dataesr/scientific-paragraphs-categorization huggingface dataset:
220, 221 Further evaluation involved data collected at 600 MHz. 220 Shown in Fig. 26  is that several resonances are broadened and overlapping. This confounds the task of extracting many J values, so many signals were listed as "m" in the original publication. 217 The second generation analysis at 600 MHz 220 included obtaining NOE data and remeasuring the J values for H-15 (5. 62 ppm) as a dtt (J ¼ 10. 7, 7. 5, 1. 5 Hz) prompting the reassignment of the C-14, C-15 geometry from E to Z (Fig. 26 ). New FIDs have been obtained for MYC and are available as electronic information. Presented below are examples for which obtaining new FIDs enable accurate measurement of J HH and J HC values for the rst order or non-rst order multiplets. The rst example involves closely overlapping resonances of olenic hydrogens H-6, H-14 and H-15.
The data in Fig. 27C  and D  provide additional coupling values for H-14 and H-16 previously described simply as multiplets. 217 milar outcomes are shown in Fig. 28  and 29 that more accurately describe the coupling patterns of olenic hydrogens (H-5, H-17) and aliphatic hydrogens (H-3 0 , H-7, H-7 0 ). The previous data from measurement in CDCl 3 reported most of these resonances as multiplets. Alternatively, analysis of these resonances by either rst order or non-rst order signal tting accurately provided the eleven J values shown. These data should be useful in the future as new MYC analogues are isolated or synthesized. The value of obtaining and using HMBCderived 1 J CH data to make functional group assignments for compounds possessing ratios of H/(C + Z) < 0. 5 was recently demonstrated.
As loss of resolution is almost always inconsequential for measuring 1 J CH , except for very weakly dispersed signals, this can be an effective way to tease out important information from FID data made available in digital format. (ii) Spectral overlap or complex multiplet structure. 13 C satellites that exhibit complex multiplet structures, due either to overlaid homonuclear coupling ( n J HH with n ¼ 2, 3, etc. ), or symmetry-related reasons, may completely 'disappear' beneath the noise or be obscured by nearby 1 H signals. Fortunately, only one half of the  13  C satellite doublet signal needs to be observed as the 1 J CH is reconstructed from twice its separation from the dominant centroid  12  C-1 H signal (ignoring the slight isotope shi of the former). Here, a caveat should be stressed: the sample should be sufficiently pure that spurious impurity signals are not mistaken for genuine  13  C satellite signals.
All this, from no more than a re-processed 1 H NMR spectrum, accessed from archived digital FID data. An enhanced HSQC experiment for an accurate and more rapid assessment of one-bond proton-carbon coupling constants has been reported very recently. 236  variety of 2D NMR methods have been developed that enhance the utility of C,H-coupling information in NP research, covering both direct ( 1 J C,H ) and longer-range ( $2 J C,H ) coupling relationships. Examples are the ASAP variant of HSQC  237  and the establishment of NOAH supersequences  238  for accelerated acquisition, non-uniform sampling (NUS)  239  and CRAFT 2D processing  240  techniques for enhanced resolution, as well as LR-HSQMBC and HSQMBC-TOCSY for improving the detection of long-range correlations. 49, 50 2 New analysis of published data by optimal processing of the FID NMR data can provide a wealth of information regarding a given chemical structure and much of this information is frequently overlooked.
Martin and co-workers have demonstrated several such cases, using both an NMR database and a commercial algorithm to predict  15  N chemical shis with only 2 ppm error, while most unfavorable result produced a variation as large as 50 ppm difference. 309 Even long-range 1 H-  15  N coupling constants are accessible through this method, with analogous limitations of currently achievable accuracy. As predictor algorithms depend on spectra derived from (manually) interpreted data, this method will become more accurate if the calculations could be based on raw NMR data. Consideration of  15  N coupling constants adds another layer to structural elucidation of nitrogen-containing molecules. Although 1 H-  13  C heteronuclear long-range couplings are generally uniform, the same cannot be said about the corresponding 1 H-15 N couplings.
Access to raw heteronuclear NMR data would much facilitate the analysis of high quality original 1 H NMR data of N-containing NPs. However, most current databases do not support this kind of data mining, because the stored data is interpreted information rather than raw data. Recently developed repositories such as Protein Chemical Shis 312 are no exception. Other typical examples of obscured NMR information is the use of "multiplets" (m) to describe signals with more than two or three spin-spin couplings. This particularly affects signals with small couplings, such as the long range 1 H-14 N couplings, which otherwise could help exploit the C-H orientation in three bond anticlinal or synclinal arrangements relative to the lone pair electrons. Collectively, the above points clearly support the importance of raw NMR data for heteronuclear NMR in the nitrogen domain, encompassing both 14 N and 15 N effects.
Databases But this digitization also came with adverse side effects. As every manufacturer developed new features, used different digitization technologies, and different acquisition methods, the complexity of the le formats increased. Current multi--vendor NMR soware accommodates up to 20 raw data formats, not counting the different avors of each of these formats. In addition to these differences, the storage methods are different: some use a single le for the acquisition, others require multiple les, whereas even others require a particular directory structure to be functional. Current conversion solutions oen involve juggling between several le formats and soware tools in order to obtain input data that is compatible with a given NMR soware. Such an elaborate process is detrimental to the integrity of both data and associated metadata, as it implies conversion between different oating point and/or integer encoding schemes and introduces rounding errors.
The single le using the Allotrope Data Format (ADF), contains the original data le, and the treated data (in a standardized but still evolving form). Further, it provides a way for the equipment, people, processes, geographical locations, and projects to be linked and described. The foundation develops standardized vocabularies and data descriptions. With the use of ontologies and semantic web technologies, all of these elements can be linked to other resources (online databases or internal repositories) and annotated appropriately. This joint effort aims to unify current analytical data, including NMR data, and allow NMR records to survive the test of time, crashing hard drives, and the confusion of a myriad of formats.
8. 2 The urgent need for spectral repositories and automation support for peer-reviewing of spectral data Structure elucidation of organic compounds starts with experimental data, transformed into spectra, and translation into present/absent structural fragments. The nal step combines these fragments into a structure proposal tting the given constraints derived from the experimental data. This process can fail and lead to a wrong structure, because this sequence can be broken at any point. 19, 121, 330, 331 In order to detect wrong conclusions, it is oen necessary to trace back to the raw data. Furthermore, there is a major need for validated reference materials of authenticated chemical structures in order to build spectral databases that can fully support the process of upcoming structure elucidation problems.
Databases for dereplication The number of known NPs has grown, and continues to grow at a remarkable rate, swelled by over a century of research into terrestrial and marine plants, animals and microbes. A signicant proportion of these NPs were rst described in the scien-tic literature prior to, or in the formative years of NMR-enabled structure elucidation. Notwithstanding the many impressive feats of structure analysis achieved in those early years, where available, published accounts of NMR data from this era predating modern high eld and 2D NMR spectroscopy, are limited to tabulation of 1 H NMR (and later, possibly  13  C NMR) resonances. By necessity, NMR assignments were oen either incomplete or tentative, with some resonances described merely as broad singlets or unresolved multiplets, and overlapping resonances collectively listed as "envelopes", or not listed at all.
The importance of raw data in databases This Raw Data Initiative is a continuation of previous efforts 1 to initiate the creation of a repository for raw NMR data dedicated to small molecules, especially those of biological origin. One major goal is to minimize the time taken to identify known molecules and therefore to avoid the duplication of structure determination efforts. Current practice involves extraction, isolation/purication, time-domain NMR data acquisition, conversion of these into the frequency domain, extraction of spectral parameters (chemical shis and coupling constants) and proposal of a chemical structure by deductive reasoning. A search of available databases provides a high probability that the compound is novel or known. Even in the latter case the task is not nished until a search of the literature conrms that the spectral data of the isolated compound matches that of the known.
347 he Institute of Molecular Chemistry of Reims, France, is putting together a focused library of raw, time domain NMR data and transformed data that is linked with enhanced structure les, i. e. , Structure Data Format (SDF) les in which atoms may be arbitrarily tagged. These tags are connected to chemical shis values and paired to coupling constant values. Data was obtained from a small library of glucosinolates and of their desulfated derivatives using the PERCH soware,  62, [348] [349] [350] [351] [352]  in a process that is similar to the constitution of the MetIDB database. Such a protocol is the only one that ensures a realistic transposition of a 1D 1 H NMR data from one static eld value to another for comparison purposes. The same research group has also designed a dereplication workow based on  13  C NMR data and is used for the analysis of complex plant extracts.
353 The 13 C NMR spectra of the samples produced by Centrifugal Partition Chromatography fractionation are binned and the bin contents are classied according to the resemblance of their chromatographic proles. Sets of chemical shis with similar proles constitute keys to the search for known compounds in a locally developed and enriched database that links structures and  13  C NMR chemical shis. The latter are obtained by prediction from structures by means of commercial soware. The availability of raw NMR data reference compounds would contribute immensely to the progression of efficient dereplication tools.
The breadth of databases and their use by chemists The past 10 years have witnessed the development of a global network for data sharing of spectroscopic and chromatographic data in metabolomics. 354, 355 The principles behind this data sharing network have been established over the past 20 years by the molecular biology community in areas such as genomics and proteomics. 356 Since the eld of metabolomics is populated by a considerable number of analytical chemists there is now hope that the principles of free and open data sharing will be more widely adopted by the quite conservative chemistry community. This hope is supported by initiatives like the FAIR data movement,  357  which advocates for research data being Findable, Accessible, Interoperable, and Reusable (FAIR), in order to properly support the scientic methods.
These are gradually being adopted by major funding agencies such as the European Commission, the National Science Foundation, the National Institutes of Health and many other funding organizations. The fundamentals of metabolomics data sharing are not very different from those in NP chemistry. Data in Metabolites and other databases are composed of a core of raw data surrounded by metadata aiding in the interpretation of the experiment. Whereas the raw data consists of NMR, MS, and chromatographic data, the metadata covers the whole range necessary to understand how the study was conducted: the species under investigation, which organism part the metabolite was isolated from, which instruments were used, and which parameters were used. Every data point in the above-mentioned metadata is backed by a term in a commonly used ontology, and the question which type of metadata needs to be reported is dictated by the minimum information standard.
For metabolomics these minimal information (MI) standards were created by the metabolomics standards initiative around the year 2007. 358 ost of the minimum information principles established by the MSI are directly applicable to the NP community. Additionally, information from the 2015 initiative to establish the minimum information about a biosynthetic gene cluster are relevant. 359 he molecular biology community, in establishing databases The computational frameworks to hold and describe raw data and metadata are equally in place. The ISA format, for example, is widely used across domains in molecular biology and metabolomics in particular. 363  It is capable of holding all the necessary metadata of an investigation, study and its underlying assays (ISA ¼ Investigation, Study, Assay), in a spreadsheet-like format, backed by a wide range of ontologies, including the NMR term ontology established as part of the nmrML work.
Today, the main use for MRS is to distinguish a brain lesion as a tumor or non-tumor. The most common metabolite signals used to identify a tumor are a decreased Nacetylaspartate/choline and increased lactate-to-lipid ratios. Reecting the belief that the power of MRS for chemical imaging of the brain and other organs is enormous, raw MRS data is being revisited with machine learning and other mathematical tools, beginning with a large database of pediatric brain tumors. Starting with this particular database is important as recent technical improvements in MRS allow the iden-tication of other tumor-specic markers that can help classify tumor biochemistry,  364, [369] [370] [371] [372]  and by progress in applying new analytical tools to increase the number of identiable signals. Using newly developed normalization and other mathematical tools,  373  proof of concept has been generated for the identication of >90 signals from brain MRS data of pediatric concussion subjects.
Similar prospects for the utility of raw NMR data disseminated via an open database concern other forms of in vivo NMR spectroscopy, including a 1D 1 H or  31  P experiments aimed at the chemical analysis of tumor and other pathological tissues. The ability to quantitatively assess contributions from certain identied metabolites can provides valuable information for subsequent patient treatment and open opportunities for individualized medicine. Comparing the development of mass spectrometry (MS) and NMR spectroscopy in terms of databases and computational tools clearly indicates that data simplicity has been the main driver of the use of such data: both the ions in MS and the singlets of 1 H broad band decoupled  13  C NMR data can be represented as x,y-pairs (MW or chemical shi, respectively, and intensity) and, thus, can readily be transformed into search algorithms.
As coupling or other connectivity/spatial relationship information is invovled in nearly all other 1D and 2D, NMR spectra are inherently more complex and mostly evade a simplistic treatment. However, the development of the CSEARCH database (http:// nmrpredict. orc. univie. ac. at/) for the systematic mining and use of  13  C NMR data can serve as an excellent example of the information content of NMR spectra in general. Especially for  13  C NMR, the chemical shi value of a given carbon atom is highly characteristic of its chemical environment in a given molecule. In fact, deviations are so small (in the low ppb range) that even the absolute conguration of monomeric building block in oligomeric compounds can be achieved  183, 184  and subtle differences in the diastereoisomerism of closely related congeners can be recognized.
60 Importantly, for general applications,  13  C NMR enables structural dereplication with extremely high degree of certainty, provided that adequate acquisition conditions are employed to ensure comparability of the data sets (e. g. , concentration range, solvent, temperature). The CSEARCH database clearly highlights both, the dereplication of  13  C NMR and the necessity to make NMR raw data accessible to the scientic public. The database has been built over decades by transferring tens of thousands of assigned NMR data sets in combination with the structures derived, from peer reviewed journal sources into a digital format. From this starting point, data comparison, and shi value statistics and shi valuestructure motif correlations were made possible. Taken together these two contributions, which do cover more than a decade of scientic progress, prove that improvements in NMR data handling, data interpretation and data presentation are still needed.
The urgent need for public dissemination of raw NMR data This review aims to increase the awareness of NP scientists, and the entire chemistry community, to the advantages of modern soware tools to extract meaningful data from raw FIDs and avoid the loss of information in the process of NMR data interpretation and documentation. This urgent call for a change in scientic dissemination practices is supported by a global group of scientists, who contributed to the present review (Fig. 38 ). Specically, this group advocates universal depositing of these FIDs in free access databases, institutional repositories, or at least in investigator-initiated ESI † les so that the FIDs will be available to scientists worldwide, not the least to manuscript reviewers. Establishing the public sharing of raw NMR data as a standard practice will create both obligations and opportunities that engage authors, reviewers, and readers equally.
This creates a mutually benecial push-pull relationships that can only enhance the integrity of science and reduce the occurrence of incorrect published structures,  19, 22, 23  especially when considering that researchers essentially serve in all three roles simultaneous, i. e. , as authors, reviewers, and readers. Although a number of databases exist, there is no universally accepted format, especially for crucial FID-associated metadata, such as, solvent, temperature, concentration, instrument, eld strength, and charge (i. e. , pH or more likely pD) for spectra of compounds with ionizable groups. This review reports on at least two examples where conclusions have not yet been reached because spectra of the same (or not) compound have not been identical, almost certainly because spectra were taken of samples with different degrees of ionization.
In general, this is particularly a problem with peptides. Fortunately, the reporting standardization including metadata aspects may be addressed as IUPAC has put together a Project Task Force to address just this problem. A global, universally accepted database is an enormous task. Its feasibility will depend on an adequate combination of international coordination, funding, and sustainable mechanisms, most likely required by "rst world" countries. Historically, funding and sustainability have restricted most existing databases. The rise of distributed databases, linked data, and data-interoperability consortia could provide alternative monolithic data-silos that are difficult to maintain. In fact, these approaches are more likely to ensure viability, accessibility, and achievement of overall project scope for a global, universally accepted database containing raw NMR data.
Evolution of raw NMR data repositories The term "database" is used frequently to refer to gathered digital content of any kind, stored in a binary or text format, using a more or less pre-dened structure, e. g. , that of a relational or NoSQL database. For the purpose of this discussion, the "container" is dened as a database, the content as (raw NMR) data, and the combination of the two as a repository. Moreover, as the term "data" has a very broad meaning, in the present review, data refers to the combination of experimental data (i. e. , the information/data obtained during the NMR experiment, in particular the FID) as well as the metadata that are necessary to reproduce the experiment (e. g. , eld strength, pulse sequence, solvent, concentration, temperature, etc. ). Oen, additional information is important or helpful, but not strictly necessary to reproduce an experiment.
Such data can be referred to as ESI † and encompasses, e. g. , patient information from the clinical trial that produced the NMR samples that enables statistical analysis. The establishment of a (raw NMR data) repository encompasses two principal steps: (i) denition of the information that is intended to be stored, including which experimental and ESI † is required and/or optional; (ii) conception, structure, and IT aspects of the repository itself. Both choices are critical as they have implications for the maintenance and evolution of a repository, especially when it is intended for long-term service. Migration of information from one database (container) to another is typically possible, with effort depending on the database technology. Despite this basic exibility, it is not possible to recover information that has not been stored to start with.
While this may sound trivial, it highlights in fact key points of the present article: (a) diligence and inclusion are paramount; (b) data which has not been stored in the past -as is the case with the majority of the experimental NMR spectra acquired since the inception of FT-NMRis irrecoverable; and, therefore, (c) building of such a repository is a timely and urgent task. Another conclusion from the general portability of a database is that, as long as the stored information is in denite format and structure, annotated, and accessible, the container itself is irrelevant to the data. However, the container is most relevant for the users as it is what scientists are interacting with. The availability of modular and publicly accessible APIs is mandatory to make the data meaningful. Under these conditions, the development of the repository, its data structure, and its storage technology can be handled separately, as long as the scope of each aspect has been dened.
384 A global repository should be able to deliver a permanent identier, such as a DOI, for each deposited object including the NMR experiment, the relevant molecule(s), assignments, linked publications, etc. Any objects based on information that are already stored in the repository can be used to generate a permanent hyperlink-like structure that connects, e. g. , assignment to the associated spectra and publications to assignments. In addition to these fundamental functions, and depending on the particular research area, databases may offer more "intelligent" functionality to the repository, such as advanced browsing or interfaces for novice users. The NP community has a high demand for tools for dereplication and identication, including separation, isolation, structure elucidation, and metabolic proling.