dataesr/scientific-paragraphs-categorization huggingface dataset:
The other ('Issue') can be used specifically for raising concerns about problems identified in the data or code, either at the point of data upload or generation or through later analysis. The status of the dataset is dynamically propagated through the registry and hence is visible in the provenance of outputs generated using these datasets. In a similar way, datasets that have been simply superseded can also be flagged, and reports and other outputs based on them can then be identified. If such a report is used as the basis of (say) a briefing to government policymakers, it will be important to make it clear where it is indeed based on outdated information. The key outcome is that all the information required to contextualize the work is available in the provenance metadata. It is instructive to compare this situation with the more extreme case where a dataset is (say) discovered to have contained erroneous data for a period of time.
All versions of the dataset subsequently identified as erroneous can be flagged as invalid. All outputs derived from these datasets will have this invalidation in their provenance; reports can be withdrawn, and there should be no risk of any work subsequently using these invalid data without this being apparent in the provenance metadata. In this way, the registry is the primary source of information to derive retrospective provenance, i. e. a detailed log of the execution of the computational task, including user-defined provenance in the form of annotations  [34]. The full schema of the registry database, including other metadata such as information about software releases and DOIs for published data, is available in electronic supplementary material, figure 1.
Examples We have selected three examples to demonstrate features of the pipeline. These are not intended to push the limits of the software framework, but to provide simple to understand examples that can be replicated by the reader to get a feel for the complexity of using the FAIR Data Pipeline. The first is the reproduction in R of a simple Susceptible-Exposed-Infected-Recovered-Susceptible (SEIRS) epidemiological model used by Bjørnstad et al. [74]  to demonstrate disease dynamics. In the second example, we reimplement this model in all four native languages of the data pipeline (R, Java, Julia and Python) and cross-validate the results. Finally, we show a more complex, but nonetheless very simplified, time-varying model of COVID-19 dynamics with parameters extracted from English epidemiological data from the pandemic, and pull these into the pipeline and run a deterministic simulation of the pandemic using those inferred parameters.
(a) SEIRS model To demonstrate a simple epidemiological model being run through the pipeline, we take the example of the SEIRS model used by Bjornstat et al. [74]  and reproduce the results that lead to fig. 1b  in that paper. The full code is available online in an R package  [75] , which provides instructions for how to run it in the README. md file on GitHub. A vignette is also provided  [76]  showing the fully worked example with results. fair pull is used (figure  1 ) to populate the local registry with the parameters from the source manuscript (using a register block in the configuration file to ensure the data are in the pipeline). fair run is then used to execute the R script (the R code is executed from the root of the git repository via the script block of the configuration file).
The R code itself is only very lightly edited from an equivalent nonpipeline version, with the addition of initialise() and finalise() steps to start and stop the pipeline monitoring, and the replacement of hard-coded file paths with calls to link_read() and link_write() with references to labels given in the configuration file (by default the unique names under which they are stored in the registry). These functions simply return appropriate paths, so can be directly used in place of filenames in any normal R code. The output of this simulation is shown in figure  2    The SEIRS model described earlier was implemented in all four of the native languages of the data pipeline, and the Java  [77] , Python  [72]  and Julia  [70]  implementations can be found online. We ran all four models through the pipeline together and then wrote a small cross-validation script to compare the models.
The FAIR Data Pipeline was SCRC's response to this aspect of pandemic response. Querying epidemiologists involved in both human and animal disease modelling, we could identify no tools being used that satisfied either FAIR principles or which publicly presented the provenance Bayesian inference using the methodology described in  [80]  applied to a stochastic version of the model described here. Note that, although the latent and infectious periods are fixed, changing these quantities has the effect of rescaling the inferred R t about 1, but does not impact on the other parameters significantly. Lower panel: the deterministic SEINRD R code captures the first, second and third wave of the outbreak in terms of cases and deaths attributed to COVID-19. (Online version in colour. ) of research outputs.
We felt that the media storm surrounding some of the scientific advice during the pandemic demonstrated that such a tool would be valuable in improving trust in science used for public policy. In addition to the indirect and intangible costs arising from diminished public faith in science, there is also potential for wider efficiency benefits to accrue from developing and using software to support FAIR data management. An analysis of the qualitative and quantitative costs of not having FAIR data has estimated a negative impact of e10. what functionality a data pipeline would need to provide to be useful to not just to the epidemiological and other modelling communities, but also the broader lay and policy audience  [33]. After a review of software available for data management and reproducible research, we concluded that no suitable tools existed in the public domain that could be easily adapted to these uses.
We therefore developed the new open source suite of tools for FAIR management of data and models described here to improve the openness of science for policy and better support the traceability of evidence, with as low a barrier of entry as we could devise. Although a small amount of friction remains, our intention has been to ensure that there are benefits for all potential users that can be realized in only a few minutes: for instance, for modellers, the ability to automatically trace the provenance of the output of an existing model with only a single short configuration file to describe any input and output data, and minimal changes to the code; for a data manager or user to look up a file in a data registry and see what (if any) metadata is held on it, and what other versions of that data product also exist; or for third parties, the ability to look up a model output in the same data registry and see its provenance immediately, linked directly back to the code and data used to create it.
Other, more immediately pragmatic requirements include the ability to identify analyses and outputs that depend on outdated code or data; an ability to validate or invalidate a past analysis conditional on the current status of its underlying assumptions and data; and an ability to retrospectively reconstruct the validity of an analysis at a previous point in time, given the status of the underlying assumptions at that time. We anticipate that these key functions will be particularly important to those specialists brokering evidence across the science-policy interface. Where the domain expert makes metadata detailing the provenance of a dataset or codebase available, the interpretation of these in terms of applicability and validity can be assessed and challenged by their technically proficient peers.
(ii) Metadata automation At the moment, several aspects of pipeline use are manual where they could be automated. These include (i) the attribution of authors to software, which could be taken from CITATION. cff or. zenodo. json files, or other metadata in the git repository; (ii) the integration of metadata (including authorship) into the pipeline from DOIs associated with data as it is ingested; (iii) the automatic creation of Issues and CodeRepoReleases in the pipeline from issues raised and releases created in GitHub; (iv) the automatic generation of persistent identifiers (e. g. DOIs) when ExternalObjects are created; and (v) improving the ease of syncing metadata between local and remote registries. Integrating this functionality into the pipeline is desirable, since it will further increase ease of use, reducing the barrier of entry for new users.
Finally, we need to integrate the ability to use different storage engines as backing stores for the pipeline. Data accessibility. All code is available through the GitHub organization FAIRDataPipeline-https://github. com/FAIRDataPipeline-and other materials are available through links within the paper. Code directly related to the data pipeline also has DOIs from Zenodo (see table  1 ). All code will be published with permanent DOIs on Zenodo before publication. The data are provided in the electronic supplementary material  [82].
Both R t and e t vary with time and are driving variables for the model. As indicated, case and death rate data inform the transitions shown, whereas PCR and seroprevalence data from the Coronavirus Infection Survey [79]  inform on the numbers of individuals in the compartments I + N and R, respectively. With the exceptions of m E = 4 days and m I = 4 days, all model parameters were determined via Bayesian inference using the methodology described in [80]  applied to a stochastic version of the model described here. Note that, although the latent and infectious periods are fixed, changing these quantities has the effect of rescaling the inferred R t about 1, but does not impact on the other parameters significantly. Lower panel: the deterministic SEINRD R code captures the first, second and third wave of the outbreak in terms of cases and deaths attributed to COVID-19.
Acknowledgements. This work was initially undertaken as a contribution to the  Rapid Assistance in Modelling the Pandemic (RAMP) initiative , coordinated by the  Royal Society. We are very grateful to all of the volunteers in the Scottish COVID-19 Response Consortium formed as part of that initiative -https://www. gla. ac. uk/ scrc -who contributed to this project, and in particular to the team at  Man Group  for their assistance in designing and helping to build the initial version of this pipeline through the  Royal Society's RAMP initiative , and the team at  Invenia Labs -Eric Perim Martins ,  Bella Wu ,  Sean Lovett  and  Alex Robson  -for their invaluable assistance with the Julia implementation.
Funding. The work was funded by the  Science and Technology Facilities Council  under grant no. ST/V006126/1 ,  Biotechnology and Biological Sciences Research Council  (grant nos. BB/M003949/1 ,  BB/R012679/1  and  BB/S001034/1 ),  Engineering and Physical Sciences Research Council  (grant nos. EP/T004878/1  and  EP/V054236/1 ),  Medical Research Council  (grant nos. MC_UU_00022/2  and  MR/R00241X ),  Natural Environment Research Council  (grant nos. NE/T004193/1  and  NE/T010355/1 ), the  Scottish Government Rural and Environment Science and Analytical Services Division  (grants ' Centre of Expertise in Animal Disease Outbreaks ' and ' Strategic Research Programme '),  Scottish Government Chief Scientist Office  (grant  SPHSU17 ), the  UK Atomic Energy Authority , supported by  BEIS , the  French National Research Agency (ANR)  ( IDEXLYON  project, grant no.
INTRODUCTION The far-UV circular dichroism (CD) spectrum of a protein is characteristic to the secondary structure composition and is widely used to investigate protein structure. Although it does not provide site-specific structural information, CD spectroscopy is useful when a fast, inexpensive technique is needed or the application of high-resolution techniques (X-ray or NMR) is problematic. Applications cover all areas of protein science. CD spectroscopy can be used to ver-ify the correct fold of recombinant proteins, study the effect of environmental conditions (pH, ionic strength, additives, crowding) and protein modifications (e. g. mutations and post translational modifications) on the structure and stability. Moreover, CD spectroscopy is a suitable technique for the experimental verification of the structural information predicted by bioinformatics tools that make use of the ever-growing protein databases.
Protein fold prediction by BeStSel Protein folds can be characterized by specific secondary structure patterns. The eight secondary structure components of BeStSel provide sufficient structural information to predict the protein fold. The ␤-sheet composition, including the parallel-antiparallel ␤-sheets and the level of twist in the antiparallel ␤-sheets adequately characterize the diverse folds with ␤-structures, while the two ␣-helix components reveal the number and average length of ␣-helices in the proteins (4). In the BeStSel package, the CATH protein fold classification is used  (8)  distinguishing hierarchical levels of protein fold from Class to Architecture, Topology, Homology (superfamily) and further levels. The advantage of using CATH is that most of the protein domains in the PDB are classified and the database is continuously maintained.
Every single protein structure of the PDB can be represented as a point in the eight-dimensional secondary structural space of BeStSel and, in turn, the result of the BeStSel analysis of the CD spectrum can also be projected to this space. To find the fold of a protein, we search for points representing PDB structures that have similar secondary structure composition based on their Euclidean distance and then we determine their fold classification. However, the regions that different protein folds occupy in the eight-dimensional secondary structure space can be overlapping and it might be challenging to find the correct fold. The BeStSel package offers various different methods for fold recognition. A simple method performs a search on the entire PDB database for 20 structures closest to the target structure in Euclidean distance in the eight dimensional secondary structure space.
The CATH classification of the corresponding structures are listed. In the case of multidomain proteins, it might be difficult to pinpoint the correct fold. To predict the fold of single domain proteins, we use a reference database containing a non-redundant (95% maximal sequence identity) single domain reference subset of CATH 4. 3  (5). The corresponding secondary structure compositions are calculated from the PDB structures of the domains. This subset contains 61 932 single domains covering the five fold classes, 43 architectures, 1467 topologies and 6540 homologies. Three prediction methods were constructed. (i) Search for the closest structures in the Euclidean space. This is useful for structures lying in a rarely populated part of the fold space. (ii) Search for all the chains that lie within the expectable error of BeStSel secondary structure determination, more exactly, within a distance of 1.
The operation of the BeStSel web server The BeStSel web server is freely accessible. A detailed guide is provided in the tutorial file, which can be downloaded from the website in PDB format. The homepage also provides short explanations and tips for users. Error messages explain if input data format is not suitable. Warning messages draw the attention for possible problems like abnormal spectral amplitudes, which can be a result of improper data normalization or CD unit choice. The server provides 8 program modules, Single spectrum analysis, Multiple spectra analysis, Fold recognition, Secondary structure decomposition for 3D-structures, Calculation of extinction coefficients from the primary sequence, Disordered-ordered binary classification, a searchable collection of publications using CD spectroscopy with BeStSel analysis, and a Guide to CD spectroscopy and data analysis helping CD users.
A schematic diagram shows the modules and function of the web server in Figure  1. In Single spectrum analysis, a CD spectrum can be uploaded and analyzed by the BeStSel method for secondary structure content. Data can be copied to the text window in the form of two columns or can be uploaded from txt files. The program automatically recognizes the file headers and in case of data pitch different from 1 nm, sorts out and uses integer nm data for analysis. Measurement files of various instruments saved in text format are handled properly by the server. Input units can be ε (M -1 cm -1 ), [ ] (mean residue ellipticity in deg cm 2 dmol -1 ) or measured ellipticity in mdeg units. In the latter case, concentration, residue number and pathlength data should be given and the server will normalize the data to ε.
The output of fold prediction is a list of the highest ranked 1, 5, 10 and 15 CATH classes, architectures, topologies and homologies, respectively. We recommend the WKNN method for structural studies to discover the fold of model structures or structures originated from the PDB. Multiple spectra analysis will analyze a series of CD spectra simultaneously which might be helpful when multiple spectra are collected as a function of ligand or denaturant concentration, temperature, time, etc. Data table can be copied to the text window or can be opened from a text file. Input units are the same as in Single spectrum analysis. After 'Data examination' secondary structure contents are calculated by a single click and presented either as a graphical output or can be saved in. txt or. csv files for the convenience of the users.
Wavelength range and a scaling factor can be set for recalculation the same way as in Single spectrum analysis. The Secondary structure from PDB files module calculates the eight BeStSel components and for comparison, DSSP  (7)  and SELCON3 (13) composition for 3D structures. For structures deposited in the PDB the input is the four-letter PDB ID and the program will provide the CATH information as well, if exists. Structural files in PDB format (max. 20 MB) can also be uploaded to the server and the calculation will be carried out automatically. Both graphical and text output are available. This module is especially useful for experimental verification of MD results or in silico models by CD spectroscopy, making the structural information comparable (see in Result and Discussion). The Extinction coefficient calculation module provides the extinction coefficients of proteins and peptides at 214 nm  (14)  and 205 nm  (15) , based on their primary sequence and number of disulfide bridges.
The amino acid sequence should be entered or copied to the text window. The extinction coefficients can be used for concentration determination directly on the CD sample. The Disordered-ordered classification module analyses far-UV CD data to identify disordered structures. The clas- A series of CD spectra as input can be evaluated at once to get the secondary structure contents. Users can provide arbitrary secondary structure contents and carry out the fold prediction for that secondary structure composition. Users can also enter PDB IDs or upload structure files in PDB format as input to find the corresponding secondary structure contents and fold classification. Based on the CD data, a binary ordered-disordered classification can be carried out. To aid correct concentration determination, extinction coefficients at 205 and 214 nm can be calculated from the primary sequence of the protein  (14, 15).
sification is based on CD data at three wavelengths (197-206-233 nm or 212-217-225 nm triplet). Data can be copied into the text window. The first column contains the wavelength values and the other columns contain the corresponding spectral data. Entire spectrum, series of spectra, or CD data only at the necessary wavelengths, all will be accepted and handled properly. The output is a table containing the CD data at the wavelength triplet used for the classification and the predicted results. The Guide to CD and data analysis opens a separate window with practical and important considerations for CD spectroscopy measurements. Cited by. opens a separate window and provides a database of scientific articles that used CD spectroscopy with BeStSel analysis. Article identifiers and keywords are provided, and the collection is searchable showing useful examples of using CD spectroscopy and BeStSel for the convenience of the users.
Moreover, BeStSel provides extra structural information, which is sufficient for protein fold prediction down to the topology/homology levels of CATH fold classification. In the present upgrade, BeStSel basis spectra were re-optimized by using the DSSP 3. 0 algorithm to assign the major secondary structure components in the reference database. In the earlier version of DSSP, residues in -helices might have been erroneously assigned to ␣-helix. Moreover, the BeStSel basis spectra were calculated for new wavelength ranges, now available at 5 nm increments starting from 175 nm. Supplementary Table  S1  shows the performance compared to the previous version of BeStSel on the reference database. Overall, the accuracy of the method is improved, e. g. in the 190-250 nm wavelength range, the RMSD for ␣-helix estimation decreased from 0.
052 to 0. 042 (Supplementary Table  S1 ). Performance was also tested on an independent set of ␤-sheet rich or rare protein structures and compared to other available methods for secondary structure estimation (Supplementary Table  S2 ). Calculated to a common basis of helix, antiparallel ␤, parallel ␤, overall ␤-sheet and 'turn + others' structures, the RMSDs for secondary structure estimation were proved to be 0. 034, 0. 049, 0. 037, 0. 035 and 0. 038 for BeStSel, while the other methods provided RMSDs in the ranges 0. 083-0. 26, 0. 12-0. 214, 0. 076-0. 198, 0. 068-0. 23 and 0. 074-0. 232, respectively. None of the previous methods performed evenly for the different secondary structure components. The Fold prediction module was upgraded from CATH 4. 2 (16) to using CATH 4. 3 (5) data, resulting a significant increase in the number of protein folds at all levels of classification.
Supplementary Table  S3  shows the theoretical reliability of fold prediction on the domains of CATH 4. 3 (5) as secondary structure inputs in a 5-fold cross-validated manner. A new function at the webserver is the Disordered-ordered binary classification of proteins based on their CD spectra  (11). Such classifier, using experimental data has not been available yet and is highly needed by the community studying intrinsically disordered proteins (IDPs). It can be used for simple and fast experimental verification for a variety of bioinformatics tools identifying IDPs and can also facilitate the growth of experimental data in IDP databases, such as DisProt  (17). The method uses the k-nearest neighbors mathematical model with cosine-distance function on CD data at three wavelengths  (12). Using the 197-212-233 nm wavelength triplet, the estimation error is 4.
7, 1. 7 and 3. 9% on ordered, disordered proteins and in overall error, respectively, on the dataset of CD spectra with 190 nm wavelength cut-off  (11). For a cut-off at 200 nm, using the 212-217-225 nm wavelength triplet, the error of classification is 3. 3, 7. 5 and 4. 6%, respectively. The functionality of the BeStSel webserver is compared to the other available online tools in Table  1. Besides its superior accuracy and the more detailed secondary structure information, the BeStSel webserver has an intelligent interface and provides useful functions for CD spectroscopy users.