dataesr/scientific-paragraphs-categorization huggingface dataset:
com/Bibliome/ alvisnlp). External and third-party tools are used for data pre-and post-processing, i. e. , input and output formatting, and indexing. The last step of the workflow merges information occurring more than once in the same information source. • Data integration workflow: the data integration workflow (https://forgemia. inrae. fr/ omnicrobe/omnicrobe-database) integrates the data generated by the text-mining workflows into a PostgreSQL relational database. The database indexes the data by the controlled reference vocabulary through 'hierarchical paths'. It means that each entity is indexed by its specific class, and all more general classes of the controlled reference vocabulary. The Omnicrobe database is regularly updated in order to continuously improve the quality of the data.
We continuously enhance the text mining methods according to feedback on the quality of the results and errors observed. We regularly update the information sources and integrate any changes from ancillary resources (e. g. , OntoBiotope, Omnicrobe taxonomic reference) when revised versions of these resources are released. The Omnicrobe database has been updated more than 10 times since 2018. Initially the project covered microorganisms and habitats, and then was expanded to cover phenotypes and uses. The input data collected from the different sources has also been notably enriched since 2018 with newly published articles from PubMed and the additional BRC.
The results (panel D) are displayed in columns for better readability. A link to PubMed and Genbank original text is provided. PubMed references are displayed by the AlvisIR semantic search engine. AlvisIR allows a quick reading of the text thanks to the highlighting of the entities and relations to assess the relevance of the information. Chaix et al. [28]  give a detailed presentation of the AlvisIR interface and examples of its use. The Advanced search option of the Search menu is intended for the expression of complex embedded queries that combine "and" and "or" operators. Selected columns of query results obtained from dedicated panels and Advanced search can be exported in various formats (i. e. , CSV, Excel, and PDF) for further processing by users who are not familiar with API. Omnicrobe runs on an Apache web server.
Application programming interface All the data in the Omnicrobe database can be accessed through an Application Programming Interface (API). The API provides the same capability as the web interface. It allows the search for taxa, habitats, and phenotypes, as well as relations between these entities. The main difference is that responses are computer-readable. Thus, the API is suitable to embed Omnicrobe data into another information system. The API is developed with the Python Flask-RESTX framework. The main entry point is available at https://omnicrobe. migale. inrae. fr/api and the documentation at https://omnicrobe. migale. inrae. fr/api-doc.
Omnicrobe content In this section, we give an overview of the content of Omnicrobe in its current update (May 2022). The Omnicrobe database content reflects the focus of microbial research studies rather than the worldwide distribution of microbes with respect to their habitat, phenotype, or use. The following descriptive statistics reveal the domains where large sets of scientific knowledge have been gathered, and highlight potential gaps that need further attention. Table  3  shows the number of relations extracted per source after removing duplicates. PubMed is the only source of Omnicrobe for Taxon-Phenotype and Taxon-Use relationships; Integrating Taxon-Phenotype relationships from databases is reserved for future work. PubMed is also the most prolific source for Taxon-Habitat relationships, followed by Gen-Bank.
g. , plant, spoilage). Fig  4  shows the distribution of taxa ranks. The high number of species and subspecific taxa answers critical needs in microbiology research and is a strong point of the Omnicrobe database. Fig  5  shows the distribution of microbe taxa in Lives_In relations. As expected, Bacteria, Viruses, and Fungi are the most frequent taxa. The distribution of habitats at the four highest levels is shown in Fig 6. The most frequent habitats are related to the biomedical domain that represents the bulk of Omnicrobe information. They are followed by engineered (industrial, agricultural, food) environments, nonhuman hosts, and environmental habitats. The distribution is similar to the one shown in the isolation sites distribution chart of BacDive (https://bacdive. dsmz. de/dashboard)  [16].
The file includes links and IDs of PubMed and GenBank source documents that allow the biologist The analysis of the source texts reveals that two of the retrieved species are reported as both thermophile and mesophile: Lactiplantibacillus plantarum and Streptococcus thermophilus. In the corresponding articles the enumeration of species and phenotypes, and the mixture of species mentions make difficult the extraction of the relationships by automatic text mining. An example is "A mesophilic-thermophilic mixed culture of Lactococcus lactis ssp. lactis, Lactococcus lactis ssp. cremoris, and Streptococcus thermophilus was also used. " where Streptococcus thermophilus was incorrectly classified as mesophilic by the text mining process. We discarded the Bifidobacterium longum species because it was considered as an 'obligate anaerobe' and incompatible with food fermentation conditions.
Streptococcus thermophilus was cultivated at 43 ˚C and L. plantarum was cultivated at 30 ˚C. The details of the experiment can be found in  [33]. Fermentation success was checked by pH monitoring after 48 hours of fermentation. The pH of soy milk was 7. 2 at the time of inoculation. A pH below six means that an acidification process occurred. Among selected strains, 148 strains succeeded to acidify soy juice in 48 h (S5 Table ). All S. thermophilus strains acidified soy juice while none of the L. helveticus strains acidified soy juice. Acidification is strain-dependent for L. acidophilus, L. delbrueckii, L. plantarum, L. casei, L. paracasei and L. lactis. Except for L. helveticus, the pre-screening of relevant strains by using the Omnicrobe database was efficient, cost and time effective. The microbiologist saved significant time in bibliographical search and wet lab experiments because she could quickly focus on a relevant subset of species among potential candidates.
Discussion and conclusion We presented the Omnicrobe online application, with its unique database of information on microbe habitats and phenotypes. The whole framework was developed to automate as much as possible the update of the database content according to the evolution of the original sources and reference vocabulary and the improvement of the text mining process. Specifically, the annotation process of the textual source data and the indexing by standard metadata are fully automatic. Other projects aim at aggregating microbe biodiversity data. The Prego database  [35]  is the most similar to Omnicrobe in its scope and goal, although it differs in several aspects. As Omnicrobe, Prego gathers information about microbes and their environments computed from textual data. Prego indexes taxa and environments using the NCBI Taxonomy and EnvO respectively.
Additionally, Prego includes molecular processes using the Gene Ontology (GO) whereas Omnicrobe contains information about microbial phenotypes and uses. With regards to microbe-habitat information, which is common to both databases, Omnicrobe is significantly more comprehensive than Prego. Prego contains fewer habitat categories than Omnicrobe (1,000 versus 4,000), and Omnicrobe contains twice as many relationships between microbes and their environments (700,000 compared to 300,000). As with any resource automatically computed from textual data, the quality of Omnicrobe information depends on the quality and richness of the primary sources and on the performance of the text mining process. We noticed that frequent errors of normalization were due to ambiguities as illustrated by S3 Table examples.
They are currently handled by applying hand-coded rules that use the ambiguous word context. Long distance relationship (see S3 Table for examples) is also a source of errors in relation detection. Future work includes ambiguity resolution and relation detection by using machine learning methods trained on the Bacteria Biotope dataset  [9]. The extension of Omnicrobe to newly available data and sources is straightforward as long as the semantic interoperability of the data is ensured. The increasing standardization of database metadata and the use of controlled vocabulary are heading in the right direction. The next step is to extend Omnicrobe to include other information available in the current processed sources. For example, work is underway on BacDive DSMZ data to extract other types of data than habitat data.
Geographical information is one type of data that will require dedicated text mining analysis to ensure semantic interoperability since the lack of standardization allows mixing in the same record different types of places that should be distinguished, e. g. address, landscape, country. Consideration is also being given to integrating data from GOLD. The scope of the Use part of the database focuses on food application but will be extended to other domains in future versions, among which are biotechnology and sewage treatment. We are considering extending scientific literature sources to full-texts. We have compared the information present in the abstracts to the information of full papers of a sample of the International Journal of Systematic and Evolutionary Microbiology, which is the reference journal for the description of new prokaryote species and thus particularly rich in microbe habitats and phenotypes.
Acknowledgments The authors thank the  Migale platform  for providing the resources to run Omnicrobe services (MIGALE, INRAE, 2020. Migale bioinformatics Facility, doi: 10. 15454/1. 5572390655343293E12). The current affiliation of  Estelle Chaix  is the  French Agency for Food, Environmental and Occupational Health & Safety (ANSES) , 14 rue  Pierre et Marie Curie , 94701  Maisons Alfort Cedex, France.
fr),  CIRM -CFBP (https://cirmcfbp. fr), CIRM-Levures (https://cirm-levures. bioaware. com) and QPS information from EFSA (https://www. efsa. europa. eu/en/topics/topic/ qualified-presumption-safety-qps). Information system source code Workflows and source code of the Omnicrobe application are distributed on an institutional Git repository (https://forgemia. inrae. fr/omnicrobe) under an Apache license. The source code of the text-mining processing workflow is available at https://forgemia. inrae. fr/omnicrobe/ text-mining-workflow. The source code of the database integration workflow is available at https://forgemia. inrae. fr/omnicrobe/omnicrobedatabase. The source code of the web interface and the programmatic interface is available at https:// forgemia. inrae. fr/omnicrobe/omnicrobe_web.
All Omnicrobe data is Open Source and publicly available through a programmatic interface (https://omnicrobe. migale. inrae. fr/api-doc) hosted by the Migale institutional facility (https://migale. inrae. fr/). Ontologies and taxonomies The ontology of microorganism habitats, phenotypes, and uses, OntoBiotope, is available on AgroPortal (http://doi. org/10. 15454. Information sources All data sources processed in Omnicrobe are Open Source and publicly accessible on line: PubMed (https://pubmed. ncbi. nlm. nih. gov), GenBank (https://www. ncbi. nlm. nih. gov/genbank/), BacDive (https://bacdive. dsmz. de), CIRM BIA (https:// collection-cirmbia.
Astrometry and Alignment We processed all HST images using the DrizzlePac software suite. 38 All images were first aligned to a common world coordinate system (WCS) using TweakReg. We adopted the WCS used for the CLASH, GLASS, and HFF images and catalogs, 39  which is also the WCS generally used by published lens models for this cluster. We then co-added and resampled each image set to a scale of 0 03 pixel -1 using AstroDrizzle  (Fruchter et al. 2010).
Photometry with DOLPHOT Fitting a model of the point-spread function (PSF) to an unresolved point source such as S1-SX should, in principle, yield a measurement of the source's flux with an optimal signal-to-noise ratio (S/N). For a point source, pixels closest to the source's center have the greatest S/N. When fitting a PSF model to the data, one can assign greater weights to these central pixels. By contrast, when measuring instead an aperture magnitude, the flux within a fixed radius is summed and an aperture correction is applied, and all pixels in the aperture are assigned effectively the same weight. We have adapted the DOLPHOT (Dolphin 2000) PSF-fitting package to be able to measure photometry from WFC3 IR difference imaging. When applying DOLPHOT, we use the default sky-fitting setting (FitSky=1) and model images S1-SX as point sources (Force1=1).
When fitting images S1-SX, we instruct DOLPHOT to assign additional weight to central pixels (PSFPhot=2). Finally, we enforce the additional constraint that the flux of S1-SX acquired does not change between exposures taken within each imaging epoch, except for variation due to noise (ForceSameMag=0). We estimate the uncertainty of the measured fluxes by using DOLPHOT to inject fake point sources into individual exposures. We next measure the fluxes of the fake sources and estimate their uncertainty by computing the standard deviation of measured fluxes. For the purpose of comparison, we use PythonPhot 40   (Jones et al. 2015)  to measure the light curves of images S1-SX of SN Refsdal from co-added imaging. The PythonPhot package includes an implementation of PSF-fitting photometry measurement based on the DAOPHOT package  (Stetson 1987).
Light Curves of Stellar Sources in Cluster Field To assess the performance of the photometry pipelines, we selected a set of stars from a co-added image of the field acquired with the ACS WFC F606W broadband filter and required that the stars' F125W and F160W fluxes were similar to those of S1-SX over the evolution of their observed light curves. We next ran DOLPHOT in an image section around the location of each star and determined the star's position in each image. Using these positions, we then fit for the position, proper motion, and annual parallax. Finally, we ran DOLPHOT and PythonPhot on each of the comparison stars and calculated the standard deviation and reduced χ-squared statistics of the flux measurements. We estimate the photometric uncertainties by injecting 100 fake point sources using DOLPHOT and PythonPhot.
SED Fitting to Estimate the Stellar Mass Density at the SN Image Positions We next want to model the measured SEDs to derive estimates for the physical properties of the BCG+ICL and the elliptical galaxy lens. To that end, we correct the fluxes for Galactic extinction using  Schlafly & Finkbeiner (2011)  and then use the Fitting and Assessment of Synthetic Templates (FAST) software tool  (Kriek et al. 2009 ) and the  Bruzual & Charlot (2003, hereafter BC03)  stellar population model to model the SEDs shown in Figure  5. Our fitting uses the BC03 isochrones, a Chabrier (2003) IMF, and an exponentially declining star formation history; moreover, it assumes no extinction due to dust associated with the BCG, ICL, or earlytype lens. The stars are set to have initial masses that are between 0. 1 and 100 M e , and we include models with metallicities Z of 0.
Supernova Time Delays Parametric Model We fit the light-curve data using version 2 of the Supernova Time Delays (SNTD) software package  (Pierel & Rodney 2019 ). Here we model SN Refsdal's flux as a function of time using ( ) ( ) ( ) ( ) = + + t t -- -- F t A e e B 1 , 1 t t t t 0 f a l l 0 r i s e presented by  Bazin et al. (2009)  as a generic model for SN light curves, where τ rise and τ fall characterize the relative rise and decline times of the light curve, respectively, and A and B set its normalization. While this model is flexible, its simple shape minimizes sensitivity to microlensing features in the light curves of S1-SX. In order to measure the time delays of the simulated light curves described in Section 4. 6, we employed SNTDʼs parallel method according to which the light curves of S1-SX are first fit separately before deriving joint likelihood estimates for the light-curve model parameters (see  Pierel & Rodney 2019 ).
Python Curve Shifting3 Free-knot Spline Model Python Curve Shifting3 (PyCS3) is a new version of the PyCS package, which was originally designed for application to lensed quasar light curves, and includes several independent methods for measuring time delays and estimating uncertainties  (Tewes et al. 2013; Millon et al. 2020b). For application to the SN Refsdal data, we use the free-knot spline estimator, which fits third-order splines to the light-curve data. The intrinsic variations of the source, common to all lensed images, are fit with a single spline. The  Note. The rms scatters in the time delay columns are given in units of days. The magnification ratio columns are unitless and report the scatter of flux magnification ratios (i. e. , no conversion into magnitudes has been done). When computing the scatter, we have excluded > 30-day outliers for the time delays, and 1.
Relative Time Delays and Magnification Ratios With a procedure for optimally combining estimates from the light-curve fitting methods and estimating uncertainty, we turn to measurements of the relative time delays and   16. magnification ratios from the SN Refsdal light-curve data. We used DOLPHOT photometry, restricted to data acquired after 2013 December 24 (MJD = 56,650) to fit the light curves of S1-S4 and 2015 October 10 (MJD = 57,300) for SX. To enable the time delay fitting to be blind, we generated a random number for each light curve and added that random number to the Modified Julian Date corresponding to each photometric measurement. Each of the four fitting methods was applied to the shifted light curves and combined using the weighted sum described in Equation (6). In Figure  14 , we plot the combined constraints on the time delays relative to image S1.
Summary and Discussion We have described our blind measurements of the relative time delays and magnification ratios of the first known example of a multiply imaged SN. SN Refsdal provides a powerful and unique probe of the value of H 0 and a novel test of galaxycluster models. Our constraints employ all available HST WFC3 IR imaging data collected during multiple follow-up campaigns. To arrive at robust, as precise as possible, and accurate inferences about the relative time delays and magnification of the five observed images, we have used four separate light-curve fitting algo rithms. Moreover, to minimize human bias, we have performed  the analysis in a blind manner by adding random offsets to the light-curve dates. As shown in Figure  2 , we find evidence that image S4 has experienced chromatic microlensing, which is not expected given our simulations with a spherically symmetric model photosphere.
We anticipate that the methods we have developed to extract the time delay and magnification ratios for SN Refsdal may be helpful to the analysis of the expected set of discoveries. In particular, our adaptation of DOLPHOT for difference imaging should be useful for the Roman Space Telescope. Moreover, the framework for the combination of estimates from multiple light-curve fitting algorithms, informed by their performance on simulated data, we hope will be useful. SN Refsdal was surprising given that it was a peculiar, SN 1987A-like SN explosion of a blue supergiant star, which are not common in the nearby universe  (Kelly et al. 2016a). Intriguingly, a second, highly magnified blue supergiant star was discovered in the same host galaxy  (Kelly et al. 2018). The data set that we assembled for SN Refsdal required a large investment of observing time with HST.
We thank  Program Coordinators Beth Periello  and  Tricia Royle  and Contact Scientists Norbert Pirzkal,  Ivelina Momcheva , and  Kailash Sahu  of the  Space Telescope Science Institute (STScI)  for their help carrying out the HST observations. We also appreciate useful discussions with  C. Grillo ,  P. Rosati , and  S. Suyu. NASA/STScI  grants  14041 ,  14199 ,  14208 ,  14528 ,  14872 ,  14922 ,  15791 , and  16134  provided financial support. P. K. is supported by  NSF  grant  AST-1908823  and by  NASA/Keck   JPL RSA 1644110. M. O. acknowledges support by the  WPI Initiative (MEXT, Japan ) and by  JSPS KAKENHI  grants  JP22H01260 ,  JP18K03693 ,  JP20H00181 , and  JP20H05856. T. T. acknowledges the support of  NSF  grant  AST-1906976. S. T. was supported by the  Cambridge Centre  for Doctoral Training in Data-Intensive Science funded by the  UK Science and Technology Facilities Council (STFC).
J. D. R. P was supported by  NASA Headquarters  under the  NASA   FINESST award   80NSSC19K1414 , and by grant  HST-AR-15050  from  STScI , which is operated by  AURA, Inc. , under  NASA  contract  NAS 5-26555. J. M. D. acknowledges the support of projects  PGC2018-101814-B-100  ( MCIU/AEI/MINECO/ FEDER ,  UE) Ministerio de Ciencia, Investigación y Universidades  and project  MDM-2017-0765  funded by the  Agencia Estatal de Investigación ,  Unidad de Excelencia María de Maeztu. M. J. is supported by the  United Kingdom Research and Innovation (UKRI)   Future Leaders Fellowship  " Using Cosmic Beasts to Uncover the Nature of Dark Matter " (grant  MR/S017216/1 ). M. M. and V. B. acknowledge support from the  European Research Council (ERC)  under the European Unionʼs  Horizon 2020 research and innovation program  (COSMICLENS: grant agreement  787886 ).
ToO Catalog for EUSO-SPB2 With the goal of optimizing the scientific reach of the ToO program within the operational parameters of the EUSO-SPB2 mission, a catalog of sources was developed from the three preexisting alert systems broadcast from various observatories and instruments. The ToO catalog is populated through a combination of manual and automated processes that parsed alerts from the Gamma-ray Coordination Network (GCN), the Transient Name Server (TNS), and the Astronomer's Telegram (ATel) are monitored and sorted based on their relation to potential neutrino sources of interest. GCN and TNS alerts are sorted by a python script, and ATel alerts are sorted by hand. The catalog also includes a list of hand-selected steady sources (Fig. 3 ). For the EUSO-SPB2 observation period on May 14, 2023, the ToO catalog contained 152 sources, including 16 sources from GCN, 12 sources from TNS, 4 sources from ATels, and 120 steady sources.
ToO Scheduling A prioritization scheme is critical for the ToO scheduling process. The scheduling process, which is outlined in  [7] , takes entries from the ToO catalog, compares their daily trajectories with the CT trajectory, and calculates the time and location of when the ToO will cross the detector's field of view. After this is finished, there can be tens of sources that cross into the detector's field of view during a nightly observation run. Because each of these runs may only be 4-5 hours at the most, with each ToO being visible anywhere from 20 minutes to 1 hour and 20 minutes, 4 or 5 sources at most may be scheduled (see Fig. 4 ). The prioritization scheme is used to trim down the observable ToOs to fit the observation run schedule. For a given observation period, if the number of observable sources is more than 5, the ToO software parses through the observable sources and chooses 4-5 based on priority values.
PoS(ICRC2023)1185 If there are multiple observable transient sources of a given priority value, the software checks a database of how much time the sources have already been observed. This database is updated after every observation run and records the observed sources with the amount of time (in minutes) of the ToO observation. Sources that have less recorded previous observation time in the database are prioritized over sources that have already been observed. An example schedule from May 14, 2024 is included in Table  3.