dataesr/scientific-paragraphs-categorization huggingface dataset:
A 0. 83 ppm overall mean average chemical shift deviation value was achieved. The number of unassigned signals ranges from one to four in the 9 remaining cases. One reason why the prediction of signals might fail is that there is no entry in the HOSE code library and thus no prediction value is available. This problem will be overcome when larger open-access collections of assigned NMR spectra will become available. Another cause for a missing signal could be a larger difference in the given maximum shift tolerance or average deviation. Furthermore, diastereotopic carbons might not be distinguished by their 3D HOSE code since the stereochemical information in the output of pyLSD is not provided and no method in Sherlock is implemented to provide it. In addition, the required stereochemical properties, as suggested  [18, 19] , are not available for all compounds in Sherlock's structure-to-spectrum database which leads to the same problem (mentioned above) during the HOSE code knowledge base creation and thus also affects the prediction capability.
Sherlock provides the search for fragments for 13 C query spectra. This was tested for the first 40 case studies as well, by incorporating the first entry of the ranked fragment list in the input file of the structure generator. In the vast majority of the cases (32) the number of candidates was equal to one and the solution was the expected one. Only six of the ** Gladiofungin A No result After the three necessary parameter adjustments and the elucidation for each of the first 40 test examples, the rank of the expected compound was determined. As a result, 38 of 40 candidate lists contained the desired structure in the first place whereas two of them were ranked #6 and #8. This indicates that the prediction and ranking modules of Sherlock are reliable and the expected compound is often situated in the top ten of the ranked candidate list.
A full spectrum-to-spectrum assignment (Table  1 ) was produced in 31 cases among 40. A 0. 83 ppm overall mean average chemical shift deviation value was achieved. The number of unassigned signals ranges from one to four in the 9 remaining cases. One reason why the prediction of signals might fail is that there is no entry in the HOSE code library and thus no prediction value is available. This problem will be overcome when larger open-access collections of assigned NMR spectra will become available. Another cause for a missing signal could be a larger difference in the given maximum shift tolerance or average deviation. Furthermore, diastereotopic carbons might not be distinguished by their 3D HOSE code since the stereochemical information in the output of pyLSD is not provided and no method in Sherlock is implemented to provide it.
In addition, the required stereochemical properties, as suggested  [18, 19] , are not available for all compounds in Sherlock's structure-tospectrum database which leads to the same problem (mentioned above) during the HOSE code knowledge base creation and thus also affects the prediction capability. Sherlock provides the search for fragments for 13 C query spectra. This was tested for the first 40 case studies as well, by incorporating the first entry of the ranked fragment list in the input file of the structure generator. In the vast majority of the cases (32) the number of candidates was equal to one and the solution was the expected one. Only six of the problems resulted in more than one solution but mostly with a massive reduction in solution size. In two cases  (14 and 17)  no structure was produced due to improper fragment proposal.
Methods The Sherlock CASE software consists of two parts, a frontend and a backend (Figure  14 ). The frontend acts as a graphical user interface (GUI). It serves for spectra and structure visualisation and for the adjustment of parameters related to NMR data processing and to CASE tasks. The backend runs services that enable database lookup for the identi- No prior knowledge or user influence, such as a manually added or imported fragment, was used in this example, showing that no change of the CASE settings was needed to produce the correct structure. Additionally, the expected structure appeared at the first place in the ranking list. To further narrow down the number of results the fragment search was carried out and the most likely fragment selected for inclusion in the solution structures.
Methods The Sherlock CASE software consists of two parts, a frontend and a backend (Figure  14 ). The frontend acts as a graphical user interface (GUI). It serves for spectra and structure visualisation and for the adjustment of parameters related to NMR data processing and to CASE tasks. The backend runs services that enable database lookup for the identification of known compounds (dereplication) and for the proposal of structural constraints and molecular fragments useful to narrow the chemical search space during elucidation. Tools for 13 C NMR chemical shift prediction as well as solution structure filtering and ranking complete the set of functions present in CASE programs  [2, 8]. In addition, the backend also manages the storage and retrieval of previously obtained elucidation results.
Structure-and-Spectrum Database Design Sherlock offers several CASE-related services which rely on its own knowledge base of structure and spectrum assignments  [7, 8, 16, 20]. This knowledge base is involved in the dereplication from a list of 13 C NMR chemical shifts and is used for the construction of a molecular fragments library. The calculation of the probability for a given chemical shift to be related to a particular structural feature, as used for constrained structure generation, also depends on Sherlock's internal database. The knowledge base consists of 892. 841 records. Each record contains the full description of a molecular structure as a collection of atoms and bonds. Each 13 C NMR chemical shift value is associated with a multiplicity (the number of attached protons) and an equivalence index.
This index indicates the number of chemically equivalent carbon atoms existing in a molecule for a given chemical shift. Each newly inserted spectrum is checked during database construction for the existence of signals with the same chemical shift and multiplicity. If identity occurs, then the equivalence descriptor of the concerned atoms is set to the number of such signals. Consequently, the sum of all equivalence indexes of a spectrum is equal to the number of carbons in the currently inserted molecule. Figure  15  and Table  2  contain an example where the equivalence index of two signals from a monosubstituted aromatic ring is different from 1. Atoms 6 and 7 as well as 9 and 10 are assigned to a single 13 C NMR signal in each case. Sherlock assumes that 1D and 2D NMR data and the molecular formula of a pure compound are provided as input.
This index indicates the number of chemically equivalent carbon atoms existing in a molecule for a given chemical shift. Each newly inserted spectrum is checked during database construction for the existence of signals with the same chemical shift and multiplicity. If identity occurs, then the equivalence descriptor of the concerned atoms is set to the number of such signals. Consequently, the sum of all equivalence indexes of a spectrum is equal to the number of carbons in the currently inserted molecule. Figure  15  and Table  2  contain an example where the equivalence index of two signals from a monosubstituted aromatic ring is different from 1. Atoms 6 and 7 as well as 9 and 10 are assigned to a single 13 C NMR signal in each case. Each 13 C spectrum entry in NMRShiftDB  [21]  was stored in the format p Figure  15  and Table  2  to build the reference database.
Spectra Tab The NMR spectra are displayed and analysed in Sherlock's frontend by m open-source NMR software component NMRium  [23]. Spectrum analysis in consists of extracting the position of spectral peaks in 1D and 2D spectra in ord lish lists of chemical shift values and of coupling-mediated correlations bet The full description of the 13 C query spectrum (Table  2 ) is the minimum requ the dereplication. In the case of an elucidation procedure, a molecular formul tory. The standard proton ( 1 H) spectrum is recommended to enhance the upc ysis. The multiplicity of the 13 C NMR signals can be deduced automatically fro plementary DEPT-90 and DEPT-135 spectra. The 1 H,X-HMQC/HSQC, 1 H,X-H 13 C or, less frequently, 15 N), and 1 H, 1 H-COSY spectra constitute the minimu NMR data necessary for automatic structure generation.
The recording of m edited 2D HSQC (me-HSQC) spectra constitutes a good alternative to the time acquisition of DEPT spectra. Moreover, 13 C signal multiplicity can be autom duced from me-HSQC spectra. Each 13 C spectrum entry in NMRShiftDB  [21]  was stored in the format presented in Figure  15  and Table  2  to build the reference database. In total 27. 938 experimental and predicted spectra were extracted. In addition, the chemical shift verification tool in Advanced Chemistry Development, Inc. (ACD Labs) C+H Predictors and DB software was used to predict the 13 C spectra of all molecules (864. 903) in the natural product database COCONUT  [22]. These structures and spectra were subsequently inserted into Sherlock's knowledge base.
Spectra Tab The NMR spectra are displayed and analysed in Sherlock's frontend by means of the open-source NMR software component NMRium  [23]. Spectrum analysis in this context consists of extracting the position of spectral peaks in 1D and 2D spectra in order to establish lists of chemical shift values and of coupling-mediated correlations between them. The full description of the 13 C query spectrum (Table  2 ) is the minimum requirement for the dereplication. In the case of an elucidation procedure, a molecular formula is mandatory. The standard proton ( 1 H) spectrum is recommended to enhance the upcoming analysis. The multiplicity of the 13 C NMR signals can be deduced automatically from the complementary DEPT-90 and DEPT-135 spectra. The 1 H,X-HMQC/HSQC, 1 H,X-HMBC, (X = 13 C or, less frequently, 15 N), and 1 H, 1 H-COSY spectra constitute the minimum set of 2D NMR data necessary for automatic structure generation.
Dereplication Sherlock supports structural search dereplication, a lookup into a structure-to-spectrum database that prevents any re-elucidation of an already known chemical compound or to retrieve very similar ones  [2, 4, [6] [7] [8] 25]. A structure-to-spectrum knowledge base is necessary for this purpose (see above). The user can select parameters that influence the database screening and the filtering of the result list, such as the tolerance value or maximum allowed average deviation during the chemical shift matching between a 13 C NMR query spectrum and the ones stored in the database. Further details on how to operate the Sherlock system can be found in the user m ual which is embedded in the GUI and thus directly readable in the frontend on the tab. The user manual is available in the frontend repository (Section 5.
Dereplication Sherlock supports structural search dereplication, a lookup into a structure-to-sp trum database that prevents any re-elucidation of an already known chemical compou or to retrieve very similar ones  [2, 4, [6] [7] [8] 25]. A structure-to-spectrum knowledge bas necessary for this purpose (see above). The user can select parameters that influence database screening and the filtering of the result list, such as the tolerance value or m mum allowed average deviation during the chemical shift matching between a 13 C N query spectrum and the ones stored in the database. Spectrum comparison relies on a distance calculated for all valid signal pairs. A nal pair is considered valid if the chemical shift matching is successful. Complement criteria take into account signal multiplicity or equivalence and can be enabled or disab The closest valid signal pairs are then taken into account for the spectrum-to-sp trum matching.
Fragment Library A list of fragments which should be present in each solution, or goodlist  [26, 27] , can be forwarded to Sherlock. Such substructural information can dramatically shrink the number of possible constitutional isomers after structure generation. Moreover, providing fragments helps to cope with the lack of atom proximity knowledge in problems where proton-poor parts of a molecule exist and thus for which important structural information is unreachable by commonly used heteronuclear 2D NMR experiments HSQC and HMBC. Therefore, fragment data can lead to a noticeable reduction of the candidate list size or give hints to the user who might add complementary structural restrictions manually. [4, 5, 7, 8, 24, 28]  In order to build a fragment library all entries in the structure-and-spectrum database were used for the fragmentation.
Every fragment was created by spherical propagation following the instruction by Elyashberg et al. [8]. Starting at an atom in a molecule, specific conditions preserve connections between heavy atoms are applied to keep important substructural characteristics, such as no bond removal between carbons and hetero atoms or within a ring system if one of the atoms is a starting point  [8]. The fragment library consists of around 24. 5 million records. Each has a bit string representation to indicate whether a given chemical shift in the assigned subspectrum exists  [7, 8, 28, 29]. The database is screened via a bit string comparison during a fragment search where all set bits of a fragment have to be present in the query bitset. Afterwards, every fragment is ranked first by its number of heavy atoms and second by the same spectral matching procedure which is applied during the dereplication (Section 3.
Statistics-Driven Generation of Structural Constraints The elucidation process is further supported by the statistical analysis of the structure and spectra database for the determination of complementary structural restrictions. Sherlock is able to detect the likely hybridization states of atoms as well as their forbidden and mandatory atom neighbourhoods. The previously collected spectral database was used to count what hybridizations or connected atoms for a carbon atom appear. This information is coupled to a tuple consisting of 13 C chemical shift value, multiplicity and the elemental composition (MF) of a molecule so that for every 13 C signal in a query spectrum the probability of each hybridization state and of neighbouring atom type can be extracted. A chemical shift value, a multiplicity and a molecular formula need to be provided in order to request statistical information about hybridization states.
In addition, a lower boundary (in percent) is expected to define a minimum occurrence rate for each detected hybridization state compared to all hybridizations stored for a given shift range in the underlying database. If the frequency of a specific hybridization does not reach that given threshold (1% by default), then it will be discarded. Similar to the hybridization search, a minimum occurrence (1% by default) of a neighbour atom type is required. Otherwise, such an atom type will be treated as nonneighbor (forbidden) for the carbon atom(s) bearing that 13 C request signal. If an atom type appears more or equally often compared to the upper boundary (95% by default) it will be considered as a mandatory neighbour. All elements with an occurrence between those two boundaries might or might not be neighbours of an atom during the structure generation.
Structure Generation PyLSD  [12, 30] , a free and open-source powerful software for CASE, takes charge of the structure generation task in the Sherlock backend. It relies on the LSD  [11, 31]  structure elucidation software and provides the ability to deal with atoms with incompletely defined multiplicity or hybridization state. User-defined and automatically detected constraints are passed to pyLSD. Its built-in mechanism of solution ranking was disabled in Sherlock and replaced by a more recent tool (Section 3. 3. 5).
Spectra Prediction and Ranking PyLSD-generated candidate structures are ranked according to the similarity of predicted and measured spectra. Prediction relies on a HOSE code-based  [32]  approach commonly used in CASE systems  [2, 4, 7, 8, 12, 21]. The prediction tool in Sherlock makes use of stereo-enhanced HOSE codes  [18]. The number of spheres involved in the creation of the HOSE code library ranges from one to six. During spectra prediction, the HOSE code for the highest number of spheres is created first. If there is no matching entry in the knowledge base, the number of spheres is decreased until matching becomes possible or the number of spheres reaches zero. In the latter case, the prediction for the carbon atom is not possible since no values for a prediction exist. [21]  During the prediction, the number of HOSE code spheres in use, the number of entries as well as chemical shift range are stored to enable a posteriori quality assessment.
Conclusions An unambiguous interpretation of NMR data can be challenging, due to the many combinatorial possibilities that arise in constitutional space. To address this problem and to support organic chemists in structure determination tasks we introduced Sherlock, a free and open-source software for computer-assisted structure elucidation (CASE). Sherlock's functionality covers the common steps of structure determination. It provides the processing and visualisation of NMR data produced in common NMR file formats (Bruker, JEOL, JCAMP-DX). Here, the range of functionality extends from data processing to automatic peak picking up to a summary of all correlations between the different 1D and 2D spectra. Furthermore, a given molecular formula and the correlation information are used for the dereplication through a spectral knowledge base or the de novo elucidation of an unknown compound.
1) for a wide range of nuclei types, including 1 H or 15 N. Nevertheless, the presently implemented knowledge base contains 13 C information only and hence dereplication (Section 3. 3. 1), spectrum prediction and solution ranking (Section 3. 3. 5) rely solely on 13 C NMR. The incorporation of NMR data of other nuclei types (e. g. , 1 H, 15 N) and software developments will be necessary to expand the scope of these operations. Consequently, the CASE tab in the frontend will be extended in future works to enable for multiple spectrum-to-spectrum comparisons and the display of their results. The project descriptions and installation guides are available under https://github. com/michaelwenk/sherlock-frontend (frontend, accessed on 22 November 2022) and https://github. com/michaelwenk/sherlock (backend, accessed on 22 November 2022).
Acknowledgments: M. W. and C. S. acknowledge funding by the  Carl-Zeiss-Foundation. We are very grateful to the following people for providing us NMR data for test and validation purposes:  Nils Schlörer ,  Friedrich-Schiller-Universität Jena, Germany. Johannes Liermann, Johannes-Gutenberg-Universität Mainz, Germany. Huijuan Guo & Christine Beemelmanns, Leibniz-Institut für Naturstoff-Forschung und Infektionsbiologie Jena, Germany. Sarah Nies, Leibniz-Institut für Naturstoff-Forschung und Infektionsbiologie Jena, Germany. Luc Patiny, École Polytechnique Fédérale de Lausanne, Switzerland. Sebastian Kemper ,  Technische Universität Berlin, Germany. Torsten Schaller ,  Universität Duisburg-Essen ;  Germany. Furthermore , we thank the  NMRium and nmrXiv team  for the collaboration and technical support.
2006 Sboras et al. , 2010;; Walker et al. 2010; Georgiou 2019)  intersect  (and vice-versa)  the E-W striking normal faults of the Gulf of Corinth rift and its continuation towards southern Viotia (also written Beotia or Boeotia;  Jackson et al. 1982; Papanikolaou et al. 1988; Stewart and Hancock 1991; Roberts and Koukouvelas 1996; Roberts and Ganas 2000; Ganas et al. 2005 Ganas et al. , 2007b;; Kokkalas et al. 2007; Sakellariou et al. 2007; Tsodoulos et al. 2008). Our research methods include the processing and analysis of geodetic (InSAR, GNSS) and seismological data. First, we present a detailed analysis of the geodetic data. The SAR images acquired by the SENTINEL-1 satellites are routinely distributed free of charge by the European Space Agency (ESA). The GNSS data originated from three sources: (a) from a permanent station west of the epicentre (station THIV) that was provided by the Greek private GNSS network HxGN SmartNet, (b) the permanent GNSS station 014A belonging to the national GNSS network of Greece HEPOS (https:// www.
hepos. gr/ Map/ Senso rMap. aspx) and (c) the campaign GNSS station KALI that is part of the NOA local network KAPNET  (Ganas et al. 2007a; Marinou et al. 2015). We then derive a co-seismic fault model from joint inversion of geodetic data (GNSS and InSAR) assuming that the earthquake can be modelled by the slip on a rectangular fault buried in an elastic and homogeneous half-space. We also inferred kinematic source parameters by analysing waveforms recorded by the Hellenic Unified Seismological Network (HUSN) and the National Observatory of Athens Seismic Network. Our analysis aimed at the comparison of the two datasets (geodetic vs. seismological) given the very shallow depth of the slip centroid, as reported by NOA (2020). An additional aspect of our research concerns the rupture pattern of the active NW-SE striking normal faults in Viotia,, which continue southwards in Attica (e.
Sentinel-1 interferograms We produced co-seismic interferograms using Sentinel-1 radar imagery for both the ascending (track 102) and the descending (track 007) orbits. The two pairs of SAR acquisitions were processed with the SNAP v8. 0 ESA software (https:// step. esa. int). For both tracks, the pre-event and post-event acquisitions are on 27 November 2020 and on 3 December 2020, respectively. The interferograms were produced by complex multiplication of the primary (pre-event) image with the complex conjugate of the secondary (postevent) image. The phase of such a complex image (interferogram) is related to the ground displacement between the two images. The obtained interferograms are shown in Fig. 4a  and b ; in both cases, we observed about two fringes each (coloured from blue to red) corresponding to a motion away from the satellite.
Since the LOS displacement has a similar amplitude and pattern in both ascending and descending tracks, we can conclude that a vertical ground motion is predominant. Phase unwrapping was carried out by using SNAPHU  (Chen and Zebker 2002). We then applied a decomposition procedure to the ascending and descending unwrapped interferograms  (Wright et al. 2004 ) in order to obtain the vertical displacement map shown in Fig. 4c. We found that 7 cm of subsidence occurred in the hanging-wall of the Kallithea normal fault, which can be interpreted as the result of a coseismic motion along either (a) the Kallithea normal fault itself, running ESE-WNW and dipping to the south (fault code GR0608 in the NOAFAULTs database; Ganas 2022) or (b) along a 'blind' normal fault, parallel and synthetic to the Kallithea Fault whose geometry is unknown.
Co-seismic motion of the GNSS stations We analysed the daily data files (dual-frequency observations at 30-s sampling interval) of two permanent GNSS stations, station THIV belonging to the HxGN SmartNet and the station 014A belonging to the HEPOS Greek state network. Both stations are located to the west of the epicentre (Fig. 1 ). Station 014A is located about 5 km from the village of Kallithea (the location with the highest macroseismic intensity; see Fig. S1 ). The processing was done using the GIPSY-Precise Point Positioning (PPP) software and Fig. 5a , b shows the time series of the two stations. At THIV the position time series is 10-year long and stable. With this quality of data, the threshold of detectability is around 1 mm, therefore GNSS certifies that the displacement at this location is below this value (a similar result was obtained by  Elias et al.