dataesr/scientific-paragraphs-categorization huggingface dataset:
AND FUTURE WORK This paper presents ShapeGuide, a 3D interaction technique allowing users to modify parameter values of native CAD data by directly pushing/pulling its surfaces in an immersive environment. ShapeGuide proposes users a set of shape variations by computing several meshes from different parameter values. Consequently, users can select the desired shape from a 3D hand motion with an optional force feedback guidance during shape modification. We conducted a controlled experiment to investigate the efficiency of ShapeGuide for industrial CAD part parameter modifications. We compared it with a one-dimensional scroll technique (Scroll), which enables participants to manipulate parameter values with a left-right hand motion. The impact of attractive force feedback to assist the parameter value setting was also investigated for both interaction techniques.
Furthermore, we plan to scale the scene according to the detected distance between sub-part meshes on shape computation. Previous research work attempted to decrease the number of iterations between workstations and VR platforms by allowing users to directly modify CAD data during immersive design reviews. From the experiment, we observed that ShapeGuide significantly enhanced the previously used scroll-based interaction technique. Through ShapeGuide, non-CAD experts can easily access and manipulate parameter values without a deep understanding of the internal organization of CAD data. As a consequence, it reduces misunderstanding or misinterpretation errors that could occur when non-CAD experts specify modification requests to CAD engineers. In the experiment, we imposed static values of the number of computed shapes and parameter step size for comparison purpose.
FUNDING This research was partially supported by  RTA Digiteo and Labex DigiCosme (Idex Paris-Saclay   ANR-11-IDEX-0003-02 ), and by  EquipEx DIGISCOPE  ( ANR-10-EQPX-26-01 ) operated by the  French National Research Agency (ANR) ,  as part of the program Investissements d'Avenir.
We demonstrate that this is indeed possible with a test example of JEZEBEL (PMF001) drawn from the ICSBEP handbook, comparing k eff directly calculated with SERPENT to those predicted with NDaST. Furthermore, we show that one may retain the normal NDaST benefits; a deeper analysis of the resultant effects in terms of reaction and energy breakdown, without the normal computational burden of Monte Carlo (results within minutes, rather than days). Finally, we assess the rationality of using either full or HMMs, by also using the covariance data to do simple linear 'sandwich formula' type propagation of uncertainty onto the selected benchmarks. This allows us to draw some broad conclusions about the relative merits of selecting a technique with either full, half or zero degree of Monte Carlo simulation.
Introduction The TENDL evaluated nuclear data library  [1] , produced by the nuclear data modelling code TALYS is produced according to the Total Monte Carlo (TMC) and Unified Monte Carlo methodologies  [2, 3]. As a result, many randomly sampled realisations of the nuclear data are generated À in some cases these are made available to users. For the TENDL-2014 release, processed files in ACE format, compatible with the neutronics codes such as MCNP, SERPENT, etc. , are also provided. 1  This conveniently allows us to test the supposition, that the difference in k eff with each of these random files, relative to the 'zero-th' (nominal) file can be predicted by Nuclear Data Sensitivity Tool (NDaST)  [4] , within acceptable accuracy. These Dk eff values, corresponding to the uncertainties from the nuclear data evaluation, can be calculated using the relative perturbations automati-cally generated by the tool and the multi-group sensitivity profile retrieved from the DICE database  [5].
Brute Force Monte Carlo (BFMC) The methodology, on occasion known as Brute Force Monte Carlo (BFMC)  [11]  (not to be confused with Backward-Forward Monte Carlo), involves the user producing their own random library samples, based upon the covariance data representation within the evaluated file. It can therefore cope with non-linearity in the responses between cross-section changes and k eff and does not require sensitivity data. It is therefore generally applicable to almost any code and data combination, since only the input information needs to be re-sampled and modified. However, it is still somewhat slow and can obscure details in terms of reactions and energy. In addition it necessitates a code capable of performing the sampling process in the manner required; several examples have been developed and demonstrated; NUDUNA (Areva), KIWI (LLNL), SANDY (SCK-CEN), SHARK-X (PSI), etc.
Linear propagation (sandwich formula) method The TENDL-2014 239 Pu evaluation file also contains reaction covariance data (C), representing the uncertainties and correlations between energies and reactions. These are processed using the NJOY2012  [12]  code into BOXER format files for use within NDaST. In this way, in conjuction with the sensitivity profile (S k ), the uncertaintes can be linearly propagated to k eff using the classical linear 'Sandwich' formula  [13] method (V ar k ¼ S k ⋅C⋅S T k ). It requires no random libraries and is therefore convenient and quick to apply. Its other benefit is a more complete physical understanding of the resulting data, which is retained completely on a reaction and energy basis. In practice, this method is widely used within the nuclear data and application communities À even for determination of bias estimation in criticality safety limits.
In generation of the perturbations solely as a function of E 0 therefore, one needs to weight the contributions from each of the incoming E. In this manner, compatibility with the sensitivity data units which are a function only of E 0 is ensured. An appropriate weighting for the case(s) under investigation is required, and in this work the 299-group JEZEBEL fission spectrum calculated with KENO/ ABBN-93, as taken from the DICE database was applied. Numerical verification work of the HMM was performed, but is not reported here. For example, tests of energy interpolation functions were made to ensure best accuracy is maintained; also tested was the difference between forming the requested multi-group pertubation bins, before or after the division operation, the latter being the preferred and adopted choice.
This was solved simply by deleting, and ignoring, the cross-reaction correlations. Furthermore, the x covariance distributions were observed to not comply with the 'zero-sum' rule. Therefore in each of the output files, re-normalisation of the PFNS distribution to unity was performed. Both of the above limitations are considered to be problematic artefacts of this particular nuclear data evaluation. These SANDY sampled files were also passed both through NDaST and used in direct calculation of PMF001 using the SERPENT code  [16]. Since the samples are formed from the covariances, this will be referred to as the 'backwards' HMM. This is as opposed to the samples produced from TALYS using input modelling parameter uncertainties in a 'forwards' sense.
Results and analysis The results panel of the NDaST software is shown in Figure  1 , where each random file is shown as a table row, and each reaction as a column. The value within the cell is the total Dk eff for that given combination; highlighting one or a number of cells shows the energy level detail as a series in the graph immediately below. In Figure  2 , the results of the Dk eff for each of the 988 random TENDL-2014 files are show, for both the direct method, using SERPENT as the Monte Carlo neutronics solver, and with the 'forwards' HMM method via NDaST. The bin widths are 200 pcm and vary from approximately À2000 pcm to +2500 pcm. The average difference between the two sets of values is only 13 pcm À on the same order as the statistical uncertainty in the Monte Carlo simulations.
Comparison with TALYS HMM Tables  2  and 3  show the comparison of results between the NDaST 'forwards' HMM and the sandwich method. Specific values are highlighted (in bold in Tab. 3) to show where the reaction totals are inconstent, i. e. , for inelastic, fission, nubar and Chi. One can see therefore that the HMM gives insight into why the totals of 808 and 929 pcm differ (Tab. 1). However, it is difficult, based on the limited knowledge of how the random files were generated with TALYS as to the reasons for this. These could be allotted to reasons such as methods of constraint, evaluator choice, software/processing effects or the limited ability of covariances to capture high order/non-Gaussian responses. The greatest inconsistency may be noted for the results in Chi À values of 93 and 470 pcm for HMM and sandwich method respectively.
As before, the greatest difference is apparent for Chi À the 87 pcm uncertainty obtained is much closer to the 'forwards' HMM value from the TALYS random files. In this instance, because of the identical origin of the covariance data, we can almost certainly attribute the difference to the re-normalisation applied by SANDY. It does this to each of the random files when the integrated Chi probability distribution does not sum to unity. NJOY is understood to also apply some correction to covariances which do not conform to the zero-sum rule; documentation states that for the condition in equations (  1 ) and (  2 ) is applied. However this work seems to indicate some discrepancy between the two approaches or a processing/software mistake. Confirmation of the magnitude of the re-normalised variances was again found by reconstruction as above, this is shown as a third series in Figure  4.
The effect is driven by the particularly high uncertainty above 2 Mev, coupled with JEZEBEL's sensitivity in this region (10% of fissions occur above 4 MeV and contribute 30% of the absolute x sensitivity total) À this is also apparent in the energy-wise series shown in Figure  1. One final consideration made, was as to which of the incoming energy (E) covariance tables is used by NJOY for processing by its ERRORR module. This is controlled in the input by stating the mean energy of fission (Ef mean parameter), which if not specified defaults to 2 MeV. NJOY selects only the covariance whose energy-span includes the Ef mean value. To investigate, we processed BOXER data at eight additional energies, from thermal to fast spectra as shown in Table  5. Also shown are the propagated errors in k eff obtained from using each of them.
As discussed, the TALYS tool is not the most accessible to users of nuclear data. However, the ability at least to do 'backwards' sampling and pass these to NDaST for HMM (alike to a BFMC method) would replace the need for covariance processing and any of the pitfalls involved. It is the intention in the near future, to make the SANDY tool used here freely available, possibly via the NEA Databank Computer Program Service. Close pairing of this to NDaST would enhance greatly the NEA services in nuclear data processing and application.
Stakeholder demographics To understand the current practices in data integration and to identify challenges and offer recommendations, several organizations that maintain nanomaterial data resources were surveyed. The goal was to identify, define, and provide a stimulus for initiating integration and exchange of data resources across nanomaterial data repositories and with other related, non-nanotechnology data resources. Survey questions included current and recommended functionality and web services enabling data integration as well as perceived challenges associated with integrating primary experimental data sets, or data sets curated from the literature, with other data resources. Appendix A contains a detailed summary of the stakeholder responses to the survey. Stakeholders who participated in the survey ranged from nanomaterial resources that have extensive experience in integrating data resources to those with limited data integration development by building up a nanosafety ontology and database and provide web modeling tools for use of these data.
Currently the eNanoMapper database at (https:// search. data. enanomapper. net) hosts data generated by several EU projects including publicly accessible NANoREG data. Tools for converting Excel templates into ISA-TAB/ISA-Nano JavaScript Object Notation (JSON) and semantic formats are provided. The application programming interface facilitates data usage by the modeling community. The Nanomaterial Registry is a publicly-available database of nanomaterial characterization and biological/environmental interaction data. Data in the Registry are curated from niche databases, literature, catalogs, and reports by trained scientists. Curation is based on a set of minimal information about nanomaterials  (Mills et al. , 2014). The data of the Registry are also available on the Portal at nanoHUB (https://nanohub.
In the context of nanomaterial data resource integration, needed metadata might include the results of physico-chemical characterization required to establish whether the nanomaterials are "the same" or "sufficiently similar" to be matched during data integration. However, the question of which physico-chemical properties need to match  (Stefaniak et al. , 2013) , not to mention complexities associated with different measurement techniques and experimental protocols, make uniquely identifying and matching nanomaterials a significant scientific challenge. Further discussion of metadata (including batch identifiers) that could support unique identification and matching of nanomaterial database records is provided in an earlier article in the NDCI series  (Marchese-Robinson et al. , 2016).
Data are conceptualized in different ways In conceptualizing data models, both definitions of individual data elements and the relationships between them can be created. As a result, data models created for different purposes or different database owners may have the same data elements but with different relationships between them. There has been a trend away from establishing a fixed relationship, such as a hierarchy, between database elements, a trend that, in some regards, adds to the data mining challenge. Sometimes knowledge of the relationships between attributes is built into the establishment of a hierarchy and that knowledge can be extracted when mining a database to ensure that data are appropriately aggregated when performing statistical analyses. Often times, databases are designed to support searching, but not specifically to support mining.
Lack of data completeness also poses a challenge to data integration because it is often difficult to obtain the necessary metadata to support comparison (a prerequisite for matching and data integration) between material records in different databases. For example, when obtaining information on physico-chemical characterization, it is valuable to have information on the chemical composition of the nanomaterials, such as the presence/absence of coatings, and if the nanomaterial has been transformed. Lack of complete metadata for associated biological tests may affect the clarity, and hence quality, of results  (Klimisch et al. , 1997)  and could preclude an assessment of whether two sets of results were generated under sufficiently similar conditions to allow them to be meaningfully integrated.
The lack of proper particle characterization is a key problem  (Krug, 2014) , and the consequence is that often a database contains more blank fields (no information) than actual data. This lack of high quality and complete data sets discourages integration. Though no system of characterizing quality or completeness has been broadly adopted, relative evaluation approaches have been proposed, including the NNI Data Readiness Level framework, as modeled from technology readiness assessment methods https://www. nano. gov//NKIPortal/ DRLs. A thorough discussion of the challenges associated with assessing the completeness and quality of nanomaterial data was presented in an earlier paper in the NDCI series  (Marchese Robinson et al. , 2016).
Protection of intellectual property hinders data sharing Although data sharing encourages the public to use and exploit knowledge contained in a database, restrictions may be in place to protect intellectual property rights and investments in generating and updating database content. Often, these restrictions have unclear statements regarding ownership, copyright, and licensing. Researchers are sometimes reluctant to share data until they are completely done analyzing and reporting their results out of fear that others will take their data and use it in a way that limits or reduces the novelty of their work  (Reichman et al. , 2011). Some have even suggested that those performing analysis on data they had no role in generating are "research parasites"  (Longo and Drazen, 2016). The need to maintain "unique selling points" of a data resource can impede data sharing.
Data annotations It is important that data are clearly annotated with statements such as possible provenance, including ownership and licensing or rights waiving where applicable. Understandably, data can be proprietary and, if so, should be clearly marked as proprietary. The use of resources, such as ZENODO (https://zenodo. org/) and FigShare (https://figshare. com/), which allow users to assign a specific license to their research data, is arguably indicative of a growing awareness of the importance of clarity regarding rights to data usage within the scientific community. These resources, however, do not support the application of automated data integration techniques  (Wilkinson et al. , 2016). In addition to annotations on data provenance, data annotations can also be provided to further clarify the quality of the data.
To support such data integration, ISA-TAB and ISA-TAB-Nano Application Programming Interfaces (APIs) that retrieve entities based on the ISA-TAB and ISA-TAB-Nano JSON schemas (https://github. com/ISA-tools) have been developed. (N. B. : previously ISA-TAB and ISATAB-Nano data sets have been represented using tab-delimited text fields  (Sansone et al. , 2008; Marchese-Robinson et al. , 2015). The nanosafety community has many interests and covers many different scientific domains. Of special interest at this time are web services that meet two essential needs: determination of the similarity between two nanomaterials and locating all data and information associated with one published paper or with a specific experimental protocol. Common web services envisaged by these stakeholders as being needed to support integration of nanomaterial data in the biomedical nanotechnology and nanosafety domains are presented in Table  1.
Data integration approaches A variety of approaches to data integration exist, with new ones continuing to be developed, supported by technologies ranging from manual integration (e. g. via an Excel spreadsheet) to a federated search architecture based on semantic web technologies (http://www. w3. org/ standards/semanticweb/)  (Cheung et al. , 2009; Eyres, 2013). It is beyond the scope of this paper to provide a comprehensive review of these approaches. Instead, approaches that best facilitate the retrieval of integrated data via automated queries (e. g. through data query languages such as SQL or SPARQL  (Hartig and Langegger, 2010) ) are discussed. Nonetheless, it is important to note that given the preference of many scientists to work in Excel, tools that allow for automated integration of manually prepared Excel data sets into queryable databases are of considerable value (e.
Data integration technology The extremes of the spectrum with regard to selecting an architecture that supports data integration through automatic querying are provided below  (Doan et al. , 2012). • Data warehousing -an approach that loads the content of different data resources into the same physical database. Subsequentlythe "warehouse" database can be queried, which involves querying all loaded data resources concurrently, with results presented to the user. • Federated querying -an approach that sends the same query todifferentdataresourcesattheiroriginallocationsandpresentsthe results to the user in a unified view after they are received. The data warehouse paradigm accomplishes the integration by transforming all the data resources into a physical schema (i. e. tables and relationships for relational databases, or XML schema, etc.
). The federated query approach relies on a "mediated schema" (i. e. , a virtual schema, embedded in the application), which does not store any data, but presents to the user a unified view of the domain across resources. The integration itself relies on how the different attributes of the mediated schema match the attributes of the resources, and if the grouping of the attributes corresponds to similar groupings of attributes in the data resources. This is known as "semantic mapping" and is the hardest task within the integration. The technology for accessing the data resources can be the same for both approaches. The data warehouse approach may use extract-transform-load (ETL) procedures, connecting to external data resources via web services and loading the results into the warehouse, while federated querying can use wrappers for accessing several distinct databases residing on the same machine and combine results only when presenting them to the user.
• How can/will entities be matched across data resources? • How will query results be integrated into coherent answers? Regardless of the integration approach, all methods require entity matching (linking associated information based on database content), or schema mapping (virtually altering the schema of one database so that its content can be queried with data from a database with a different schema), or both mapping techniques. Mapping is typically performed using transformation procedures, and there may not exist a simple one-to-one mapping between the final schema and the original data resources.
This is especially true in scientific and technological disciplines in which very complex concepts (entities) have been modeled differently by different groups at different times, a situation exacerbated by the evolution of new knowledge constantly being developed. A key requirement for an integration effort is having a network of schema mapping algorithms, based on individual data and metadata identifiers, that crosslink content from different data resources. In disciplines close to nanotechnology, efforts such as http:// identifiers. org/ (Juty et al. , 2012)  unify how these identifiers are represented, and other resources provide solutions for mapping identifiers from different databases  (van Iersel et al. , 2010; Chambers et al. , 2014; Wohlgemuth et al. , 2010). Identifiers, however, typically focus on entities studied, such as chemicals, materials, genes, and proteins.
5. 3. 1. Integrating small molecule chemical databases-As in nanoinformatics, the major challenge in integrating small molecule chemical data resources is the determination of the equivalency of the composition and structure of two small molecule entities. In the case of small molecule chemicals, the entities are the chemical structures, and the IUPAC International Chemical Identifier, InChI, (http://www. inchi-trust. org) (Heller et al. , 2015)  can be used as a uniform identifier across databases. When performing integration, the rule for entity matching is, "if the search results returned include one and the same Standard InChI, then the results are for the same compound". Several complexities must be considered when matching based on an InChI. For example, small molecule chemicals may be considered the same, yet still have different InChIs due to their rapidly interconverting structures.