ADI sequences generated with different instruments under different observing conditions and with different characteristics (see F. 1 for the frames number, FOV rotation), it should allow us to estimate the homogeneity of the parametrisations for a common instrument, and potential heterogeneity between instruments. As the full-frame version of the optimisation algorithm provides the best performance, we rely on the set of parameters generated by this mode to conduct our analysis.
As expected, the NIRC2-2 is not in the same group as the other sequences generated with the NIRC2 instrument (see Fig. 7  and Table  2 ). For the other sequences, we reach the same conclusion as before, apart from the fact that both SPHERE and LMIRCam data sets are regrouped into a single cluster whose centre is close to the SPHERE-1 ADI sequence. Increasing the number of clusters does not lead to clear separation between the SPHERE and LMIRCam instruments, demonstrating the similarity of the ADI sequences generated by both instruments. We eventually tested the feasibility of using a single set of parameters for a given instrument, allowing us to investigate the sensitivity of the detection map estimation to the parametrisation. We selected the optimal parametrisation of the SPHERE-1 data set as this parametrisation is the closest to the centre of the SPHERE-LMIRCam cluster, and estimated the detection maps for the SPHERE-2 and SPHERE-3 data sets.
These curves are computed for the SPHERE-2 and SPHERE-3 data sets of the EIDC, relying on the detection maps estimated with the SPHERE-1 optimal set of parameters along with the full-frame version of auto-RSM using the bottom-up approach. Considering all these results, the use of a single set of parameters for large surveys seems feasible in most cases, especially for the SPHERE and LMIRCam instruments. However, as demonstrated by the ADI sequences generated by NIRC2, when large dissimilarities are observed in terms of background noise level, a more refined subdivision should be considered.
Different parametrisations of the full-frame and annular auto-RSM are tested to investigate the added value of different methods to select the optimal set of cubes of likelihoods or to compute the final probabilities. The data sets of the EIDC and the performance assessment framework proposed in  Cantalloube et al. (2020)  are used to compare the performance of the different versions and parametrisations of the auto-RSM. The performance assessment is performed via the computation of a data set-dependant F1 score at a predefined threshold, as well as the estimation of the AUC of the TPR, FPR, and FDR. The auto-RSM results demonstrate the interest of the approach: in most cases, it provides better performance than the original RSM-detection map submitted to the EIDC, while the original RSM map was already at or close to the top of the ranking for all performance metrics in the EIDC.
The full-frame auto-RSM using the bottom-up approach to select the optimal set of cubes of likelihood and the forward approach to compute the RSM probabilities provides the best overall performance in terms of detection. Considering the longer computation time and lower performance of the annular version, the full-frame auto-RSM should be preferred. As auto-RSM is computationally expensive even when using the full-frame version, we investigate the possibility of using a common set of parameters for each instrument. We studied the commonalities existing between the parametrisations of the nine data sets of the EIDC, and found that the distance between the parametrisations for a common instrument is smaller than the distance between the parametrisations of different instruments. Potential differences between the noise characteristics of different data sets generated with a common instrument should nevertheless be taken into account, as illustrated by the NIRC2 data sets.
However, the use of a single parametrisation (or a limited number of them) for large surveys seems possible. The auto-RSM framework is not limited to the RSM algorithm and the first step of the algorithm may be used separately to optimise the parametrisation of PSF-subtraction techniques and generate S/N maps. A S/N version of the proposed optimisation framework, called auto-S/N, was developed. Despite the degraded performance compared to auto-RSM, auto-S/N is characterised by a reduced computation time and can sometimes be a good complement to auto-RSM. All these versions of the pro-posed optimisation framework are available in a single Python package called PyRSM. This package offers a parameter-free detection map computation algorithm with a very low level of residual speckles, especially for the auto-RSM, allowing a simple detection threshold selection.
In addition to the planned public data releases, the DES Collaboration prepares incremental releases with value-added products and detailed characterizations of survey performance that are designed to support cosmological analyses (e. g. , the "Gold" catalogs;  Drlica-Wagner et al. 2018; Sevilla-Noarbe et al. 2021). The value-added products associated with the DES Science Verification (SV) period (2012 November 1 through 2013 February 22) were released in 2016 January,  69  those associated with selected DES Y1 publications in September 2018  70  , and those supporting the DES Y3 cosmological analyses are expected in 2021. Future releases are expected to support the Y6 scientific publications. Here we present the content, validation, and data access services for the second public DES data release, DES DR2.
DES DR2 comprises coadded images of size 0. 534 deg 2 and source catalogs, as well as calibrated single-epoch CCD images, from the processing of all six years of DES widearea survey observations and all five years of DES supernova survey observations. DES DR2 contains roughly 18 TB of coadd images and 3 TB of tables. All DES DR1 data have been reprocessed as input into DES DR2. Access to DES DR2 data is provided via web interfaces and auxiliary tools, which are made possible through the partnership between NCSA, the Laboratório Interinstitucional de e-Astronomia (LIneA), and NOIRLab. The primary landing page for DES DR2 data products is https://des. ncsa. illinois. edu/releases/dr2. In Section 2 we briefly summarize DES operations and data taking. In Section 3, we summarize the DES data processing and describe pipeline changes from DES DR1 to DES DR2.
Basic validation of DES DR2 is presented in Section 4. Section 5 describes the products, data access frameworks, and tools made available for DES DR2. A summary of the release is given in Section 6. The Appendices present photometric transformation equations between DES DR2 and other largearea surveys, details on the absolute photometric calibration, data processing parameters, and a detailed description of the data products. All magnitudes quoted in the text are in the AB system  (Oke 1974) , and all astronomical coordinates are provided in the Gaia-CRF2 reference frame (Gaia Collaboration 2018). All quoted uncertainties are 1σ (roughly 68% confidence interval) unless indicated otherwise.