dataesr/scientific-paragraphs-categorization huggingface dataset:
These questions explored community opinions, including the current utility of Tripal, main challenges for developing and curating data, future technology priorities and more. The surveys and their results represent 21 databases and 26 individuals (Supplementary Files 2 and 3, see Supplementary Data available online at http://bib. oxfordjournal. org/). Aggregated across the databases responding to the survey, over 168 000 unique users interacted with Tripal sites in the past year. These databases employee 21. 7 personnel as Tripal developers and 19. 25 personnel as biocurators (fractional positions may reflect full-time positions split across different projects or students with part-time appointments). The Tripal survey results highlight the international scope of funding resources, as well as the uneven split across specific funding sources, with government agencies and programs providing the majority of support compared with funding from industry and agricultural commodity boards.
Of the 12 databases reporting financial support of development directly related to or building on Tripal, a total investment of over US $123 000 000 was reported, with the largest funding from the United States Department of Agriculture (USDA), the United States National Science Foundation (NSF) and Genome Canada. Other funding was obtained from Cotton Incorporated, Government of Saskatchewan, Saskatchewan Pulse Growers, United States National Pork Board, University of Saskatchewan and Western.
The Tripal infrastructure provides a strong foundation for the adoption of both FAIR and TRUST by supporting and encouraging open-source software, standards-based data storage and community-driven development. Tripal version 3 expanded this support with a focus on web services, backend platform flexibility and reliance on ontological frameworks. The communities that implement Tripal databases have also responded with FAIR-minded extension modules that are intended to collect and curate data for improved distribution and integration. For example, Tripal Galaxy  [43]  is connecting data to analysis tools, thus paving the way for reproducible scientific workflows and more data reuse. Finally, Tripal's recent adoption of governance committees supports many of the principles outlined by TRUST. Tripal is an example of user-driven and transparent development where community contributions are evaluated and adopted frequently.
Today, Tripal is both a platform for independent databases as well as an integrated solution for cross-database communication and analysis. The second anonymous Tripal survey asked administrators and developers to prioritize architectural considerations (Figure  8 ) and reflect on current challenges (Figure  9 ). The survey and the organization of the Tripal committees represents the Transparency aspect of TRUST. We relate the resulting objectives and challenges, as well as development objectives for Tripal v4, back to the remaining TRUST guidelines.
(T)technology: support for multiple database schemas and engines One of the ways Tripal strives to help biological data portals reach technological TRUST-worthiness is through flexible data storage support. Tripal core provides default integration with the GMOD Chado database schema that supports many biological data types  [13]. Such integration allows Tripal to support many data types out of the box and the implementation of a common schema allows standards-based development of Tripal data loaders. The highly normalized flexibility of Chado makes determining data storage best practices challenging for complex, interdependent data types (Figure  9 ). For example, quantitative trait loci (QTL) data associates phenotypic traits with genetic maps, which are both complex data types in their own right.
The diverse Tripal community aids in development of robust, inclusive standards; however, finding common ground among diverse needs is still challenging. The Tripal community encourages groups to develop data importers for proposed data standards to facilitate evaluation and adoption by other biological data portals. There are currently a number of QTL-focused extension modules, each implementing different standards for community consideration. This approach ensures that community needs can be met quickly, while ideal data standards are being developed and evaluated. In addition, some data are not well suited for relational models. For example, metabolic pathways are more intuitively stored in graph-based databases. These data highlight the need for support for additional data backends that provide flexibility beyond Chado's highly normalized and relational structure.
Tripal 3 supports additional data backends to allow storage flexibility; however, it remains dependent on Chado for storage of controlled vocabularies for its ontology-driven design. Since the Tripal community has indicated that this is a priority (Figure  8 ), Tripal 4 will retain the GMOD Chado default integration but also be completely database agnostic to allow maximum flexibility. This ensures that biological data portals using Tripal are able to use appropriate data storage mediums based on their data and associated standards.
(S)sustainability: backwards compatibility versus changing technologies Quickly evolving web technologies create a dilemma: it is critical to support existing Tripal sites since new biological web portal funding for upgrades is not guaranteed, but it is often necessary to break backwards compatibility to leverage new technologies. Tripal has always guaranteed backwards compatibility of all Tripal APIs, even with the substantial changes between Drupal 7 and 8 that required a major rewrite of Tripal Core and many extension modules. Insulating the Tripal community from the difficulties associated with major Drupal upgrades was successful, with most developers reporting that the upgrade was not a major challenge (Figure  9 ); however, this was an immense task for the Tripal development team and not sustainable for future versions.
Fortunately, the Drupal software platform is maturing and reaching a new level of stability. Releases from Drupal 8 onward will follow a rolling deprecation scheme with a guarantee of backwards compatibility for any new functionality for at least two major versions. This allows progress in adopting new technologies but is implemented as smaller changes over a greater time period to ease developer workload. Tripal will now mirror the Drupal rolling deprecation philosophy to allow gradual adoption of new technologies. Tripal will be fully upgraded to Drupal 9 (in Tripal 4, Figure  1 ) to provide site administrators and biocurators with a familiar administrative interface. Once this new version becomes available, the community will begin exploring new technologies prioritized by the community, such as microservices, additional data storage backends and tighter integration with other programming languages (Figure  8 ).
Support for custom data is provided by a number of extension modules (https://tripal. readthedocs. io/en/latest/e xtensions/data_input. html) that can be used by biocurators to bulk import interrelated data into a Tripal site. This streamlined process goes beyond just importing the file, as it also focuses on collecting important metadata (i. e. species, cultivar), including analysis details such as analysis program and version. Data need to be curated in growing in volume and complexity. This process can be streamlined by encouraging community contribution, where data generators perform the initial steps of submitting the data and providing relevant metadata. To ensure high quality and accuracy, submissions still need to be reviewed by professional curators. This process is facilitated by the Tripal HeadQuarters extension module (https://github.
com/ statonlab/tripal_hq), which allows registered users to submit their own data and metadata. Designated data curators have an administrative interface to view and accept or request changes to this triaged data. Data curation can also be more efficient with semi-automated (or machine-driven) curation. Examples of this are emerging in Tripal extension modules, including the Tripal Plant PopGen Submit Pipeline  [5]. In this module, metadata standards such as the Minimum Information About a Plant Phenotyping Experiment and the associated ontological frameworks are assigned through the use of natural language processing-based approaches for the many of the sequence and phenotype objects. These tools and approaches enable Tripal sites to provide high-quality data to their respective communities as efficiently as possible.
(S)sustainability: funding for development of core and member data portals Currently, Tripal is largely supported by member databases through a combination of federal grant dollars and industry/commodity funds. These funds support the database resources themselves, which in turn dedicate some developer time to Tripal as a support infrastructure. This model has proved challenging for funding the extensive effort needed for major refactoring and Core update efforts; current funding sources generally only support new infrastructure and innovation. As long-term software sustainability requires dedicated effort in maintenance and security updates, we foresee funding will remain a challenge for the Tripal community. This model has additional major drawbacks for personnel. 'Soft' (i. e. temporary grant) funded that positions are by their very nature temporary, leading to developers coming and going in relatively short time frames (2-3 years).
While this cycle of grant funding supports a training pipeline for students and temporary staff, it precludes true expertise from being developed and maintained in a system as complex as Tripal. Drupal is a substantially complex system with developers often requiring a long training period prior to productive code contribution. This is compounded by the need for the developer to understand biological entities. Tripal would benefit from full-time, dedicated developer positions that would not only maintain Core code but provide training and support to new developers. In addition to maintaining Core, Tripal must support new data types and research methods to meet current research needs. The quantity and complexity of data will increase, as will the number of research communities needing online resources.
Tripal can help through collaborative development and shared modules. However, these ambitious development efforts will need reliable funding, whether dedicated to a single resource or shared by dozens of resources. The Tripal community has engaged in extensive discussions to develop recommendations for ongoing support. We recommend that granting agencies that fund member databases through short-term grants and/or long-term contracts/personnel consider funding long-term support personnel specifically for community database infrastructure. This would enable a more efficient, cost-effective, sustainable, shared and FAIR/TRUST-focused infrastructure to benefit all biological data portals and software systems, including Tripal. This support could also facilitate the migration of the many databases using different code bases to Tripal and other shared platforms.
Future directions The Core architectural priority has always been to both ensure that Tripal is compatible with the most recent stable Drupal major version and that it leverages the new functionality provided by Drupal. With Tripal 4, the main goal is to become compatible with current Drupal release  [9]. With the new release cycle, Tripal 4 can evolve into a system supporting the new functionality provided by Drupal 9 without requiring large rewrites of extension modules or extensive site upgrades. This current approach has been informed and approved by the community, which agrees that Drupal should continue to be the platform of choice (Figure  8 ). Integration of additional storage backends (e. g. MongoDB, variant call format) has been a high priority since the initial development of Tripal 3 and as informed by the community (Figure  8 ), is one of the driving design principles behind Tripal 4.
Core Tripal developers are also focused on easing the upgrade of both Tripal sites and Tripal extension modules during development of a new major version. Survey results also indicated a need for Core support of phenotypic breeding data, genome synteny and genetic data (Supplementary File 1, see Supplementary Data available online at http://bib. oxfordjournal. org/). With Tripal 3, Core data types were added, and with Tripal 4, additional support in the form of Core fields and data storage standards is planned. The PMC and TSC will continue to consult with the community through monthly user meetings and our Github issue queue to ensure emerging needs are addressed. Many Tripal databases participate in the AgBioData consortium, a working group of representatives from biological databases and other software resources that meet regularly and facilitate cross-resource working groups to ensure standards and best practices for biological knowledge  [17].
Conclusions This review covers the current scope and impact of Tripal, as well as plans to sustain and expand its usefulness in supporting many biological data portals. Tripal provides tools for data storage, dissemination and discovery of genetic, genomic and breeding data. Research communities can leverage Tripal to build their own data web portals focusing on their specific needs while benefiting from the collaborative nature of Tripal and its community-derived extensions. Both Drupal and Tripal provide extensive APIs for portal customization and tool integration as highlighted in the Tripal community-driven functionality section. Furthermore, Tripal provides extensive documentation and training materials paired with a welcoming developer community and transparent governance structure. Tripal currently underpins between 27 and 130 community databases, provides service to 168 000 unique users, employs at least 21.
7 personnel and brings in over US$123 000 000 to member databases. The Tripal framework underpins a variety of successful community focused data web portals. Tripal encourages its member databases to follow the guiding TRUST principles to develop trustworthy resources for their communities by leading through example. Development of Tripal focuses on (T)ransparency of governance, (R)esponsibility to adopt community standards, (U)ser focus on understanding and serving community needs, (S)ustainability of both Tripal and member databases, and regular (T)echnology upgrades. Tripal also provides tools for member databases to ensure data they house which is FAIR as discussed briefly here and more extensively in  [12]. Tripal is currently funded entirely through its member databases. Given the importance of integrated big data and the growing database community, Tripal infrastructure would benefit from sustainable, reliable funding for Core efforts and coordination of this funding across international borders.
Key Points • Tripal is an open source, community developed software platform to build online, open access databases for biological knowledge, spanning genetic, genomic, trait and/or breeding data. • At its 11 year anniversary, Tripal serves as the base for over 30 public databases and has developed a unique, shared, inclusive governance structure to manage code development, communication and user support. • Through use cases and community surveys, we show the collaborative development, innovative functionality and the flexibility inherent in Tripal as well as its ongoing support for FAIR (Findable, Accessible, Interoperable and Reusable) data.
For the  US-Swine Pathogen Database , this work was supported by the  US Department of Agriculture (USDA) Agricultural Research Service  ( ARS  project number  5030-32000-118-00-D) ;  USDA ARS  project numbers  5030-32000-108-21I  and  5030-32000-108-37I  funded by  Animal and Plant Health Inspection Agency; USDA Agricultural Research Service Research Participation Program of the Oak Ridge Institute for Science and Education (ORISE)  through an interagency agreement between the  US Department of Energy (DOE)  and  USDA Agricultural Research Service  (contract number  DE-AC05-06OR23100  to B. I. );  SCINet  project of the  USDA Agricultural Research Service  ( ARS  project number  0500-00093-001-00-D );  National Pork Board  (NPB project number. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
He is working on the Tripal Project as a web developer. He is a member of Tripal's  Project Management Committee (PMC). He is currently in a cooperative agreement with the  USDA  through their i5k project, which utilizes Tripal. He also collaborates with the  University of Connecticut ,  United States , on the TreeGenes database, also a Tripal site. Valentin Guignon  is a Bioinformatics Specialist working at the  Montpellier office (France) of the Alliance of Bioversity International  and  CIAT  (CGIAR) since 2010. He received his M. Sc. degree in  Bioinformatics from the University of Montreal (Canada ) in 2006. He worked in the  French National Center for Scientific Research (CNRSdiseases. Dorrie Main  is a Professor at  Washington State University. Monica Poelchau is a Geneticist at the  USDA-Agricultural Research Service's National Agricultural Library.
cirad. fr/) [19] Cacao Genome Database (http://www. cacaogenomedb. org/) CGD (http://www. citrusgenomedb. org/) CorkOakDB (http://corkoakdb. org/) CottonGen (http://www. cottongen. org/) [4] Cucurbit Genomics (http://cucurbitgenomics. org/) [20] GeneNet Engine (http://gene-networks. org/) [21] GDR (https://www. rosaceae. org/) [22] GDV (http://ww. vaccinium. org/) GrainGenomes (development version) Grass Genome Hub (https://grass-genome-hub. southgreen. fr/) Hardwood Genomics Project (http://www. hardwoodgenomics. org/) I5K Workspace (https://i5k. nal. usda. gov/) [23] Kiwifruit Genome Database (http://kiwifruitgenome. org/) [24] KnowPulse (http://knowpulse. usask. ca) [25] Legume Information System (http://legumeinfo. org/) [26] LiceBase (https://licebase. org/) Mimubase (http://mimubase.
org/) Musa Germplasm Information System (https://www. crop-diversity. org/mgis/) [27] NanDeSyn Database (http://nandesyn. single-cell. cn/) [28] PeanutBase (http://peanutbase. org/) [29] Planarian Educational Resource (https://cuttingclass. stowers. org) [30] Planosphere (https://planosphere. stowers. org/) [31] PCD (https://www. pulsedb. org/) Rice Genome Hub (https://rice-genome-hub. southgreen. fr/) RNAStructurome (https://structurome. bb. iastate. edu/) [32] SeriolaDB (https://www. serioladb. org/) SIMRBase (https://simrbase. stowers. org) [33] SpinachBase (http://spinachbase. org/) [34] TreeGenes (https://treegenesdb. org/) [5] US-SPD (https://swinepathogendb. org/) Zeamap (http://www. zeamap. com/) [35].
PCR products were detected using capillary gel electrophoresis (Fragment Analyzer, Agilent, France). Double strand sequencing of PCR products was performed by Eurofins (Germany). Sequence polymorphisms were identified with the CLC Main Workbench 20 software (Qiagen) by using the 3D7 strain of P. falciparum (PF3D7_1343700) as a reference sequence. Electropherograms with mixed alleles were considered as mutant for the purpose of mutation frequency estimation. The quality control was assessed by including three blinded quality-control samples (wild type, C580Y and R539T) in each 96-well sequencing plate.
Data analysis Data were double entered, cleaned, and analysed using the software programme WHO excel (http:// www. who. int/ malar ia/ publi catio ns/ atoz/ 97892 41597 531/ en/). Enrolled patients who were lost to follow-up or withdrawn from the study, had recurrent parasitaemia with new infection, or unknown PCR (indeterminate or missing) were excluded from per-protocol analysis. However, these cases were included in the Kaplan-Meier analysis up to the day of loss or withdrawal from the study. Descriptive statistics including percentages, mean, standard deviation, and range were used. Student's t-test was used for analysis of continuous variables (parasite density and age) and Fisher's exact test was used for categorical data. A p-value of < 0. 05 was considered significant.
Funding Open access funding provided by University of Gothenburg. Funding was obtained from the Bill and Melinda Gates Foundation through WHO (grant no. OPP1140599). This work was also supported by the French Government (Agence Nationale de la Recherche) Investissement d' Avenir programme, Laboratoire d'Exceellence (LabEx) "Frech Parasitology Alliance For Health Care" (ANR-11-LABX-0024-PARAFRAP).
Corresponding determination coefficients 0. 46 and 0. 54 are inferior to the optimized quantum model. Top right panel of Fig. 3  shows ranking of the probe documents by Google search engine in response to query website promotion, used as estimator of semantic relation between the query words (Methods). The obtained determination coefficient R 2 = 0. 79 is slightly inferior to that demonstrated by the optimized concurrence measure. Similar results are obtained for Russian language.
Although synthetic training data has been shown to be beneficial for tasks such as human pose estimation, its use for RGB human action recognition is relatively unexplored. Our goal in this work is to answer the question whether synthetic humans can improve the performance of human action recognition, with a particular focus on generalization to unseen viewpoints. We make use of the recent advances in monocular 3D human body reconstruction from real action sequences to automatically render synthetic training videos for the action labels. We make the following contributions: (i) we investigate the extent of variations and augmentations that are beneficial to improving performance at new viewpoints. We consider changes in body shape and clothing for individuals, as well as more action relevant augmentations such as non-uniform frame sampling, and interpolating between the motion of individuals performing the same action; (ii) We introduce a new data generation methodology, SURREACT, that allows training of spatio-temporal CNNs for action classification; (iii) We substantially improve the state-of-the-art action recognition performance on the NTU RGB+D and UESTC standard human action multi-view benchmarks; Finally, (iv) we extend the augmentation approach to in-the-wild videos from a subset of the Kinetics dataset to investigate the case when only one-shot training data is available, and demonstrate improvements in this case as well.
Introduction Learning human action representations from RGB video data has been widely studied. Recent advances on convolutional neural networks (CNNs)  [38]  have shown excellent performance  [6, 15, 16, 18, 41, 82, 86]  on benchmark datasets, such as UCF101  [75]. However, the success of CNNs rely heavily on the availability of large-scale training data, which is not always the case. To address the lack of training data, several works explore the use of complementary synthetic data for a range of tasks in computer vision such as optical flow estimation, segmentation, human body and hand pose estimation  [12, 72, 76, 83, 97]. In this work, we raise the question how to synthesize videos for action recognition in the case of limited real data, such as only one viewpoint, or oneshot available at training.