dataesr/scientific-paragraphs-categorization huggingface dataset:
The results of automated data analysis pipeline and beamline metadata are displayed in ISPyB/ExiSAXS, an international collaboration between several synchrotrons and industry; the latest version uses an open source data reduction pipeline, FreeSAS, developed by the ESRF and other SAXS experts across Europe and the USA  (Kieffer et al. , 2022). The above approach has provided for a very successful, productive beamline providing a state-of-the-art resource for BioSAXS experiments with, to date, data from the beamline being cited in over 700 peer-reviewed publications (EPN-library, 2022). However, the beamline reopened in 2012 suffered from some limitations. The surface area of its in-air Pilatus 1M detector restricted both the accessible q-range and signal-to-noise ratios, while a relatively large beam size FWHM (100 mm Â 500 mm, H Â V) at the sample position was sub-optimal for experiments with small sample volumes.
Moreover, BM29's sample environment was rather inflexible and unsuited to experiments based on new technology (i. e. microfluidics) and to concurrent biophysical measurements (i. e. UV absorption measurements, etc. ), which could provide added value to SAXS measurements. The recent (December 2018 -August 2020) closure of the ESRF for its Extremely Brilliant Source (EBS) upgrade (ESRF, 2022a) was therefore leveraged to provide an opportunity, carried out in collaboration with EMBL Grenoble and EMBL Hamburg, to upgrade BM29. Commensurate with the magnetic lattice of the ESRF-EBS storage ring lattice, the beamline's bending magnet X-ray source was replaced by a two-pole wiggler, increasing the brilliance of BM29's X-rays by a factor of $ 60. This was coupled with the refurbishment of almost the entire beamline sample environment and renewal/refactoring of beamline control and data reduction and analysis software.
Software developments In order for the original BM29 to maintain a high level of automation while, at the same time, being accessible to nonexpert users, the beamline featured the BSXCuBE (BioSAXS Customized Beamline Environment) software for experimental control and display, in real time, of both two-dimensional scattering images and integrated one-dimensional scattering curves  (Pernot et al. , 2013). Further downstream processing was performed within the EDNA framework  (Incardona et al. , 2009)    experiments. For these reasons, in addition to upgrading the low-level beamline control macro language from SPEC  (Swislow, 1987 (Swislow, -2022   (Konarev et al. , 2006).
The BSXCuBE3 graphical user interface The layout and functionality of BSXCuBE3 (Fig. 6 ) were developed with a 'user first' principal in mind, with the aim of producing an unobtrusive, intuitive beamline control interface accessible to new users but powerful enough for more expert users and beamline staff to carry out more complex experiments. Simple colours and an ergonomic stepwise progression with an emphasis on limiting the button clicks required to prepare and start an experimental data collection add to the simplicity of its use. The MXCuBE3 framework on which BSXCuBE is broadly based consists of a back-end written in Python3 that is split into four parts: web server, control system integrator, common application logic and application-specific components. The reusable user interface components, many already existing in MXCuBE3, and a general-purpose backend further facilitated good developmental practices by providing patterns and abstractions for both back-end and front-end development.
This approach also allows faster development and easier maintenance. The front-end web client also shares the same key concepts as MXCuBE3, including beamline configuration and calibration, and incorporates an orthogonal view of the sample capillary. The logic behind the BSXCuBE3 user interface is achieved by the components being handled by the 'state management' library Redux (https://redux-toolkit. js. org/) and implemented with REACT (https://reactjs. org) and Bootstrap (https://reactbootstrap. github. io) using HTML5 and ECMAScript 9 (https://github. com/tc39/ecma262). These advances in architecture and the design of new features in BSXCuBE3 are intended to enhance the automation available on BioSAXS beamlines and facilitate the integration of new sample environments, such as microfluidics.
FreeSAS data analysis software An essential aspect of high-throughput BioSAXS experiments is feedback, in as close to real time as possible, regarding the quality of the scattering curves produced for any given sample. The automatic data processing and analysis pipelines available for experiments at BM29 provides this. Originally built using the EDNA framework  (Brennich et al. , 2016) , the BM29 data processing and analysis pipelines are now managed via Dahu (named after the mythical alpine goat), an online JSON-RPC data analysis server operated over Tango (https://www. silx. org/doc/dahu/dev/). Here, upstream data reduction (data scaling and azimuthal integration) is built around PyFAI (Python fast azimuthal integration)  (Ashiotis et al. , 2015; Kieffer et al. , 2020). The BioSAXS data analysis performed using the open source FreeSAS  (Kieffer et al.
, 2022;  https://www. silx. org/doc/freesas/dev/) includes curve averaging that accounts for radiation damage, background subtraction (matched buffer measured before and after each sample) and routine analysis on the resulting scattering curve carried out with CorMap  (Franke et al. , 2015) , autorg  (Hopkins et al. , 2017)  or Auto_GPA  (Putnam, 2016) , invariants (Porod volume, volume of correlation mass calculation;  Rambo & Tainer, 2013)  and BIFT (Bayesian inverse Fourier transform;  Vestergaard & Hansen, 2006). All data analysis results are saved in hierarchical data format (HDF) that can be visualized with silx view for both SC and SEC-SAXS modes, respectively (Fig. 7 ). Ab initio reconstructions are not yet a part of the automated pipeline and for this the plan is to implement the open source software DENSS (density from solution scattering;  Grant, 2018).
Results of the automated analysis are accessible in a modified ISPyB/ExiSAXS database for SAXS  (De Maria Antolinos et al. , 2015). This allows for crosschecks for consistency between different runs, providing 'on the fly' feedback regarding the need for repeat or extra measurements to complete the current experiment. Examples of the ISPyB/ ExiSAXS visualization for a batch SC and SEC-SAXS measurements are shown in Fig. 8.
Current status and future development After the ESRF-EBS upgrade, the BM29 BioSAXS beamline has been in full user operation since mid-September 2020 and has hosted more than 216 experimental sessions of which 122 were mail-in experiments during the recent health crisis. Access to the experimental facilities is offered through the ESRF MX BAG (Block Allocation Group) and Rolling access system (ESRF, 2022b). The latter, upon approval through ESRF's external beam time allocation panel (BTAP), allows data collection within six weeks after the experimental proposal submission. Refer to Table  1  for beamline schematics post-EBS upgrade. To promote the study of proteins and nucleic acids in solution with small-angle scattering (SAS) in Grenoble, France, a joint application process was implemented for both neutron small-angle scattering (SANS) and X-ray small-angle scattering experiments (SANSAXS BAG) in collaboration with the ILL, France  (ESRF, 2022c).
Acknowledgements We would like to thank  Peter van der Linden  from the Partnership for Soft Condensed Matter (PSCM) at the  ESRF. The authors would like to acknowledge members of the  ESRF-EMBL Joint Structural Biology Group (JSBG)  and all other  ESRF  support groups for all their help and also thank past and present ESRF directors ( Harald Reichert ,  Jean Susini  and  Annalisa Pastore ) and  EMBL-Grenoble management (Stephen Cusack)  for their support of this project.
5 times the interquartile range above and below the boxes. e XCI strongly predicts identity within a quadruplet (one-sided empirical p value = 5. 9e-5). Observed predictability score for sibling identity using X chromosome genes (purple vertical line, average across the 3 female quads and the 3 time points) against the theoretical distribution for random predictions (median score = 1, dashed line). f Plot of mean predictability scores (y-axis) versus gene set size (x-axis) for random gene sets and the X chromosome genes (highlighted with purple lines, one-sided empirical p value < 0. 001). Source data are provided as a Source Data file. following a binomial model, see Methods), which we refer to as ASE-SNPs. To measure our capacity to recover ASE-SNPs, we ordered SNPs by deviation from the null expectation that allelic ratios are identical across individuals within a quad (statistical significance from Chi-Squared test, see Methods), then measured the predictive power using the area under the Receiver-Operator Characteristic curve (AUROC).
Armadillo personal genome generation We used g2gtools (unpublished, https://github. com/churchill-lab/ g2gtools v0. 2. 0) to generate a personal quadruplet genome for each quadruplet set. We first created VCI files of the SNPs and INDELs using the g2gtools vcf2vci with the -pass and -quality tags. This is an indexed version of the VCF file required by g2gtools. Homozygous (alternate) SNPs and INDELs that passed quality control were kept. SNPs were incorporated into reference genome FASTA file using the g2gtools patch command. INDELs were then incorporated into the patched genome with the g3gtools transform command. A chain file was generated using the g2gtools vcf2chain command. We updated the genome annotation file (liftover) using the new genome with the g2gtools convert command. As the genome of the armadillo is not assembled beyond a large number of scaffolds, the patches and transformations were done per scaffold.
Building functional annotation sets for the armadillo Currently, no gene functional annotations exist for the armadillo. We used the gene annotations from Ensembl 52 to generate a gene ID map between human and armadillo homologs. From the total of 33,374 coding and non-coding genes and transcripts annotated for the armadillo, there are 13,492 human homologs. In close parallel to the GO 60 annotation project's own process, we built an armadillo ontology using human gene-GO annotations  61. Within our current mapping, on average, each armadillo gene belongs to ~85 GO groups, and each GO group has on average ~56 genes.
Acknowledgements We thank  Tony Zador ,  Paul Pavlidis ,  Peter Koo  and  Dan Levy  for helpful discussion. We thank  Amanda Withnell  for animal care. This research was supported by the  National Institutes of Health   R01LM012736  and  R01MH113005  (J. G. ) and by the  National Institute of Allergy and Infectious Diseases Interagency Agreement   IAA 15006-004  (M. T. P. and L. B. A. ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institutes of Health. Armadillo blood for the study was obtained from the  National Hansen's Disease Program.
We are witnessing an acceleration of the global drive to converge consumption and production patterns towards a more circular and sustainable approach to the food system. To address the challenge of reconnecting agriculture, environment, food and health, collections of large datasets must be exploited. However, building high-capacity data-sharing networks means unlocking the information silos that are caused by a multiplicity of local data dictionaries. To solve the data harmonization problem, we proposed an ontology on food, feed, bioproducts, and biowastes engineering for data integration in a circular bioeconomy and nexusoriented approach. This ontology is based on a core model representing a generic process, the Process and Observation Ontology (PO2), which has been specialized to provide the vocabulary necessary to describe any biomass transformation process and to characterize the food, bioproducts, and wastes derived from these processes.
In addition, the need to develop more integrative approaches to reconnect agriculture, environment, food, and health has been reaffirmed recently  2. This nexus approach combines the issue of the sustainability of diets with that of the sustainability of agricultural and food production systems, leading to sustainable agri-food systems. To address such complex questions, the collection of multi-scale characterization data and the extension of analysis methods are major challenges. Data are collected in various projects and are by nature heterogeneous and multidimensional as they cover nutritional, sensory, physicochemical, rheological, microbiological, environmental, and socio-economic aspects. There is thus a need for standardization of these multidimensional and multisource data to facilitate their discoverability and reusability, to assemble them into new datasets allowing new statistical analyses or metaanalyses.
This ontology addresses animal and plant food sources, food categories and products, and other descriptive facets coming from LanguaL, a mature and popular food indexing thesaurus (http://langual. org). LanguaL has been used to index numerous food composition databases, including the U. S. Department of Agriculture (USDA) Nutrient Database for Standard Reference (SR) and the European Food Information Resource (EuroFIR) Network of Excellence  8. However, in 2015, the European Food Safety Agency (EFSA) developed a standardized food classification and description system called FoodEx2 revision 2 (https:// www. efsa. europa. eu/en/data/data-standardization). The system is built upon European legislations for pesticide residues, chemical contaminants, food additives, biological monitoring data of zoonoses and zoonotic agents, and microbiological criteria for foodstuffs.
FoodEx2 consists of descriptions of a large number of individual food items aggregated into food groups and broader food categories in a hierarchical parent-child relationship called the "master hierarchy". Moreover, the FoodEx2's approach is wellsuited to our strategy of following a process of transformation, as the classification is based on the identification of the most relevant treatments to create new natures of products from the raw materials and on the creation of specific food groups for the derivatives obtained with these treatments. In this paper, we present PO2/TransformON, an ontology on food, feed, bioproducts and biowastes engineering for data integration in a circular bioeconomy and nexus-oriented approach. The objective was to build the ontology by reengineering non-ontological resources such as FoodEx2 and by reusing existing ontologies to ensure backward compatibility with existing datasets  [9] [10] [11] [12] [13] [14] [15] [16]  , and the integration of other bio-based and food systems information thanks to an ontology network.
Data stewardship was our main driver. This new ontology is a specialization of a core model, Process and Observation Ontology (PO2)  17, 18. The PO2 core model was designed to represent a generic process described by a set of steps and experimental observations available for the input and output components of each step of a process. This generic model is already well adapted to transformation or characterization processes but new needs have been identified to cover the targeted domain and answer new questions. Considering that FoodEx2 is an authoritative reference complementary to the Standard Sample Description (SSD2) at the European level and is used by national agencies in European countries for database annotation and data exchange, we decided to base the construction of PO2/TransformON on FoodEx2 for the food and feed hierarchies.
In addition, we built a hierarchy for nonfood products to cover the newly defined domain. To this end, the legacy vocabulary of FoodEx2 and other valuable nonsemantic resources were transformed into linkable data thanks to the fairification process (https://www. go-fair. org/fairprinciples/fairification-process/) and the PO2 core model. In the Results section, we present a new version of the PO2 core model and the domain ontology TransformON. In the Discussion section, we illustrate the relevance of this new ontology to get out of information silos through four concrete use cases in the fields of process engineering, bio-based composite making, food design, and consumer preferences.
The Unified Code for Units of Measure (UCUM, https:// ucum. org/) is used for unambiguously representing measurement units. Finally, data are typed according to Schema. org vocabulary (Schema. org, https://schema. org/), and metadata from the Data Catalog vocabulary (DCAT, https://www. w3. org/TR/vocab-dcat-2/) are associated with the dataset corresponding to the process (in yellow in Fig. 1 ). Datasets are stored in an RDF graph database (also known as a triple store) named PO2 BaGaTel. The new features of PO2 V2. 3 are the addition of two SOSA/ SSN concepts (sosa:Platform and sosa:System) to be able to list the equipment of a facility or experimental platform and some additional relations (PO2:hasObjectOfInterest, PO2:has Produc-tOfInterest, PO2:hasReplicate) to better specify the products of interest in a process or to reduce the ambiguity on the objects that are observed, as well as experimental replications.
Thanks to the introduction of PO2:hasObjectOfInterest, PO2 core model is compliant with the InteroperAble Descriptions of Observable Property Terminology (I-ADOPT, https://www. rdalliance. org/groups/interoperable-descriptions-observableproperty-terminology-wg-i-adopt-wg), a general framework for representing the variables derived from observations. Besides, DCAT metadata were added to document the dataset annotated with the ontology. Seven classes of the core model may be specialized for a specific domain, namely PO2:Process, PO2:Step, PO2:Component, PO2:Scale, PO2:Material (sosa:System), PO2:Method (sosa:Procedure), PO2:Attribute (ssn:Property).
PO2/TransformON domain ontology The domain ontology PO2/TransformON aims to cover all the aspects related to food, feed bioproducts, and biowastes processing, as well as the characterization of the functional properties of the raw commodities, primary derivatives, end products, and wastes or residues from the treated biomass (Fig. 2 ). Figure  3  shows the top levels of the PO2/TransformON domain ontology together with the UCUM module allowing the standardization of units of measure.
It includes 4234 classes directly imported from FoodEx2. The "Food" branch is divided into five main subclasses with respect to the source of raw commodities (animal or plant origins) and the degree of transformation (raw, derivative, composite food, and isolated purified ingredients). The food components from the datasets milk microfiltration (DS1) and dairy gels (DS4) were added to this hierarchy. Feed classes. The "Feed" branch includes 759 classes imported from FoodEx2. However, two main subclasses were created: one for grouping all the primary sources of feed and the other one for grouping the compound feed. Figure  5  shows the non-food substances, biochemical constituents, and water class hierarchies, with a focus on recyclable waste. Non-food substance classes. The "Non-food substance" branch was created in PO2/TransformON to group all the chemical substances used or produced during transformation processes, taking into account the role or nature of the substances, namely whether they are organic or inorganic: chemical reagents, cleaning products, energy resources, matter, packaging material, packaging gas, refrigerant fluids, polluting emissions, and recyclable wastes.
We then defined a "recyclable waste" class and further divided it into "recyclable organic waste or residue" and "recyclable inorganic or mixed waste" to include packaging wastes. Lastly, following the same logic as for the "Food" and "Feed" branches of FoodEx2, namely the degree of transformation, we created classes of "primary organic waste" (animal or plant tissue waste or residues from agricultural production), "secondary organic waste" (residues or by-products from transformation processes), and "residual biowaste" (liquid or solid organic final wastes, including sludges and liquid wastes from waste treatment). The concepts used in the biorefinery and transmat datasets (DS2 and DS3) were also integrated into this hierarchy. The "non-food substances" hierarchy includes 270 classes which can be further specialized into additional subclasses for other types of by-products and waste encountered in future use cases.
Concepts that refer to chemical substances will be aligned with CheBI (https://www. ebi. ac. uk/chebi/init. do) or PubChem database (https://pubchem. ncbi. nlm. nih. gov/). Biochemical constituents and water classes. Finally, the "Substance" hierarchy includes two other subclasses for biochemical constituents and different forms of water. The "biochemical constituents" subclass groups all the components that are part of food or feed products (i. e. , nutritional compounds or dietary compounds). The "water (generic)" subclass allows us to group the different types of water that can be encountered in different physical states or forms such as drinking water, process water, purified water, or water found as a food constituent. The concept "water (as food constituent)" belongs to both subclasses of generic water and biochemical constituents in a parent-child relationship thanks to polyhierarchy.
Overview of PO2/TransformON building and datasets stewardship To conclude this section, it should be emphasized that the PO2 core model and the TransformON domain ontology vocabulary make it possible to structure and annotate the data collected both on the input/output products obtained from biomass and associated processes and steps. This structuring provides specific metadata that makes querying the data more efficient, which is a valuable asset for answering complex questions and breaking out information silos. Figure  8  illustrates the general workflow and the ecosystem of tools for ontology building and data stewardship (see the Method section for more information).
Evaluation of the reusability and relevance of the domain ontology PO2/TransformON The objective of this work was to build a new domain ontology by re-engineering non-ontological resources such as FoodEx2 and by reusing existing ontologies to ensure backward compatibility with existing datasets. Beyond the FAIR principles (Findability, Accessibility, Interoperability, and Reusability), the evaluation of the ontology should also take into account its relevance to address specific use cases and applications. In this part, we will show the relevance of this new domain ontology PO2/TransformON through several concrete use cases in the fields of process engineering (use case 1), bio-based composite making (use case 2), food ecodesign (use case 3) and relations with consumer's perception and preference (use case 4).
We chose these use cases to illustrate each of the sub-domains of ontology in a circular bioeconomy and nexus-oriented approach. The use cases have been previously published in data papers or articles  [9] [10] [11] [12] [13] [14] [15] [16] 23, 24. The fourth use case is still in progress, but it provides a good example of how heterogeneous sources can be integrated and made interoperable thanks to the new domain ontology, which combines the vocabulary previously used in the other use cases. Use Case1: Predicting the performance of the milk microfiltration process Milk microfiltration is becoming more and more attractive in the dairy sector. Cross-flow microfiltration with a 0. 1 µm membrane is widely used to separate native casein micelles (~150 nm) from whey proteins (~2-15 nm) in skimmed milk.