dataesr/scientific-paragraphs-categorization huggingface dataset:
SUMMARY AND CONCLUSION In this paper, we present a technique to investigate the spatial clustering of the young stellar clusters in the LE-GUS galaxy NGC 628 with UV+optical data taken with WFC3/UVIS from HST. The inclusion of NUV observations within this study provides reliable measurements of both the age and masses of the clusters, giving us an unprecedented, high-angular resolution view of the clustering in a way that has not been possible before. Through visual inspection, we have identified 1264 stellar clusters within the galaxy that have an absolute Vband magnitude brighter than -6, CI > 1. 4, and detection in at least four filters. For the fainter (V-band magnitude fainter than -6) clusters that did not undergo visual inspection (a total of 384 objects) that still have detection in at least four filters, we take cuts in both concentration index and age to separate out possible cluster candidates from spurious, non-cluster candidates.
We thank the anonymous referee for the careful and constructive comments that greatly improved this paper. This paper is based on observations made with the  NASA/ESA Hubble Space Telescope , obtained at the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy , under  NASA  Contract  NAS 5-26555. These observations are associated with Program 13364 (LEGUS). Support for Program 13364 was provided by  NASA  through a grant from the  Space Telescope Science Institute. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with  NASA. DAG acknowledges financial support by the  German Research Foundation (DFG)  through grant  GO 1659/3-2.
Background: High resolution mass spectrometry (HRMS) is being used increasingly in the context of suspect and non-targeted screening for the identification of bioorganic molecules. There is correspondingly increasing awareness that higher confidence identification will require a systematic, group effort to increase the fraction of compounds with tandem mass spectra available in central, publicly available resources. While typical suspect screening efforts will only result in tentative annotations with a moderate level of confidence, library spectral matches will yield higher confidence or even full confirmation of the identity if the reference standards are available. Results: This article first explores representative percent coverage of measured tandem mass spectra in selected major environmental suspect databases of interest in the context of human biomonitoring, demonstrating the current extensive gap between the number of potential substances of interest (up to hundreds of thousands) and measured spectra (0.
57-3. 6% of the total chemicals have spectral information available). Furthermore, certain datasets are benchmarked, based on previous efforts, to show the extent to which acquired experimental data were comparable between laboratories, even with HRMS instruments based on different technologies (i. e. , quadrupole-quadrupole-time of flight versus ion trap/quadrupole-Orbitrap). Instruments and settings that are less comparable are also revealed, primarily linear ion trap instruments, which show distinctly lower comparability.
As the aim is typically to confirm the presence of as many compounds as possible, adequate strategies have to be used in reporting the results, both for research and in the context of regulatory use. While QA/QC aspects are well established in the field of conventional targeted methods, these are currently less developed for non-targeted analyses, although these are discussed actively in both the metabolomics and environmental communities (e. g. ,  [2, 3] ). This article explores possible strategies for QA/QC of tandem mass spectral libraries and databases, especially suitable for the annotation of chemicals of emerging concern in the context of human biomonitoring (specifically within the Human Biomonitoring for Europe project, HBM4EU, http://www. hbm4e u. eu) and environmental monitoring (initiatives originating from the NORMAN Network, http://www.
Annotation is the act of linking a detected mass spectrometric feature with a chemical identity, taking into account the detected chromatographic and spectrometric characteristics. Identification is the process of proving or verifying that the annotated compound is indeed the proposed chemical (i. e. , the annotation can be confirmed). Annotation is generally performed using analytical evidence of the measured dataset alone  [1] [2] [3] , along with additional supporting evidence (e. g. , experimental context  [2]  and metadata  [4] [5] [6] ). In contrast, identification is generally accomplished by comparing measured data sets (e. g. , using reference standards), where one set of features is obtained from the analysis of an unknown compound; the other from a reference standard of known identity.
These "fingerprints" (often specific for distinct instrumental settings) are generally stored in databases. While predictive (in silico) methods exist for both fragment (e. g. ,  [7, 8] ) and retention time information (e. g. ,  [9] [10] [11] [12] ), these are not yet sufficiently accurate for unequivocal identification, although these are constantly improving in accuracy. Thus, complete "standards-free" identification is not yet possible for HRMS, although it is becoming increasingly possible to get reasonable annotations using "standards-free" approaches. From an analytical point of view and depending on the available information for annotation, chemicals can be divided into three categories, which we define as follows for this article (see Fig. 1 ). Fig. 1  The chemical space difference between targets, suspects and non-targets/unknowns Targets are compounds ("knowns") that are preselected for analysis in a sample and for which full mass spectrometric reference data, including MS/MS fragmentation and retention time, is available for annotation.
The reference data are usually acquired with certified reference standards in house; the reference mass spectra and, in some cases retention times (depending on the database), are stored in mass spectral databases. Suspects are known compounds ("known unknowns"  [13] ) that are expected ("suspected") to be present in a sample, but for which either no reference standard (in house) or incomplete mass spectrometric reference data are available, such that unequivocal annotation is not always directly possible. In suspect screening, this could be either missing or measured with alternative methods, or predicted with computational tools, and as such not of sufficient accuracy in many cases to allow reliable annotation-although it may provide supporting evidence. For instance, while MS information can be computed reliably from the structure, the in silico prediction of MS/MS fragmentation and retention time information still needs to be improved.
Tandem mass spectral databases: current status Tandem mass spectral databases are indispensable tools for compound annotation in non-targeted HRMS workflows based on soft-ionization mass spectrometry (typically liquid chromatography (LC)-HRMS) and good matches can yield Level 2a annotations in many cases. Several reviews are available describing the development and application of tandem mass spectral databases  [3, [16] [17] [18] [19] [20] [21] [22]. Typically, a tandem mass spectral database represents an organized collection of tandem mass spectral data within a management system. The database management system enables the user, or other applications, to interact with data within the database itself. Tandem mass spectral databases are acquired by the analysis of reference standards. Since a fragmentation spectrum can look different depending on the excitation process (e.
g. , resonant vs. non-resonant) as well as the collision energy applied to the parent ion, state-of-the-art databases include sets of compound-specific spectra that were acquired by applying different collisions energy settings, as well as different instruments  [23, 24]. Fragmentation is typically accomplished by collision-induced dissociation (CID) or higher-energy collisional dissociation (HCD). Usually, the spectral information is processed prior to storage in a library. Curation efforts may include manual inspection of mass spectra by experienced mass spectrometrists, noise and artifact removal, recalibration of spectra and peak annotations, as well as inter-library comparisons  [15, 23, [25] [26] [27]. In some databases, such as the Human Metabolome Database (HMDB  [28] ) and MassBank of North America (MoNA,  [29] ), experimental data are now complemented with in silico-generated spectra.
In 2016, the overlap of compounds with tandem mass spectra from authentic reference standards in most public and commercial databases was evaluated by Vinaixa et al. [16]. A total of 27,622 unique compounds were present across all databases. Among the 7127 compounds in the four open databases HMDB 3. 0  [30] , MassBank  [24] , the Global Natural Product Social Molecular Networking library (GNPS)  [31] , and the RIKEN MS n spectral database for phytochemicals (ReSpect)  [32] , only 18 compounds (< 1%) had at that time at least one form of spectral data in all databases. When comparing all combined open databases versus four commercial ones, only 225 compounds out of 27,622 (< 1%) had at least one form of spectral data in all databases. The ratio of compounds in each database with any type of spectral data in two or more databases was generally > 50%, with the exception of METLIN and GNPS, which only overlapped approximately 35% with other databases in terms of compounds.
As there is a relatively low overlap of compounds among existing spectral databases, most scientists currently use multiple databases. Since the 2016 review, many of the databases have expanded their compound coverage immensely and many of the open libraries have cross-imported their spectral records. However, the issue of overlap and coverage of relevant substances in chemical space remains  [17]. Conceptually, the premise of spectral library searching is very simple: the fragmentation pattern of a molecule is a reproducible fingerprint of that molecule under a given set of fixed conditions, such that unknown spectra acquired under similar conditions can be identified via spectral matching  [33]. Automated spectral library searching involves software with tailor-made search algorithms for tandem mass spectral databases  [34] [35] [36] [37].
The search score obtained following the database search represents the likelihood that the searched spectrum corresponds to a given reference spectrum in the mass spectral database. A low score indicates that the experimental fragmentation pattern has low similarity to any stored reference spectrum. A high score indicates significant spectral overlap and, consequently, that the analyte is likely either structurally similar or even identical to the reference compound. Library search should be both sensitive and specific, producing as few false negative and false positive results as possible. Ideally, the scores obtained should be able to distinguish true and false positive matches  [18]. To compare with historical targeted methods applied in a regulatory context (e. g. , forensic toxicology and food safety), the primary objective of a screening method is to limit the risk of false negatives (e.
This situation has changed thanks to both progress in instrument technologies and informatics tools. Databases combining advanced library designs with tailor-made search algorithms have been shown to enable reliable compound identification with spectra acquired in different laboratories with various instruments and different instrument settings  [18, 27]. While pre-acquisition harmonization of analytical procedures was researched, the participating laboratories encountered a number of difficulties  [40]. Thus, the current trend is rather to look for a post-acquisition flexibility of the MS/MS reference library and associated matching algorithms to deal (as much as possible) with the diversity of imported experimental data without sacrificing the ambitioned confidence level in terms of correct annotation.
Over the last 10 years, there has been substantial progress in the quality of tandem mass spectral databases. Today, spectral acquisition of reference spectra is accomplished regularly on high-resolution instrumentation (i. e. , quadrupole-quadrupole-time of flight (QqTOF), Orbitrap) employing multiple collision energies for fragmentation to comprehensively cover the breakdown curves of reference compounds. Besides the protonated and deprotonated molecular ions, adduct ions, in-source fragments as well as isotopologues are commonly selected as precursor ions  [18, 25, 26]. Furthermore, to improve spectral quality, generally only curated spectra are stored in databases, which come bundled with improved search algorithms. As knowledge and understanding of mass spectra increase, automated curation procedures are being implemented and constantly improved to reduce the manual curation load associated with mass spectral database creation  [15, 25].
Overall, the ambition for a "universal tandem mass spectral database" is closer to a reality. However, this ambition requires definition and implementation of some common procedures to ensure the reliability and robustness of the generated data, both stored in the desired tandem mass spectral reference library and generated from each experimental sample. A number of reference tandem mass spectral databases exist that have to be considered in the frame of identification of chemicals of emerging concern, in terms of structuration and/or content. These existing resources should serve as a basis to avoid unnecessary time spent in re-implementing existing and reliable elements, and to ensure a coherence of the ambitioned outputs with potential established standards. However, an obvious lack of high-level QA/QC consolidation appears within many of these existing databases (e.
2. To determine a baseline for the status of environmentally and toxicologically relevant compounds and their presence in various resources, a mapping exercise was performed. Compound numbers (number of entries) were obtained for the CompTox Chemicals Dashboard  [42] , NORMAN SusDat  [43] , HMDB  [28] , DrugBank  [44] , the Toxic Exposome Database (TEDB)  [45]  and Exposome Explorer  [46]  from their respective websites Fig. 2  The proposed workflow to convert suspects into targets or download files on March 15, 2019. CompTox numbers mapping to mzCloud  [47] , MassBank  [48]  and WRTMD  [49]  were obtained via downloading the respective list files (list codes on https ://compt ox. epa. gov/dashb oard/ chemi cal_lists are: MZCLO UD, HDXNO EX, MASSB ANKEU SP, MASSB ANKRE F, MYCOT OXINS , WRTMD ), also on March 15, 2019 and counting/merging by InChIKey first block (thus ignoring stereochemistry).
Interlaboratory study to validate acquired reference spectra An interlaboratory study was organized to verify that participating laboratories were generating new reference data with experimental settings and workflows that were compatible with existing reference spectra collections. Each participating lab was asked to use fifteen reference compounds for producing tandem mass spectral libraries. Table  1  includes the set of compounds used in this study. They have already been applied to test the transferability of the WRTMD in different laboratories with various available instruments and procedures (e. g. ,  [18, 34, 36] ). Seven laboratories involved in the NORMAN Network and/or HBM4EU participated in the interlaboratory study. An overview of the applied instrumentation as well as the applied fragmentation technique is provided in Table  2  as well as in the Additional file 1.
Starting point-overview on existing tandem mass spectral data for chemicals of potential concern Various initiatives and/or sources of information have documented proposed lists of chemicals of emerging concern in various contexts, mainly in the field of environment and toxicology. Examples are the many lists on the NORMAN Suspect List Exchange  [41]  and the corresponding merged database SusDat  [43] , the CompTox Chemicals Dashboard  [42]  and a series of topical databases from the Wishart laboratory and collaborators (e. g. , HMDB  [28, 30] , DrugBank  [44] , the Toxin and Toxin Target Database (T3DB) or Toxic Exposome Database (TEDB)  [45]  and the Exposome Explorer database  [46] ). While these sources overlap to some extent, they also provide a lot of complementary information and functions.
As several reviews have covered the number of overlapping substances in tandem mass spectral libraries in various contexts (especially metabolomics) recently  [16, 17, 54] , the focus here will be on substances of interest for the environmental and biomonitoring contexts, using the resources mentioned. Looking into these collections, tandem mass spectral fragmentation information is already available for a considerable number of suspects in MassBank, HMDB, WRTMD or mzCloud, but this represents only a fraction of the substances actually present in the respective resources. An overview of the number of compounds in the respective resources, as well as the number of entries that map to mass spectral data within that resource is given in Fig. 3. The CompTox Dashboard (875,000 compounds) includes 3997 compounds in mzCloud, 2377 in MassBank and 1429 in WRTMD, corresponding with 5019 unique compounds (ignoring stereochemistry differences), thus 0.
57% of the resource. HMDB (144,098 compounds) contains MS/MS data corresponding to 750 unique compounds (ignoring stereochemistry), or 0. 66% of the resource. NORMAN SusDat contains 40,180 entries, of which 1387 are in MassBank (3. 6% of SusDat). This overview shows that tandem mass spectral data is available only for a rather low number of compounds. A further complicating factor is that these tandem mass spectral data are spread among several spectral collections. For the vast majority of interesting suspects, no public mass spectral data exists and measured mass spectral data will have to be newly generated, if possible. While METLIN now claims MS/MS spectra of over 500,000 chemicals (https ://metli n. scrip ps. edu/, accessed 8 Dec. 2019), information on the coverage is not available, nor are the spectra openly available.
However, as the PubChem database [50] aggregates information from a number of sources, the 74,678 compounds with MS/ MS annotations (ignoring stereochemistry; 89,726 with stereochemistry), of 102,404,298 compounds (~ 0. 073% of PubChem) give a reasonable indication of the total number of compounds with MS/MS information available to some extent, although some of these are in silico and many of these are not directly relevant for human biomonitoring or environmental studies. The generation of new reference spectra is considered to represent an important element for the successful and long-term establishment of non-targeted LC-MS. As a single laboratory will not have the necessary resources available to handle the huge number of suspects ahead, this challenge must be addressed as a group. For successful realization of multi-partner generation of reference spectra, harmonization of acquisition and processing strategies is essential.
Quality control of MassBank collections MassBank is an important collection of reference tandem mass spectra  [24]. Currently, 45 collections are available on MassBank (https ://massb ank. eu/MassB ank/Recor dInde x) with more than 55,075 tandem mass spectra (of 76,037 spectra total) representing 14,297 compounds (15,988 stereoisomers) total (over all spectral types). In terms of compound coverage, there is significant overlap between MassBank and the WRTMD that can be used to create sets of positive controls for testing the libraries. Positive controls are particularly suitable for testing the quality and comparability of databases. Matching positive controls is used to determine the sensitivity (= true positive rates) of a database. Ideally, the obtained sensitivity values should be close to 100%.
Negative controls are used to test the specificity (= true negative rate) of a database. Fig. 3  Selected resources relevant for environmental and/or human biomonitoring studies (in blue, see main text) as well as the corresponding mass spectral entries, where available (orange). Both SusDat and HMDB have mass spectral resources integrated (SusDat partially), while CompTox has lists mapped to spectral libraries, indicating availability and PubChem has a Table of Contents with the number of compounds containing MS/ MS annotations. CompTox, HMDB, and SusDat have more extensive in silico entries available that have not been represented here. While both TEDB and DrugBank list MS/MS entries (measured and in silico), this information is not as easily accessible as HMDB and many entries overlap with HMDB Initial benchmarking efforts between the Swiss Federal Institute of Aquatic Science and Technology (Eawag) MassBank collection and the WRTMD were published recently  [27].
Multi-partner acquisition of new tandem mass spectral data Interlaboratory harmonization studies are useful to verify that a laboratory is generating new reference data with experimental settings and workflows that are compatible with existing reference spectra collections. One way to characterize this interlaboratory comparability is to introduce a number of predefined known compounds and accompanying QA/QC criteria, such that these compounds must be detected and successfully identified with a given instrumentation and related procedure to validate the method appropriateness and reliability. Seven laboratories involved in the NORMAN Network and/or HBM4EU project participated in the first harmonization study. The study was aimed to demonstrate compatibility and transferability of newly acquired tandem mass spectral data among participating laboratories as well as with already available reference spectra collections.
5 ). Thus, databases that contain series of multiple spectra acquired on one instrument will enable reliable compound identifications when querying spectra from other instruments. Clearly, databases produced in different laboratories will offer complementary identification possibilities. Another important result of the interlab study was the observation that even for instruments with identical configurations, a considerable inter-instrument variability in the optimal collsion energy range necessary for obtaining library matches with high match probability was observed (Fig. 6 ). Taking into consideration that all Orbitrap technology-based instruments were provided by the same manufacturer, a higher degree of similarity between those instruments regarding compound-specific breakdown curves was expected.
The observation suggests that even after years of instrument development and optimization, harmonization of collision energy values has hardly been accomplished yet. The good news is, however, that state-of-the-art tandem mass spectral databases can cope with spectral variability leading to high reliability of a library match. The analyst applying these libraries just needs to find the optimal collision energy corridor for acquiring test spectra. The herein presented interlaboratory study could represent an appropriate strategy for this purpose. The interlaboratory study also highlighted some limitations. These are mainly connected with the use of tandem-in-time fragmentation in the ion trap of the linear ion trap (LIT)-Orbitrap instrument (Fig. 5d ). In contrast to quadrupole collision or HCD cells that use non-resonant CID, ion traps use resonant CID (i.