dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, a menu My workspace summarizes the current state of the datasets, analyses and dependencies between them by an interactive directed acyclic graph (DAG). It also includes: • a list of all datasets currently available, which includes the possibility to remove some, along with all the analyses that have been performed on them, to export a selection (in CSV or RDA), or to add new ones. During importation, the user can specify if the dataset corresponds to a specific type of omic (e. g. , "Sequencing data (RNA-seq, miRNA,. ) counts" or "metagenomics composition (compositional data)") and if these data have been previously normalized or log-transformed. These choices automatically drive sound options in the edition, normalization, or differential analysis menus, with understandable warnings or errors if the user does not perform the best analysis corresponding to what he/she has declared during importation (e.
In the different screens, a question mark button points to a documentation explaining how to set options and how to interpret results of the different screens, with short texts, understandable plots or short videos. This documentation is also gathered in a single document available at https:// aster ics. pages. mia. inra. fr/ user_ docum entat ion/ that provides descriptions of the functions and methods used in the backend to perform the analyses, along with the way the options have been set for these functions. The user documentation includes two case studies, one using the example datasets available in the interface 2 and the other using an in-house dataset obtained to study piglet survival  [31] [32] [33]. 3.
Here, we perform an analysis of three metabolomics datasets, respectively obtained on plasma, urine, and amniotic fluid samples of 444 piglets, which is an extension of the study of  [33]. The workspace of the case study is made available under UID piglet_usecase on ASTERICS. 4 Datasets are described in  [33]  and correspond to a metabolomic analysis performed with 1 H NMR technique. Raw data ( 1 H NMR spectra) are available in the metabolights database  [34]  under accession number MTBLS1541. Spectra were processed with the ASICS package to identify and quantify metabolites as described in  [33]  and metabolite quantifications together with a metadata dataset containing information on the design of the experiment have been made available at https:// doi. org/ 10. 57745/ TCKSTD.
Dataset importation and correction From My workspace, the four datasets (three metabolite quantifications and information on the experimental design) are imported from CSV files using the button "Add" in the panel "All datasets". For metabolite quantifications, the original datasets were transposed during the upload to stick to ASTERICS requirements that the individuals are in rows. In the experimental design file, one of the variable (N_mother, reporting the mother identifier) was encoded as numerical in the CSV file. The menu "Edit/Dataset edition" allows to correct that problem, using the option "Change variable (column) types". These operations resulted in the workflow DAG of Fig. 2 , as obtained from the menu My workspace. The four imported datasets are displayed in blue in rectangular nodes, whereas the corrected design of the experiment is displayed in white, also in a rectangular node.
The edition process is also displayed in white, with a round node, as for all the analyses. This simple representation allows to easily understand the relationships between datasets and analyses. Fig. 2  Workflow from the My workspace menu, as obtained after the importation of the four CSV files and the change of a variable type in the last dataset "Metadata" (that contains the uploaded data describing the design of the experiment) 4 Directly accessible at https:// aster ics. miat. inrae. fr/ works pace/ piglet_ useca se.
Exploratory analysis of the design of the experiment First, simple exploratory analyses were first performed to understand the structure of the metadata file. Using the menu "Explore/Explore variables in a dataset/All variables in a dataset", the content of the design of the experiment dataset (named Metadata_Clean) was summarized. From My workspace, the results can be obtained by simply clicking of the node corresponding to the analysis "UnivariateDataset_1" and further on the button "More" in the panel that opens on this action. For numerical variables, statistical summaries (min, quartiles, mean, maximum, standard deviation, number of missing values, number of unique values) are displayed in a table and the distributions are represented by boxplots (that can be scaled or not, to account for possible differences in scales between the numeric variables).
Relation between 3-fluid metabolome and stage of gestation The last part of the analysis consisted in the analysis of the stage of gestation effect on the metabolome. Contrary to what was performed in  [33]  to address this question (PLS-DA analyses performed independently on each of the fluid), we performed an integrated analysis combining the three fluids using MFA. The effect of the stage of gestation ("Age") was further investigated on plasma metabolome using a differential analysis. The menu "Integrate/Integrate dataset with MFA" allowed us to perform a MFA for the three metabolomics datasets. The individual plot (first two axes of the MFA with variables "Age" and "TG_F" superimposed as colors and shapes respectively, Fig. 3 , top) showed a good discrimination of the stage of gestation on the first axis but not of the fetus genotype "TG_F" (even interactively removing the mixed genotypes from the plot as shown on the right).
The menu "Explore/Explore variable in a dataset/2 variables" can be used to further visualize the impact of the stage of gestation on metabolites found important to drive the first axis of the MFA. For instance, used on valine in plasma and amniotic fluid, this menu generates the graphics in Fig. 4 , which confirms the findings of the MFA and shows, in addition, that valine is much more concentrated in plasma than in amniotic fluid. Since valine was also not detected in urine, these results tend to indicate that valine, which is not excreted, is used by the fetus in late gestation. Note that both plots can be saved using the corresponding icon for further inclusion in exported report (see next section). Finally, using the menu "Integrate/Differential analysis", a differential analysis of the plasma metabolome with respect to the day of gestation ("Age") is performed with nonparametric tests (correction for multiple test performed with the Benjamini-Hochberg procedure  [37] ).
The panel "Extract dataset" in this workflow allowed us to export downand over-concentrated metabolites found differential for this analysis (FDR < 5%). These results confirm that (as already discussed before) valine is significantly more concentrated in plasma at 110 days of gestation (adjusted p-value = 1. 674e-52). Detailed results can be exported as CSV files from My workspace and are made available for the reader in the data repository https:// doi. org/ 10. 57745/ TCKSTD. Using these results in MetaboAnalyst 5. 0 https:// www. metab oanal yst. ca/  [38] , we also confirmed the Galactose metabolism pathway as the most significant pathway for metabolites downconcentrated at 110 days of gestation. This confirms previous findings by  [33] ), which also reported that most of the metabolites in this pathway were also found differential in the other two fluids.
Discussion and conclusions ASTERICS makes the analysis and combined analyses of datasets easily accessible to people not expert in statistics, data science and programming. Other tools like Orange  [39]  might provide a more extensive panel of statistical and machine learning methods but they do not offer the same level of interactivity in plots, do not include data processing methods and analyses specific to certain omics, and require a local installation. Our use case study showed that ASTERICS is able to perform analyses in omics integration settings and to reproduce findings in just a few clicks. Results and plots can be Currently, the version of ASTERICS deployed online is designed to handle small-tomedium datasets (typically of the size of standard RNAseq data) due to limitations in RAM of the deployment server and to the uploading of data over https in uncompressed form.
Local deployment does not have this limitation (it is only limited by the computating capacity of the local deployment machine). Currently, ASTERICS is designed to perform the most common statistical analyses on the most common omics (batch transcriptomics from microarray or RNA-seq experiments, metagenomics, metabolomics or proteomics, in particular as compositional datasets, etc). However, the tool has been programmed in a modular way (both in the frontend and backend) with a full developer documentation so as to allow the extension of ASTERICS to other analyses. Future developments are expected to include more advanced workflows (MOFA, DIABLO, kernel methods), analyses helpful for interpreting results such as gene enrichment analysis and network inference, as well as more recent omics such as single-cell RNA-seq.
Availability and requirements Project name: ASTERICS Project home page: https:// forge mia. inra. fr/ aster ics/ aster ics. (also archived on software heritage under SWHID https:// archi ve. softw arehe ritage. org/ swh:1: dir: 7fbdd c80f2 87596 a6472 d2980 0681e f74b6 82dfe). Operating systems: platform independent. Programming languages: R, Python, JavaScript, HTML. Other requirements: docker for local deployment and none for web usage. License: GPL 3. Any restrictions to use by non-academics: none.
Availability of data and materials The source code of ASTERICS is released under GPL-3 license at https:// forge mia. inra. fr/ aster ics/ aster ics and docker images for its local deployment are also available in this repository. 1 H NMR spectra on which the case study is based are available in the metabolights database  [34]  under accession number MTBLS1541. Processed data (metabolite quantifications), experimental design and output reports of ASTERICS are available at https:// doi. org/ 10. 57745/ TCKSTD. The case study is fully accessible at https:// aster ics. miat. inrae. fr/ works pace/ piglet_ useca se.
Context. We provide the first results from the complete SNAD adaptive learning pipeline in the context of a broad scope of data from large-scale astronomical surveys. Aims. The main goal of this work is to explore the potential of adaptive learning techniques in application to big data sets. Methods. Our SNAD team used Active Anomaly Discovery (AAD) as a tool to search for new supernova (SN) candidates in the photometric data from the first 9. 4 months of the Zwicky Transient Facility (ZTF) survey, namely, between March 17 and December 31, 2018 (58 194 ≤ MJD ≤ 58 483). We analysed 70 ZTF fields at a high galactic latitude and visually inspected 2100 outliers. Results. This resulted in 104 SN-like objects being found, 57 of which were reported to the Transient Name Server for the first time and with 47 having previously been mentioned in other catalogues, either as SNe with known types or as SN candidates.
The challenge then becomes developing automated unsupervised learning strategies that can successfully identify such sources among large and complex data sets. The astronomical community has devoted significant efforts to this direction. For example,  Pruzhinskaya et al. (2019)  applied the isolation forest (IF,  Liu et al. 2008 ) algorithm to identify contaminants in the Open Supernova Catalog  (Guillochon et al. 2017). Malanchev et al. (2021a)  used four different anomaly detection (AD) algorithms and a comprehensive feature extraction process to identify unusual light curves in the third ZTF data release (DR). In searching for changing-state active galactic nuclei (AGNs),  Sánchez-Sáez et al. (2021)  identified 75 promising candidates by combining dimensionality reduction via deep learning with IF.
The active anomaly discovery (AAD,  Das et al. 2017)  algorithm has proven to be effective in its first application to real data  (Ishida et al. 2021). In this work, we stress test the effectiveness of this strategy by applying it to light curves from ZTF DR3. Considering as anomalies any light curves that resemble those of supernovae (SNe), our experts scanned 70 ZTF fields searching for uncatalogued or anomalous transients. This paper is organised as follows. Section 2 describes the data selection process (Sect. 2. 1), learning algorithm (Sect. 2. 2), and a summary of the results (Sect. 2. 3). In Sect. 3, we present the results of our light-curve modelling for a subset of the newly reported transients. Section 4 presents an in-depth discussion on superluminous supernova (SLSN) candidates (Sect.
4. 1), along with a complete set of labels within the SNAD viewer knowledge database (Sect. 4. 2) and a description of other non-catalogued objects found during our search  (Sect. 4. 3). We present our conclusions in Sect. 5. Additionally, the complete SNAD catalogue of discovered transients is shown in Appendix A. Appendix B shows light curves and corresponding fit models for SNAD objects. Appendix C gives a glimpse of the domain knowledge database within the SNAD viewer 3.
ZTF data and field selection We analysed photometric data from the first 9. 4 months of the ZTF survey, between 2018 March 17 and December 31 (58194 ≤ MJD ≤ 58483). This period includes data from the ZTF private survey, thus offering a better cadence than the rest of DR3 4. However, the expert analysis of discovered SNe (see Sect. 3) used more complete light curves from ZTF DR8. Given the higher probability of finding SNe in low extinction regions, we analysed only those fields with centres at > 20 • above the galactic plane. The distribution of the 70 fields considered in this work is given in Fig. 1. Each one of the selected fields contains from a few thousand to a little more than a million objects with at least 100 photometric points in zr-band (catflags = 0), thus comprising ∼26. 5 million light curves in total.
5 million light curves). Since the algorithm can adapt to the expert's opinion, it can be used for a targeted search of transients of a certain type (e. g. SNe). Therefore, in this analysis, a human expert considered only SN-like candidates as anomalies; all other objects proposed by the algorithm are rejected by the expert as 'uninteresting' (i. e. 'yes' and 'no' in the AAD interface). For each field, the expert has gone through a total budget of 30 objects. In order to enable a smooth interaction between our experts and the AAD algorithm when dealing with such a large data set, we developed the SNAD knowledge database  (Malanchev et al. 2023) , a framework used by our experts to log their input as one entry in a tailored set of labels (see further details in Sect. 4. 2). For each one of the ZTF fields, our experts went through 30 objects registering their feedback as a binary answer.
The distribution of objects by type for each of the 35 fields containing SNe or SN candidate is given in Fig. 2. Each line represents one AAD run with 30 queries in the order of appearance to the expert. The colour denotes the assigned tag, namely, whether it is a supernova, artefact, or other type of object. In what follows, we further investigate the most interesting objects we encountered. The source code is publicly available as a part of zwad  (Malanchev et al. 2021b ) GitHub repository 5.
Supernova modelling We used the PYTHON library SNCOSMO 8 to obtain a preliminary photometric classification for SNAD objects. Their light curves were fitted with Peter Nugent's supernova models 9 , which cover the main SN types (Ia, Ib/c, IIP, IIL, IIn). Nugent's models are simple spectral time series that can be scaled up and down. The model parameters are the redshift, z, the observer-frame time corresponding to the source's zero phase, t 0 , and the amplitude. The zero phase is defined relative to the explosion moment and the observed time, t, is related to phase via t = t 0 + phase × (1 + z). In order to perform a preliminary fit, we used only the zr-band from DR8. We subtracted the reference magnitude from ZTF light curves, thus roughly accounting for the host galaxy contamination. The reference magnitude was retrieved from ZTF archival data 10 and listed in the SNAD catalogue 7.
2017). Figure  7  shows the observed light curves of SNAD120, SNAD121, SNAD160, and SNAD187 in the zr-band in comparison with SN 2006gy  (Smith et al. 2007 ) -one of the brightest among the well-studied SLSNe, shifted to z = 0. 3 and 0. 4. SN 2006gy has a very broad light curve, but it is clear that the SNAD candidates have even broader light curves, making them really peculiar objects among known SNe. The discovery of four slowevolving transients among the SNAD objects, non-reported by previous searches provides clear evidence that the AAD is efficient in searching for rare classes of astronomical objects within large and complex data sets.
SNAD knowledge database Beyond the transient candidates discussed previously, this work also produced a valuable knowledge database incorporated within the SNAD viewer  (Malanchev et al. 2023 ). The viewer is a specially designed web-interface, which allows the expert to visualise ZTF DR light curves, provides access to the individual exposure images, and performs cross-matches with different databases and catalogues. For authorised users, there is a possibility to assign the labels (tags) to ZTF objects (see Fig. We defined a system of tags that includes some general classes: variable star of unspecified type (VAR), transient (TRANSIENT), active galactic nucleus (AGN), quasar (QSO), normal star without strong variability (STAR), and galaxy (GALAXY), as well as the most popular types and subtypes of variable stars and transients 13 , such as:  -Supernova (SN): Type Ia supernova (SNIA There are a few custom tags for internal purposes, such as transients with one outlier point (1-POINT) or candidates to be send to TNS (TNS_CANDIDATE).
Also, tags of non-astrophysical origin such as artefacts and their subtypes are present. Several tags can be assigned to one object, the history of tag changes is also stored in the database (Fig. C. 1, on the right). The choice of tags is determined by the experts, based on the most frequent types of objects appearing in the output of the AD algorithms and also determined by the project needs. Therefore, we do not claim to be complete in covering all possible types of variables and transients. During the supernova search a total of 1482 objects were labelled. Despite the fact that ZTF data processing pipeline includes a procedure to separate the astrophysical events from bogus ones, namely, false positive detections  (Masci et al. 2019) , fields with SNe consists of ∼45% of artefacts. Examples of found artefacts are given in Fig.
Segura et al. 2010; Engle & Guinan 2011; France et al. 2013; Webb et al. 2021). Moreover, good observational cadence of the flare (∼70 points in 46 min) also opens up a possibility to search for fast transients in ZTF data. Another interesting object, OID = 676213300006792, located at a distance of ∼234 pc (Bailer-  Jones et al. 2018 ), shows two outbursts, one of which is observed at a high frequency (see Fig. 10 ). Based on its SDSS spectrum, 676213300006792 was previously identified as a white dwarf-main sequence binary with a secondary M-dwarf companion  (Liu et al. 2012). 676213300006792 is a weak UV source and does not appear in any X-ray database. Its SDSS spectrum does not show a significant H α emission. The high cadence zg-band light curve shows a periodicity with P ≃ 4. 25 min, just before the flare.
Conclusions In this work, we provide the first results from the complete SNAD adaptive learning pipeline in the presence of big data from large-scale astronomical surveys. The SNAD team became aware of the existence of non-reported supernova candidates within the ZTF DRs once they appeared in a non-targeted anomaly detection search  (Malanchev et al. 2021a). A new experiment was then designed to develop a tailored machine learning model which would explore this possibility by taking advantage 14 The SNAD catalogue of selected artefacts found in ZTF data is available at https://snad. space/art/ A111, page 6 of 22  of the SNAD adaptive learning pipeline  (Ishida et al. 2021 ) and our experts' long-term experience studying supernovae. We selected 70 ZTF fields in high galactic latitude, employed a series of quality cuts followed by designed feature extraction (Sect.
2. 1). The resulting homogeneous feature sets (one per field) were submitted independently of 30 iterations of the active anomaly discovery algorithm, where at each iteration, the domain expert would input a positive feedback to any outlier whose light curve resembles a SN and a negative one otherwise. During this process, human-assigned labels were added to the SNAD knowledge database, opening the way for future deeper analysis of the same data (Sect. 4. 2). From the 2100 objects visually inspected, we found 104 SN-like events, 57 of which were reported for the first time. These transients received an internal name, were reported to TNS and added to the SNAD catalogue 7 (see Sect. 2. 3). In order to evaluate probable classification types for the newly found transients, we performed light curve fits using different supernova models (Sect.
3). Among the newly found transients, we reported three objects (SNAD121, SNAD160 and SNAD187) with broad, slowly evolving light curves that stand as promising superluminous supernova candidates (see Fig. 7  and  Pruzhinskaya et al. 2022). Despite the fact that the AAD was aimed at supernova search, other potentially interesting objects have been found, including non-catalogued AGNs and red dwarf flares. The high cadence data of discovered flares opens the possibility of searching for fast transients in ZTF. Moreover, the visual inspection of AAD outliers during the SN search led to the creation of the SNAD knowledge database that can be used for different machine learning tasks in the future 15. The overall efficiency of the pipeline is highly dependent on the total number of objects being analysed, feature choices, and maximum iterations budget, among other parameters.
Acknowledgements. We thank  Anastasia Voloshina  and  Alexandra Zubareva  for the assistance in variable star classification and analysis. We also thank  Stephane Blodin  and  Alexandra Kozyreva  for discussion involving PISN modelling. The reported study was funded by  RFBR  and  CNRS  according to the research project  21-52-15024. We used the equipment funded by the  Lomonosov Moscow State University Program of Development. The authors acknowledge the support by the  Interdisciplinary Scientific and Educational School of Moscow University "Fundamental and Applied Space Research". P. D. A. is supported by the  Center for Astrophysical Surveys (CAPS)  at the  National Center for Supercomputing Applications (NCSA)  as an  Illinois Survey Science Graduate Fellow. V. V. K. is supported by the  Ministry of science and higher education of Russian Federation , topic no.
(2)  The source-proof language can have complex operational semantics that is cumbersome to encode in the target system. (Note that no universal standard has yet emerged for encoding the formal semantics of arbitrary proof languages; cf. sec. 5. )  (3)  As systems change and mature, older versions of proof certificates can become stale and unmaintained. (4) Perhaps most importantly, many popular reasoning systems do not produce proof certificates at all. Prominent examples of that latter are SMT solvers that are not certifying when memory size and execution time are critical  [20]  and the specification tool Twelf  [43]  when using non-certifying procedures (e. g. , totality checking). Formal reasoning systems that are non-autarkic have an additional way to interact with external provers that addresses many of the above issues.