dataesr/scientific-paragraphs-categorization huggingface dataset:
2 , it enables to a) load a MTG file format that encodes in a standard way (topology and property) the description of strawberry architectural data and provides a generic and multiscale MTG data structure , b) visualize in 2D and 3D architectural data of one plant, one or several genotypes), c) analyze the architectural data at different scales (i. e. node, module and plant scales) by providing various exploratory functions to plot and extract data for further analysis such as estimation of HMSM models. Second, the Jupyter Notebook tutorials, designed for scientists, provide an interactive web interface to illustrate the various steps of the pipeline and run existing analyses in a reproducible way  (Kluyver et al. , 2016). The full software and user documentation, including some tutorials, is available and updated after each change (https://strawberry.
rtfd. io). Finally, a web application has been designed on the top of the OpenAlea. Strawberry package to automatically visualize and analyze strawberry architecture for end-users such as breeders. A demonstration of the application is provided through a video (see Supplementary Video, https://data. inrae. fr/dataset. xhtml?persistentId=doi:10. 57745/XJY5RO). This application is built using Voilà and Jupyter frameworks. The web application can be installed directly from github or using the Docker image  (Merkel, 2014).
4C). Furthermore, the fate of the AXM depends on its position on the axis, as demonstrated here. An additional level of complexity in strawberry is that acquiring high-resolution plant architecture datasets requires a destructive step, namely dissecting the primary crown (PC), which is a compact structure  (Tenreira et al. , 2017). Powerful tools are thus required to analyze the large and complex datasets produced by phenotyping and to represent the evolution over time of 2D and 3D architectures of plant and inflorescence and their impact on flower and fruit production. To address these challenges, we first developed a free and open-source software library and web application, called OpenAlea. Strawberry, that allows the analysis of phenotypic traits relevant to strawberry architecture, which include the occurrence of branching modules according to their order, as well as the complexity of inflorescences.
By combining 2D and 3D representation of strawberry plant development and exploratory statistical methods, our software enables comprehensive comparison of branching structure and inflorescence complexity in plants from diverse genotypes or under different environmental conditions. Using OpenAlea. Strawberry, we could analyze over time the phenotypic variation in plant and inflorescence architecture of six strawberry genotypes that were extensively described monthly at node scale. By considering the spatio-temporal growth of the plant and formally describing plants through Multiscale Tree Graphs (MTG)  (Godin and Caraglio, 1998) , we were able to highlight and analyze the diversity of the growth strategies used by the various strawberry genotypes to build their architecture. We then extracted from OpenAlea.
(F) 2D schematic representation of the plant generated by the OpenAlea. Strawberry package. Only the PC (zerothorder module) is represented. The colored spheres indicate the non-visible/undeveloped higher-order modules (starting with "f") of the AXB, as determined by the developmental stage of the AXM. TB: Terminal Bud; FB: Floral Bud; IB: Initiated Bud; VB: Vegetative Bud. (A) 3D representation of the 9 plants of the genotype Ciflorette during the seasonal production. The seasonal production is represented by six successive dates of observation. Colors represent module orders: green for order 0, red for order 1, blue for order 2, yellow for order 3, and purple for order 4. The size of the light blue boxes representing the inflorescence is proportional to the number of open flowers. (B) 2D representation of the genotype Ciflorette during the seasonal production.
(E) MTG formalism of the plant with stage property. Colors represent the module order: green for order 0, red for order 1 and blue for order 2. Botanical entities as named in MTG: F, leaf; f, leaf primordium (leaf enclosed in the bud); ST, stolon; BT, bud; ht, inflorescence enclosed in BT; HT: visible inflorescence (not represented in this plant); FB: Floral Bud; VB: Vegetative Bud. '+' indicates a branching link and '< ' a succession link. (F) 2D schematic representation of the plant generated by the OpenAlea. Strawberry package. Only the PC (zerothorder module) is represented. The colored spheres indicate the non-visible/undeveloped higher-order modules (starting with "f") of the AXB, as determined by the developmental stage of the AXM. TB: Terminal Bud; FB: Floral Bud; IB: Initiated Bud; VB: Vegetative Bud.
Acknowledgements. This paper is dedicated to the memory of our dear co-worker  Yann Guédon , who was central to this work and passed away while this paper was being prepared. The authors thank  Lauriane Rouan  for her constructive advice. We also thank  Christian Gauthier  and  Cédric Duranton  from  Invenio  for strawberry culture and  Fabien Barthoulot  for help in phenotyping.
Data availability. All data are available in Github: https://github. com/openalea/strawberry/tree/master/share/data Downloaded from https://academic. oup. com/jxb/advance-article/doi/10. 1093/jxb/erad097/7143673 by INRAE Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement user on 28 April 2023.
In the grey rectangle on the right, detail of the dissection of the TB. (E) MTG formalism of the plant with stage property. Colors represent the module order: green for order 0, red for order 1 and blue for order 2. Botanical entities as named in MTG: F, leaf; f, leaf primordium (leaf enclosed in the bud); ST, stolon; BT, bud; ht, inflorescence enclosed in BT; HT: visible inflorescence (not represented in this plant); FB: Floral Bud; VB: Vegetative Bud. '+' indicates a branching link and '< ' a succession link. (F) 2D schematic representation of the plant generated by the OpenAlea. Strawberry package. Only the PC (zerothorder module) is represented. The colored spheres indicate the non-visible/undeveloped higher-order modules (starting with "f") of the AXB, as determined by the developmental stage of the AXM.
of 18 Current Protocols The table will show a filtered list according to the values in the params section (see snippet from step 2). In this case, all future courses aggregated in TeSS will be live-filtered on the general search term "Python" and display only the courses from Belgium and the United Kingdom. 5. Remove the term "Python" from the general search. The table should now be updated with all upcoming courses from Belgium and the United Kingdom. By adapting other facets such as "Scientific Topics" and "Target audience", other filtering schemes can be established.
of 18 Current Protocols 15. In the "Address" and related fields, start to type the address. Once you select an address, the Google maps view will be shown and the fields Venue, City, Region, Country, and Postcode will be filled in automatically. For our example: Address: Herestraat 49 Venue: Campus Gasthuisberg City: Leuven Region: Flemish region Country: Belgium Postcode: 3000 16. In the "Eligibility" field, enter "First come first served". The other two options are "Registration of interest" and "By invitation". More than one type can be provided, if applicable. 17. In the "Organiser" field, enter the organizer of the event: "ELIXIR-Belgium". This is a free text field. 18. In the "Contact" field, enter the name of the training coordinator. This is also a free text field, and one or more contact persons can be added, preferably with an email address.
26. In the "Tech requirements" field, enter "R, Rstudio, various R packages". Any technical requirements can be added. Ideally, a link to installation instructions should also be provided. This field supports markdown syntax. 27. For the type of credit or recognition of attendance that will be delivered, enter "Certificate of attendance recognized by Doctoral School". Other types of recognition can be entered as free text. 28. In the "External resources" field, search for "DESeq2" in the tools section. Once the tool has been found in bio. tools, click on the "+" sign to add it to the entry. You can associate policies, standards, and databases (provided by FAIRsharing) with your training event. In each case, a list of existing resources will be listed when you start typing keywords (click on the "+" sign to select).
of 18 Current Protocols 1. Log in to TeSS at https:// tess. elixir-europe. org. 2. Click on the "Material" menu. 3. Click on "Register training material" to be directed to the form. As an example, the following steps use training material about the use of AlphaFold for structure prediction of proteins and binary protein complexes. 4. In the "Title" field, enter "How to use AlphaFold for prediction of single proteins or protein complexes using the HPC". 5. In the "URL" field, enter "https:// elearning. bits. vib. be/ courses/ alphafold/ ". 6. In the "Description" field, enter: "Architectural details, code and trained AlphaFold models were released by Deep-Mind in 2021. Given the high computational cost of deep learning algorithms, specialized hardware and software are required. Online solutions are available but come with considerable disadvantages.
Therefore, the Flemish Supercomputer Center (VSC) provides high performance computing facilities, on which AlphaFold is installed and fully operational. This course gives a solid introduction on how Al-phaFold can be easily and swiftly accessed using the HPC. This tutorial material was created by Jasper Zuallaert (VIB-UGent), with the help of Alexander Botzki (VIB) and Kenneth Hoste (UGent). For questions and remarks, feel free to contact jasper. zuallaert@vib-ugent. be or training@vib. be. " This field supports markdown syntax 7. In the "Keywords" field, enter "Structure prediction", "AlphaFold Database", and "Protein complex prediction". A list will be displayed when you start typing keyword(s). If your desired keywords are not listed, you can add them. You may add and select as many as you like.
14. In the "Authors" field, enter the name(s) of the author(s) of the material: "Jasper Zuallaert" and "Alexander Botzki". 15. In the "Contributors" field, enter the name(s) of contributor(s) to the material: "Kenneth Hoste". Preferentially, specify your ORCID. 16. In the "Events" field, select "Structural Prediction of Proteins using AlphaFold on the HPC" from the dropdown list. We recommend doing the linking from the event side (when the event is registered) according to Basic Protocol 3, step 30. 17. In the "Target audience" field, select "Life scientists with programming skills" from the dropdown list. Add as many applicable options as you want 18. In the "Prerequisites" field, add: "You are encouraged to use your own laptop. For those who do not have a laptop, the YASARA can be run in a remote Linux environment (access to cloud via web browser).
20. For "Learning Objectives", enter: "Understand the technical methodology of AlphaFold2, Understand the technical setup at the Flemish SuperComputer, Predict three-dimensional protein models with AlphaFold2 using the HPC at the VSC UGent. " We recommend applying the Bloom hierarchy of cognitive skills to formulate the learning objectives. For more detailed information about our pedagogical model, please browse the course material of the ELIXIR Train the Trainer course at https:// github. com/ TrainTheTrainer/ ELIXIR-EXCELERATE-TtT. This field supports markdown syntax. 21. Enter the "Date created" (1 April, 2022). 22. Enter "no value" in "Date modified". If applicable, enter the date the material was last modified. 23. Enter the "Date published" (28 April, 2022). 24. In the "Resource types" field, enter "Tutorial" and "Slides" from the dropdown menu.
Other options include video, slides, pdf, etc. 25. In the "Scientific topics" field, select "Structure prediction, Protein Structure Analysis, Machine Learning". You can associate policies, standards, and databases (provided by FAIRsharing) or tools (provided by bio. tools) with your training event. In each case, a list of existing resources will be listed when you start typing relevant words (click on the "+" sign to select). 27. In the "Nodes" field, use the dropdown list to select "Belgium" as the node to which the authors and contributors belong. 28. In the "Operations" field, select "Multiple Sequence Alignment, Structure Visualisation". Operations can be added according to the EDAM ontology. If you do not see a matching entry, you can use the keywords field. 29. Click on "Register training material" to finalize the registration.
Necessary Resources Desktop or a laptop computer or a mobile device with fast Internet connection Up-to-date web browser such as Google Chrome, Mozilla Firefox, Apple Safari, or Microsoft Edge  For training events, select Course/CourseInstance by using the profile described at https: // bioschemas. org/ profiles/ CourseInstance. 2. Manually annotate the HTML website containing the training material (here, index. html; https:// nanocommons. github. io/ tutorials/ enteringData/ index. html) using JSON-LD markup. More specifically, enter a JSON object as shown below in a script element with the attribute type="application/ld+json". This script element needs to be placed as a child of the head element of the HTML page. This results in the following script HTML element. <script type="application/ld+json"> { "@context": "https://schema.
org/", "@type": "LearningResource", "http://purl. org/dc/terms/conformsTo": { "@type": "CreativeWork", "@id": "https://bioschemas. org/profiles/TrainingMaterial/1. 0-RELEASE" }, "name": "Adding nanomaterial data", "version": "0. 9. 3", "description": "This tutorial describes how nanomaterial data can be added to an eNanoMapper server using an RDF format. ", "license": "https://creativecommons. org/licenses/by/4. 0/", "keywords": "ontologies, enanomapper, RDF", "url": "https://nanocommons. github. io/tutorials/enteringData/", "provider": { "@type": "Organization", "name": "NanoCommons", "url": "https://www. nanocommons. eu/" }, "audience": { "@type": "EducationalAudience", "educationalRole": "Graduates" }, "inLanguage": { "@type": "Language", "name": "English", "alternateName": "en" }, "author": [ { "@context": "https://schema.
org", "@type": "Person", "name": "Egon Willighagen", "identifier": "https://orcid. org/0000-0001-7542-0286", "orcid": "https://orcid. org/0000-0001-7542-0286" } ] } </script> 3. Validate the individual page with the Schema. org validator at https:// validator. schema. org by pasting the URL https:// nanocommons. github. io/ tutorials/ enteringData/ index. html into the Fetch URL tab (Fig. 4 ). More complex mechanisms for sitemap creation are available by using content management systems such as Drupal and using specific sitemap plugins.
COMMENTARY Background Information During the ELIXIR-EXCELERATE project, the TeSS platform was established as the key application for bioinformatics training events and material aggregation. During further adoption of the TeSS platform as such on the international level (e. g. , in Australia and the Netherlands), it became obvious that the implementation of individual automatic content scrapers for more than 30 content providers lead to an enormous diversity of the code base and finally to a maintenance challenge. Therefore, during the last two to three years, a strategic reorientation of the automated harvesting procedure has been proposed and implemented in TeSS using the Bioschemas profiles for Course, CourseInstance, and TrainingMaterial. This approach will put content providers in a more prominent position, since they are in turn responsible for more structured delivery of more comprehensive metadata of their training events and materials to TeSS as the aggregation platform.
In the long run, this will increase the quality level of aggregated resources and their usefulness to the TeSS user base. Content providers looking to promote the use of particular tools and databases via their course offerings or specific training materials can find information in other ELIXIR registries, such as bio. tools (https:// bio. tools) for tools and web services and FAIRsharing. org (https:// fairsharing. org) for databases, standards, and policies. Lately, such registries have started to track training relating to the resources they list. Therefore, it is mutually beneficial that TeSS provides the relevant links (allowing users to search for resource-specific training) and the resource pages in bio. tools and FAIRsharing that contain reciprocal links for users to discover relevant training in TeSS.
Related to the provisioning of persistent identifiers, TeSS provides the Digital Object Identifier (DOI) as a unique means to identify training materials. Given that minting services by, for example, university libraries are not yet broadly available for teaching and learning material in contrast to research datasets and collections, associated workflows, software, and models, we recommend using services like OSF or Zenodo to get own unique, persistent URLs and DOIs. Given the engagement of the ELIXIR Train the Trainer instructor's community to create a vibrant environment for reciprocal support and exchange of experiences via the ELIXIR Train the Trainer Programme, TeSS content providers are encouraged to follow bestpractice guidance on course and training material development  (Via et al.
Submitting a change will write it to a new branch in your fork <your github user name>/bioschemas_sources, so that you can send a pull request afterwards. If you have implemented Bioschemas markup on your website and would like your content to appear in TeSS, see our "Bioschemas sources" repository for detailed information on how to proceed: https:// github. com/ ElixirTeSS/ bioschemas_sources# readme.
The validation procedure will indicate if you have used non-existing properties of the Bioschemas profile. If error messages are returned, see Troubleshooting below. 4. Create a sitemap listing the material page URL and save it as sitemap. xml. The sitemap. xml file needs to be publicly browsable on the internet. In our example, the manually created sitemap. xml is published at https:// nanocommons. github. io/ sitemap. xml.
Acknowledgments The fundamental work for building TeSS as a platform was part of the  ELIXIR-EXCELERATE  project  2014-2018  and we are grateful for the efforts provided from across the  ELIXIR Nodes and ELIXIR Platforms. We are also grateful for support and funding of this work by the  ELIXIR Scientific Programme 2019-2023  (https:// elixir-europe. org/about-us/ what-we-do/ elixir-programme). Furthermore, we would like to acknowledge  Christof De Bo (ELIXIR-BE, VIB Bioinformatics Core)  for his support creating the illustration in Figure 3.
Widgets are chunks of JavaScript code that can be copied into the website source code to display TeSS content. Several different styles and functionalities are available from our configurable widget suite. Widgets can be used to enhance your site and offer your community lists of relevant events or training resources. Examples of code can be found at https:// elixirtess. github. io/ TeSS_widgets/. Necessary Resources Desktop or a laptop computer or a mobile device with fast Internet connection Up-to-date web browser such as Google Chrome, Mozilla Firefox, Apple Safari, or Microsoft Edge 1. For a tabular layout of events with filtering options, choose the specific widget type Events table with a sidebar. A working example is shown on the GitHub pages from the ELIXIR TeSS team at https:// elixirtess.
github. io/ TeSS_widgets. For training material, investigate the example for train- ing materials on the same website. 2. Manually annotate the HTML website (e. g. , index. html) with the code snippet for the TeSS widget. More specifically, enter the code snippet as shown below as a sibling of a div element. <link rel="stylesheet" property="stylesheet" href="https://elixirtess. github. io/TeSS_widgets/css/tess-widget. css"/> <div id="tess-widget-events-table" class="tess-widget tess-widget-faceted-table"></div> <script> function initTeSSWidgets() { TessWidget. Events(document. getElementById('tess-widget-events-table'), 'FacetedTable', { opts: { columns: [{name: 'Date', field: 'start'}, {name: 'Name', field: 'title'}, {name: 'Location', field: 'location'}], allowedFacets: ['scientific-topics', 'country', 'city', 'target-audience'], facetOptionLimit: 5 }, params: { q: 'Python', country: ['Belgium', 'United Kingdom'] } }); } </script> <script async="" defer="" src="https://elixirtess.
github. io/TeSS_widgets/js/ tess-widget-standalone. js" onload="initTeSSWidgets()"></script> 3. Save the HTML page and serve the HTML page for visual inspection. 4. Visually validate that the rendered HTML page includes a table with the events ex- tracted from TeSS. 26911299, 2023, 2, Downloaded from https://currentprotocols. onlinelibrary. wiley. com/doi/10. 1002/cpz1. 682 by Universiteitsbibliotheek Gent, Wiley Online Library on [08/08/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License INTEGRATING TeSS WIDGETS ON YOUR WEBSITE.
If you never worked with R, you should attend the R introduction training first. " This field supports markdown syntax. 14. In the "Learning objectives" field, list the learning objectives of the event, such as: Learn how RNA-seq reads are converted into counts Understand QC steps that can be performed on RNA-seq reads Generate interactive reports to summarize QC information with MultiQC Use the Galaxy Rule-based Uploader to import FASTQs from URLs Create a Galaxy Workflow that converts RNA-seq reads into counts We recommend applying the Bloom hierarchy of cognitive skills to formulate the learning objectives. For more detailed information about our pedagogical model, please browse the course material of the ELIXIR Train the Trainer course at https:// github. com/ TrainTheTrainer/ ELIXIR-EXCELERATE-TtT.
To commit your change, click the button to open a pull request, which will then be reviewed. After review, the new training material will appear in TeSS. In our example, this is NanoCommons and https:// www. nanocommons. eu/. 7. Go to https:// github. com/ ElixirTeSS/ bioschemas_sources/ edit/ main/ sources. yml to edit the sources. yml file and add your Content Provider details along with the URL to your sitemap: title: NanoCommons url: https://www. nanocommons. eu/ source: https://nanocommons. github. io/sitemap. xml 8.
Throughout this paper, code examples are given in the Python programming language and HEALPixel 39  nside numbers  (Górski et al. 2005; Zonca et al. 2019)  are always expressed in the NESTED scheme. The desitarget code base, as well as the entire history of its development, is publicly accessible. 40 Much of the data model for files produced by desitarget, including definitions of quantities in these files, is also available online. 41.
Bits and Bitmasks The chief purpose of the desitarget pipeline is to determine which sources selected by a variety of algorithms will be targeted for follow-up spectroscopy by DESI. To record that a specific source has been selected by a particular targeting algorithm, desitarget uses a number of bitmasks. A tutorial to further help elucidate DESI bitmasks is available on GitHub. 42 he full set of targeting bitmasks assigned by desitarget will be tabulated in forthcoming DESI Data Release papers (e. g. ,  DESI Collaboration et al. 2023, in preparation). Here, though, we will introduce (and refer to) some of the principal DESI targeting bitmasks to help illustrate the functionality of the desitarget pipeline.