dataesr/scientific-paragraphs-categorization huggingface dataset:
To follow the ageing of the TT sensitive materials after dismantling of the OPERA detector and until its use in JUNO, some modules remained equipped with the old OPERA electronics during their transportation to China and subsequent storage. These modules are used to register atmospheric muons traversing the detector while it remains in storage prior to it being mounted in JUNO. This made it possible to still register atmospheric muons with fewer modules. Although during the period of TT storage, the layout of the containers with the modules have been changed several times, the data collected during each period demonstrated stable average signal amplitude for passing through muons. It's worth noting that while the change in the layout of the containers can create a system- atic change in the amplitude of the signal for each period, as it was observed when the containers were moved in early 2019, the relative comparisons within each period would suffer from the same systematic shift and thus allow us to measure the relative performance deterioration.
The analysis of the data no additional deterioration of the performance the plastic scintillator compared to already mentioned ageing (Fig. 35 ). Finally, from their production, about 20 years ago, up to the beginning of the JUNO data taking, about 20% decrease in the number of p. e. is expected. Considering that 6 p. e. were observed at the middle of each strip (most disfavoured position) after their production, 4. 8 p. e. are expected at the beginning of the JUNO experiment. With 1/3 p. e. threshold, the muon detection efficiency remains at comparable level as the initial one. In case more p. e. are needed during the JUNO data taking, there is the possibility to introduce optical grease between the fibre opto-coupler and the MA-PMT glass, something which has not been done up to now because it was not needed.
For this utilisation, the TT electronics have been modified to cope with the high background induced by the rock radioactiv-ity. These electronics include for each sensor a Front End Board and a Readout Board. All 16 sensors of a TT wall are connected to a Concentrator Board whose role is to emit a first trigger level by performing x -y coincidences in the same TT wall. The Concentrator Boards communicate with a Global Trigger Board providing a three-layer TT trigger and with the JUNO data acquisition system. The trigger rate of the TT after the Global Trigger Board is expected to be around 2 kHz, pushed down to a few Hz by an offline muon track reconstruction. In order to validate the whole electronics chain and trigger strategy a prototype has been constructed using the same elements as the Top Tracker.
Acknowledgments We are grateful for the ongoing cooperation from the  China General Nuclear Power Group. We acknowledge the supported of the  Chinese Academy of Sciences , the  National Key R&D Program of China , the  CAS Center for Excellence in Particle Physics, Wuyi University , and the  Tsung-Dao Lee Institute of Shanghai Jiao Tong University in China , the  Institut National de Physique Nucléaire et de Physique de Particules   (IN2P3) in France , the  Istituto Nazionale   di Fisica Nucleare (INFN) in Italy , the  Italian-Chinese collaborative research program   MAECI-NSFC , the  Fond de la Recherche Scientifique (F. R. S-FNRS)  and  FWO  under the " Excellence of Science -EOS" in Belgium , the  Conselho Nacional de Desenvolvimento Científico e Tecnològico in Brazil , the  Agencia Nacional de Investigacion y Desarrollo in Chile , the  Charles University Research Centre  and the  Ministry of Education, Youth, and Sports in Czech Republic , the  Deutsche Forschungsgemeinschaft (DFG) , the  Helmholtz Association , and the  Cluster of Excellence PRISMA+ in Germany , the  Joint Institute of Nuclear Research (JINR)  and  Lomonosov Moscow State University in Russia , the  joint Russian Science Foundation (RSF)  and  National Natural Science Foundation of China (NSFC)   research program , the  MOST  and  MOE in Taiwan , the  Chulalongkorn University  and  Suranaree University of Technology in Thailand , and the  University of California at Irvine in USA.
This work, in particular, was supported by the initiative of excellence  IDEX-Unistra  ( ANR-10-IDEX-0002-02 ) and  EUR-QMat  ( ANR-17-EURE-0024 ) from the  French national program  "investment for the future", and by the  Strategic Priority Research Program   of the Chinese Academy of Sciences , Grant No. XDA10010300. We would like to thank all private companies which have collaborated with our institutes in developing and providing materials. We are particularly grateful to the  OPERA Collaboration  for the donation of all TT modules to the JUNO Collaboration. Finally, we would like to thank all technicians nonauthors of this paper who have helped us all these last years.
OPEN ACCESS EDITED BY Iris Salecker, INSERM U1024 Institut de biologie de l'Ecole Normale Supérieure, France 2021) and more sparsely in other nervous system and brain regions  (Davie et al. , 2018; Li et al. , 2022). In Drosophila, transgenic techniques  (Luan et al. , 2006; Pfeiffer et al. , 2010)  and libraries of transgenes  (Jenett et al. , 2012; Tirian and Dickson, 2017)  allow precise targeting of neuron types to manipulate and measure their activity, enabling the genetic dissection of circuit function. While this opens up unprecedented opportunities for understanding the circuit basis of behavior, it also poses new challenges: How can researchers know when they have data about one of the thousands of new cell types being identified and characterized in the literature and as part of large-scale analyses? How can they explore these massive new datasets, in conjunction with the literature, to generate hypotheses and form an integrated picture of the anatomical and molecular nature of circuits? How can researchers share their data in a way that conforms to FAIR standards  (Wilkinson et al.
, 2016)  and easily reuse the data of others? Virtual Fly Brain (VFB)  (Milyaev et al. , 2012)  provides a solution to these problems by integrating massive amounts of data derived from diverse techniques and multiple sources along with curated information from the literature. All this content is available via a web application and an API. The web application facilitates finding and integrating information about brain regions, neuron types and individual registered images, via sophisticated text search, point and click interaction with 3D images and graph visualizations and via semantic queries (e. g. , finding neurons by type and location). All data is integrated following FAIR principles and we provide tools to enable users to share and integrate their data on VFB following these principles.
VFB integrates images, connectomics and transcriptomics data by using two strategies -semantic and imagebased. Semantic integration is facilitated by the Drosophila Anatomy Ontology (DAO)  (Costa et al. , 2013) , curated from the literature, and increasingly from data-driven identification of cell types. The DAO serves both as a queryable store of knowledge about Drosophila neuroanatomy, cell types and their classifications, and a source of terms for annotating data across modalities including images, connectomics, transcriptomics and expression patterns. Standard parcellation schemes have been developed for all Drosophila central nervous system regions and stages including adult brain and ventral nerve cord  (Ito et al. , 2014; Court et al. , 2020). These parcellation schemes provide a standard reference for CNS regions defined in the DAO.
Following FAIR data standards, all data on VFB is accessible via an identifier, in the form of a URL, that is globally unique, persistent, and resolvable. Image-based integration makes use of standard templates (see Figure  1 ) onto which image data is registered (morphed), allowing hundreds of thousands of images from multiple imaging modalities to be co-registered so that they can be viewed and analyzed programmatically in a common coordinate space. Crossregistration has made it possible to design search algorithms to compare neurons, including NBLAST  (Costa et al. , 2016) , which provides a similarity score for any two cross-registered neuron tracings based on how similar their morphology and location are. These and other alignment-style queries are key to solving another problem-that of defining neuron types in ways that allow them to be identified from data using quantitative criteria rather than, as traditionally, using qualitative criteria and human judgment.
For example, the largest of these published to date  (Scheffer et al. , 2020)  is represented on VFB along with mappings to >1,100 known types and assigned a further 3864 provisional types based on NBLAST similarity-all cataloged and classified using DAO neuron type terms. VFB can therefore support cell type identification from data using NBLAST for a large and growing set of neuron types, assigning standard aoverlapping functionality such as NeuronBridge  (Clements et al. , 2022)  and neuprint  (Clements et al. , 2020) , are to support datadiscovery across many sources, to make first-pass exploration of complex data easy and to link that data to the literature. For further analysis, users can download data, or follow links from data on VFB to the same data on these other resources with their own data downloads, data exploration and search tools.
We believe that this initial data discovery and exploration step will become increasingly important as more and more single-cell transcriptomics and connectomics datasets become available. Consistent semantic annotation across diverse datasets on VFB is key to achieving this. Not only does this support matching of cell types across datasets, it also allows for sophisticated queries that group data in biologically relevant ways, for example by gross classification, location or connectivity of annotated neurons. advantage of FlyBase curation, including community curation efforts and text-mining pipelines  (Bunt et al. , 2012; Halperin et al. , 2012; Larkin et al. , 2021)  to easily identify and prioritize papers that contain data of high priority for VFB curation. Data curation standardizes the annotation of neuron types and transgenes in data using the same ontology and feature identifiers as FlyBase.
Ontologies and semantic schemas VFB is built around the Drosophila Anatomy Ontology (DAO)  (Costa et al. , 2013) , a manually curated, query-able classification of Drosophila anatomical structures and cell types expressed in Web Ontology Language (OWL)  (World Wide Web Consortium, 2012). DAO is built using community standards  (Jackson et al. , 2021)  and tooling  (Matentzoglu et al. , 2022)  for sustainable, scalable ontology development. Neuroanatomy is represented in DAO using a standard schema that supports recording neuronal location, connectivity, lineage and function and incorporates basic spatial reasoning  (Osumi-Sutherland et al. , 2012). We have extended this schema to incorporate relations for recording brain regions in which a neuron type has its major inputs and outputs, for example, that the synaptic input onto DA1 uniglomerular antennal lobe projection neurons is concentrated in the DA1 glomerulus  (Stocker et al.
This ontology and OWL schema, along with an OWL schema for representing image Templates and content. VFB has templates that integrate image data into common coordinate spaces and many more that serve as references for datasets in their native space The first four rows of the table provide details of the most up-to-date integrative templates on VFB: the JRC2018 unisex adult brain template  (Bogovic et al. , 2019)  has the largest number of aligned images, which include over 26,500 EM images from CATMAID FAFB  (Zheng et al. , 2018)  and the Janelia Hemibrain  (Scheffer et al. , 2020)  combined, over 70,000 images showing expression patterns or fragments of expression patterns from sources including FlyCircuit  (Chiang et al. , 2010; Shih et al. , 2015)  and Janelia FlyLight  (Meissner et al.
The DAO is also used to annotate transgene expression patterns and single cell RNAseq data in FlyBase-the latter as part of a collaboration between FlyBase and the EBI single cell expression atlas. We convert all of this into a standard OWL representation for import into VFB. The spatial reasoning built into the DAO allows us to provide a highly enriched set of results when users query for transgenes expressed in an anatomical structure-returning transgenes expressed in neurons that have some part in this structure or any of its substructures  (Milyaev et al. , 2012; Osumi-Sutherland et al. , 2012). While OWL has many advantages for standardization and querying, it cannot serve all VFB use cases. OWL is not designed for fast, tunable text search with autosuggestion. For this VFB uses an Apache SOLR document store.
It is also not ideally suited for automatically generating graph and tree visualizations or for maintaining and updating image annotations. The graph database Neo4J (Neo4j Graph Data Platform, 2020) is ideal for both of these use cases and provides a parallel system for graph pattern queries (e. g. for image metadata) that is simpler and more flexible than SPARQL  (McCarthy et al. , 2012) , the standard graph-pattern query language for OWL. We developed a standard translation between OWL and Neo4j, covering a limited subset of OWL, optimized for readability and queryability and supported by a Java Library  (Tan et al. , 2021). This allows us to maintain a curation database in Neo4J (VFB-KB) and a front-facing Neo4j server for generating trees (Figure  2C ), graphs (Figure  8 ) and graph queries for the VFB web-app and API.
Data integration pipeline and servers The VFB extract transform and load (ETL) pipeline extracts data from diverse sources (FlyBase  (Gramates et al. , 2022) , CATMAID  (Saalfeld et al. , 2009) , NeuronBridge  (Clements et al. , 2022) , NeuPrint  (Clements et al. , 2020) ) into ROBOT templates  (Jackson et al. , 2019)  specifying their transformation to OWL following our standard schemas. We then load the resulting OWL files into a triple store, along with the various ontologies used in data annotation (also in OWL) and an OWL version of our curation database (VFB-KB). The triple store integrates all of this content around a common set of persistent URLs that serve as identifiers for ontology terms, data instances etc, merging and deduplicating references to these entities. Downstream of the triplestore, a pipeline adds semantic tags using OWL and SPARQL queries, and loads the front-facing servers.
, 2020b). The NBLAST implementation in Navis  (Bates et al. , 2020a ) was used to generate a complete NBLAST matrix comparing all single neuron skeletons in VFB, including skeletons from the Janelia hemibrain  (Scheffer et al. , 2020) , FAFB  (Zheng et al. , 2018)  and FlyCircuit  (Chiang et al. , 2010)  datasets) with each other and with all split-GAL4 expression patterns in the VFB database registered to the JRC2018 adult unisex brain template  (Bogovic et al. , 2019). Most neuron types are present as bilaterally symmetric pairs. To match the same type on opposite sides of the brain, each neuron-to-neuron NBLAST was performed and then repeated with one of the neurons mirrored along the midline and only the highest of these two scores was retained. Split-GAL4 expression patterns almost always label the same neuron(s) on both sides of the brain, so we used NBLAST to compare a union of each neuron and its mirror image across the midline to each split-GAL4 expression pattern.
, 2022)  for neuron to neuron, neuron to Split-GAL4 and neuron to MultiColor FlpOut (MCFO) images of expression patterns. All scores are stored in the VFB Neo4j database. NBLAST scores are stored as a sparse matrix where scores below 0. 25 are removed and the remaining scores are limited to the top 20 for any given neuron or expression pattern. To test the efficacy of NBLAST and CDMIP similarity score queries in finding targeting split-GAL4 combinations for neuron types, we used associations between neuron types and split-Gal4 combinations curated from the literature and for which we have images (746 associations). We first found all individual neurons of each type for which a known targeting split Gal4 combination exists. For each type for which a known targeting split-Gal4 combination exists, we found the highest NBLAST or CDMIP score between individual neurons of this type and images of each Split-GAL4 combination in the database.
Visualizing circuits and part-trees A Neo4j query is used to automatically generate a browsable tree (template ROI browser, Figure  2C ) for each template, based on the painted domains associated with it. The circuit browser uses an implementation of Yen's k-Shortest Path algorithm in the Neo4j Graph Data Science package v2. 1 (https:// neo4j. com/docs/graph-data-science/current/algorithms/yens) to find the k shortest, most highly weighted paths between two selected neurons in a connectome, filtering out connections with a weight below a specified threshold. The weight is stored as a Neo4j edge property and corresponds to the number of synaptic connections in a given direction between two neurons, where one presynaptic density to one T-bar corresponds to a single connection. Yen's algorithm calculates the lowest weighted paths, so we need to invert the weights.
Organization of data Images on Virtual Fly Brain are cross registered to a growing set of standard 3D image templates covering all central nervous system regions and post-embryonic stages (Figure  1 ). Integrative templates for adult brain, adult ventral nerve cord and larval nervous system at instars 1 and 3, integrate cross-registered image data from many data sources. For example, our main adult brain template has almost 100,000 cross-registered images from 64 datasets, including connectomics data from electron and light microscopy images of neurons, lineage clones and expression patterns. These come from a mixture of small-lab datasets and large datasets imported via well-established pipelines from external databases including FlyCircuit  (Chiang et al. , 2010; Shih et al. , 2015) , Flylight  (Meissner et al.
, 2022) , CATMAID  (Saalfeld et al. , 2009)  (multiple instances hosted on VFB servers) and neuPrint  (Clements et al. , 2020). Where they provide useful reference, VFB also includes templates for single datasets in their native space, including dense connectomes, standard parcellation references and images depicting the relationship of the CNS to musculature. In summary, this cross referencing of data at the image level underpins a central feature of VFB to support the integration and comparison of many disparate datasets from across the Drosophila community. To support FAIR sharing, in particular, simple and consistent ways to access and reference data, every image on VFB is assigned a globally unique, persistent, resolvable identifier in the form of a URL. This is important not only for hosted data that has no associated identifiers, but also for the large numbers of images that are assigned local identifiers on other resources, as these local identifiers are not globally unique or easily resolvable without further information.
For example, the multiple CATMAID instances hosted by VFB have clashing neuron IDs, so these IDs are not sufficient to resolve data on CATMAID without additional information about which CATMAID instance the neuron ID came from. In contrast, the VFB-assigned URL resolves to the relevant, persistent page on VFB, from which data can be downloaded and can also be used to access data via our API. Both site and API provide mappings to IDs and links to data on external sites.
Data-driven search In addition to semantic search, VFB features neuron structure searches that find images depicting neurons with similar location and morphology to that depicted in an input image. These searches are driven by NBLAST similarity scores  (Bates et al. , 2020b) , precomputed by VFB, and color-depth Maximum Intensity Projection (color-depth MIP) scores  (Otsuna et al. , 2018) , provided by NeuronBridge  (Clements et al. , 2022). The Janelia hemibrain  (Scheffer et al. , 2020)  and FAFB  (Zheng et al. , 2018)  and the many studies that have traced neuronal circuits in these, provide de facto reference image datasets for identifying neuron types using NBLAST. Figure  5  shows an NBLAST search for potential types for an untyped neuron (Cha-F-600036) from the FlyCircuit dataset  (Chiang et al.
Exploring single cell transcriptomics One of the major strengths of VFB's semantic approach is the ease with which very different data types can be cross-integrated. VFB is working with FlyBase and the EBI single-cell atlas to annotate neuron types in single-cell transcriptomics data using the DAO. This allows queries for associated transcriptomics data from any cell type term or class in VFB. Figure  9A  shows the results of a query for transcriptomics data for olfactory receptor neurons. The results table returns clusters, the datasets they are from and their cell type annotations. Figure  9B  shows an example of summary expression data for one of these clusters, from the Fly Cell Atlas dataset  (Li et al. , 2022). For each gene these results show the level of expression, the proportion of expressing cells in the annotated cluster and semantic tags summarizing gene function, derived from Gene Ontology Molecular Function and Gene Group annotations in FlyBase (Figure  9B ).
They might be interested to find circuit paths between two neurons they believe to be targeted by two different split driver combinations that cause similar phenotypes when used to silence target neurons (Figure  8 ). In all cases, VFB supports rapid data discovery across datasets and provides a fast, accessible starting point for basic data exploration, while also supporting more advanced data exploration and analysis by providing data downloads and links to and identifiers for the same data in other tools and resources. Following FAIR data standards, all hosted data is downloadable under open licenses, with tracked provenance and rich metadata.
, 2022) , NeuronBridge  (Clements et al. , 2022) , neuPrint  (Clements et al. , 2020)  and CATMAID  (Saalfeld et al. , 2009) ), which, while limited to their own data, each provide distinct query tools and additional meta-data over that captured by VFB. In the case of CATMAID, VFB is also the sole host for official, public facing CATMAID servers for multiple connectomics datasets, providing a vital service to the community by archiving these data sets in their original form as they are released, as well as integrating them into VFB. VFB also provides a home for many datasets from independent labs that are not integrated by other resources and so would otherwise be inaccessible. This includes many independently generated Split-GAL4 datasets, registered image data for neuroblast lineage clones Frontiers in Physiology frontiersin.
org covering the adult brain, kindly contributed by the labs of Kei Ito and Tzumin Lee  (Ito et al. , 2013; Yu et al. , 2013)  and a dataset of 3D images of pharyngeal muscles, innervating motor neurons and split-GAL4 drivers  (McKellar et al. , 2020). We also link out to related resources including InsectBrain DB  (Heinze et al. , 2021) , which hosts 3D parcellation schemes and neuron images for brains of many insects and larvalbrain. org, which hosts reference information for larval anatomy and expression patterns. We currently do not link to Fruit Fly Brain Observatory/FlyBrainLab  (Lazar et al. , 2021) , which is focussed on facilitating simulation, because their dynamically generated content pages do not easily support linking.