fairdataihub/poster-sentry-training-data huggingface dataset:
OPeNDAP’s Server4 Building a High Performance Data Server for the DAP Using Existing Software Software Reuse The essential f eatures of Serv er4 are: Two process design can be adapted to a v ariety of dif f erent conf igurations The serv er supports sev eral dif f erent application protocols f or dif f erent ty pes of inf ormation Formal communication between the processes (to be published soon) will enable other groups to write their own f ront-ends In 1993 the Univ ersity of Rhode Island began a NASA-f unded ef f ort to dev elop a data sy stem f or ocean data that would take adv antage of the Internet. Out of that ef f ort, the Data Access Protocol, a serv er implementing the protocol and sev eral compatible client applications were dev eloped. The data serv er was based on the Common Gateway Interf ace (CGI) used f or many web-based interf aces, largely because of the simplicity of those ty pes of sy stems. As the use of the serv er spread, its limitations became more apparent. At the same time, other issues with the data sy stem became apparent and new work was started to address those issues (such as metadata non- unif ormity , searching dif f iculty and collection organization). Serv er4 is one aspect of that work. Its goals are to make it easier to solv e these problems and to reuse as much of our (and others’) existing DAP-compliant sof tware as possible. Sof tware reuse is an important part of the Serv er4 ef f ort. Since the start of work on the DAP, signif icant ef f ort has been made dev eloping DAP-compliant serv er sof tware (client sof tware too, although that is not the f ocus of this poster) and to make a new serv er we needed to use as much of that work (and knowledge) as possible. In the OLFS, the THREDDS implementation is 100% f rom the Unidata THREDDS library . Other elements were written new f or this project. In the BES, the f ramework itself is an extended v ersion of the Earth Sy stem Grid II data serv er dev eloped by NCAR/ESSL/HAO. It represents about 70% reuse. The BES Format Modules are about 5% new sof tware with the remaining 95% reused f rom the Serv er3 ‘Data Handlers’ (in f act the source code distributions f or the f ormat modules still build the Serv er data handlers too). The PPT modules used by both the BES and OLFS are inherited f rom the ESG II project OLFS Java Servlet Engine BES Unix Daemon BES Commands XML- encapsulated object Data files, Database, … DAP2 THREDDS HTML Optional THREDDS catalogs Server4 Supports several protocols and a high level of reuse using a two process architecture
PRESERVING DATA TREASURES Akansha Rawat, Dr. Dr. Jörg Hoffmann, Dr. Tanja Rottstock Institute for Strategies and Technology Assessment, Julius Kühn Institut Role of JKI Provides data for long term research in the field of plant cultivation, genetics, soil science, plant breeding and protection. German long term field trials on JKI experimental stations. On farm investigations of cultivated plants, useful insects, animal and bird species. Our Objective We aim to secure existing data from a completed project. In a long-term study over a period of 30 years, agricultural use and biodiversity in arable landscapes were recorded and analysed. Recommendations for agriculture, nature conservation and biodiversity monitoring were derived from this. Reference: Hoffmann, J. (2023). Biodiversität im Zeitvergleich. Strukturelemente und Nutzungen räumlich identischer Ackerbaugebiete 1991-1993 und 2018-2021. Auswirkungen auf die Biodiversität. Berichte aus dem Julius Kühn-Institut 224: 940 S. We plan to ensure the visibility, accessibility and future use of the long term biodiversity data sets of e.g. plants, insects, and bird species in agricultural landscapes. Establishing the workflow For our use case, we establish a workflow using the comprehensive, existing data on landscape structures, flora, cultivated plants, birds and butterfly species from BioZeit (1991-1993, 2018-2021): Biodiversität im Zeitvergleich (Hoffmann 2023) and harmonize the structure of datasets according to the FAIR data principles accompanied with a richly described metadata based on ABCD metadata schema. To effectively manage the data, an instance of BEXIS2 software has been set up at JKI. This step will provide a good basis for standardizing, reusing and integrating the datasets. Using the available data in BioZeit new refined data structures will be developed along with the metadata which will be migrated to BEXIS2 as a starting point towards the long term archiving of this data. Overall Aim In order to ensure the use of valuable, comprehensive data sets, we plan to publish and archive the data through relevant repositories (for eg., GBIF, Pangaea), ensuring its preservation, visibility and availability for current and future research. We welcome your suggestions and collaboration: Akansha.Rawat@julius-kuehn.de Source: Dahnsdorf Experimental Station, JKI archive Source: Icons https://www.carbondesignsystem.co m/guidelines/icons/library/ Source:BEXIS2,Uni Jena, https://bexis2.uni-jena.de Scan the code to know more about the project BioZeit
Design Image Interactive resource Other Sound Musical Notation resource types Practice made Practical. Reducing the OTHER of non-text Adam Vials Moore (Jisc), Jenny Evans (University of Westminster), Rachael Kotarski (British Library), Taylor Mudd (Cayuse) Discoverability Landscape: Where does non-text / practice research fit? ‘other’ or select the ‘best fit’ Persistent Identifiers Metadata standards Discoverability Practice Research • cross disciplinary • documents process • PRAG-UK report recommends a project item type Non-text outputs as such needs a community of practice to agree on what the form is so that this can be mapped to the discoverability landscape • intersectional - crosses multiple communities and goes beyond universities - is relevant to galleries, libraries, museums and archives • may have multilple pages, files, permissions, rights, contextual images, access levels, may be dynamic Harvests PDF only, uses rioxx, OpenAIRE Indexes text only, PDFs less than 5MG, one file per article Search Engine Optimisation (SEO) mobile optimised design, semantic mark-up, include keywords in item URL, canonical links Disciplines that share research with primarily non-textual outputs do not benefit from a scholarly communications landscape that is focussed on the discovery, access and inquiry of STEM textual articles and data. Bringing together experts into a community of practice, such as repositories that have built taxonomies to address non-text outputs, we propose building on the persistent identifier and metadata vocabularies that underpin global infrastructure. Enabling richer, more descriptive work types, allowing better discoverability, and bringing a future where all research is equally valued. Audiovisual Collection Event Image InteractiveResource Model PhysicalObject Sound Other ResourceTypes Other Project item type recommended by Bulley-Salin Other also used by CRediT - Contributer Roles Taxonomy
‘Storyboarding’ in the classroom Malizukiswe V. Vacu1, Craig Ehrenreich1, Nelisiwe Chonco1, Grace Aguti2, Xiangcong A. Luo3, Bianke Loedolff1† 1Faculty of AgriSciences, Stellenbosch University; 2Earth University; 3Faculty of Arts & Social Sciences, Stellenbosch University Professional Educational Development of Academics (PREDAC) 2019 vs. https://www.storyboardthat.com/https://wordsearchlabs.com/view/108017 1 2 3 4 5 Storyboarding es una herramienta visual de aprendizaje, cooperativa y narrativa que podría beneficiar a los alumnos de todas las disciplinas en las estructuras educativas. Esta pantalla visual permite a los alumnos ver las interrelaciones entre los conceptos centrales descritos en un marco de módulos. Tanto el experto como los alumnos participan en este proceso de Storyboarding a través de una discusión continua en el aula. Los conceptos e ideas se muestran en forma visual clara en un storyboard y por lo general, pueden incluir (i) una revisión del tema e introducción a conceptos avanzados dentro del tema, (ii) objetivos de aprendizaje, (iii) tiempo de preparación dentro y fuera de la clase para estudiantes, (iv) limitaciones de aprendizaje y (v) comprensión de contenido de aprendizaje complejo. Luego, los alumnos tienen el desafío inverso de encontrar conceptos básicos dentro de un rompecabezas de palabras, identificar los vínculos entre las palabras y desarrollar una pregunta desafiante dentro de su contexto. Storyboarding en clase presenta a los alumnos la oportunidad de desarrollar habilidades de pensamiento crítico, creativo y proporciona una plataforma de aprendizaje interactiva para aplicar y contextualizar los objetivos de aprendizaje. †Correspondence: bianke@sun.ac.za
NATIONAL RESEARCH DATA INFRASTRUCTURE FOR ENGINEERING SCIENCES100011101011000111010110001110101Finding & applying requirementsKnowing & choosing the right toolLearning & starting RDMAnd others...Automate your RDM!RDM is complex!Get support!Jarves, do my RDM!Your Journey starts here!JarvesThe Joint Assistant for Research in Versatile Engineering SciencesJarvessupportsresearchersoftheengineeringsciencesintheireverydaywork.ItfocusesonthereductionofeffortandtimeofRDM.Thisisachievedbyaddressingthefollowingpoints:GuidanceinRDMByorderingtheRDMac-tivitiesbasedontheiroccur-renceintheengineeringresearchprocess,JarvesprovidesastructureforRDMinengineering.DecisionSupportBasedontheresearch’sgeneralenvironment,likerequirementsoffundingorganisationsorinstitutionalboundaries,Jarvesprovidesinformationonthenextstepsandavailabletools.ConnectingbeyondAsJarvesisbasedonthePythonDjangoRESTFrame-work,itoffersabroadcon-nectivitytootherservices,allowingforseamless(meta-)dataexchange.The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) -project number 442146713.Meet the digital RDM assistant.Your funding:Your university:Your institution:Your discipline: Production TechnologyGenerate RDMGet Training ➢Overview: Research Data Management ↗ ➢Overview: Data life cycle (DLC) ↗ ➢How to: DMPs ↗ ➢Versioning Source Code –A Git Tutorial ↗ ➢Software Develop-ment: PyPi ↗ ➢…1000111010110001110101Tobias Hamann and Jonas Werheidt.hamann@wzl-mq.rwth-aachen.de WZL | RWTH Aachen University Chair of Production Metrology and Quality Management & Institute for Information Management in Mechanical Engineering (Prof. Robert Schmitt) Metadata4IngWhere to start?Where to find information?What to learn? The… Data collections explorerDMP tool:Code Repository:Access solution:…Your Input
Reference collection for regulated viruses and viroids (VIRUSCOLLECT) Funding Non-competitive funding mechanism. Each funder only pays for the participation of their own national researchers. Total funding € 300,000 Picture 1 Picture 1 Picture 1 Goals The goal of VIRUSCOLLECT is to establish a common plant viruses and viroids collection by linking collections of individual institutes via Q- bank and to make collection and reference materials publicly available for phytosanitary research and diagnostic laboratories. Research consortium AT-AGES, DE-JKI, NL-NPPO-NL (EZ) and UK- SASA Contact information Project coordinator: Annelien Roenhorst j.w.roenhorst@nvwa.nl Key outputs and results Accessibility of plant viruses and viroids of phytosanitary concern via Q-bank, which provides data on characteristics, i.e. biology, serology and sequences, and information on availability of isolates. Objectives • To define and implement quality standards for Q-bank linked collections (characterisation, maintenance and provision of isolates and reference material) • To collect and characterise isolates of selected regulated viruses and viroids and make both data and isolates available via Q-bank, i.e. viruses of cereals, potato, stone and pome fruits, whitefly-transmitted viruses, Tospoviruses 11/2013-10/2015
Comic Books in Translation: Process and Quality in Metaphor Translation Jisue Park (jisue.park@surrey.ac.uk) Study Aim Research Questions Participants Study design Results: Process The process of translating metaphors The final quality of metaphors The influence of MT on the human translation process RQ1 What is the process for translating metaphors in comic books? RQ2 Is the quality of the final product higher when translating from scratch or when MT post-editing? RQ3 Is the cognitive effort lower when translators MT post- edit metaphors than when they translate on their own? Translator (TR) Post-editor (PE) Reviewer (R) Translate without machine translation Translate with machine translation Assess the quality of translated metaphors - Google Translate - Korean → English An excerpt of comic book (189 words) Key-stroke data Screen-recording data Interview data Evaluation data Interview data Pre-questionnaire (Translators) Train translators Pre-experiment Main experiment Interview translators Pre-questionnaire (Reviewer) Review translations Train the reviewer Interview reviewer Analyse results Percentage of pause length for each translation phase Translator Post-editor Translation phase classified by Jakobsen (2002) Metaphor TR version PE version Label Pause length Label Pause length #1 Good 01:20.625 Fair 01:26.437 #2 Fair 00:12.187 Fair 00:50.750 #3 Poor 01:08:264 Fair 02:00.781 #4 Fair 01:06.999 Fair 01:06.344 #5 Fair 00:40.796 Poor 00:30.500 #6 Fair 00:39.282 Fair 00:24.328 #7 Poor 05:40.578 Fair 05:40.312 Total - 11:48.731 - 11:58.452 Average - 01:41.142 - 01:42.571 Results: Quality Reviewer ranking & links between effort and pauses Pausing is an indicator of mistrusting machine translation. Higher effort does not equal high quality. References Jakobsen, A. L. (2002). Translation drafting by professional translators and by translation students. Copenhagen studies in language, 27, 191-204.
These were drawn from theories of Visual Cognition and from requirements gathered from our experimental trials. ROBOTS WITH COMMON SENSE Improving Sensemaking in Service Robotics NAIVE PHYSICS LEARNING MODEL BUILDING FAST PERCEPTION We derived a set of "ingredients", i.e., required knowledge capabilities, for robots to exhibit human-like performance on sensemaking tasks. We identified the types of common sense knowledge which enable these capabilities. Hypothesis: the integration of common sense knowledge and knowledge-based reasoning with Machine Learning improves a robot's sensemaking capabilities. COMPOSITIONALITY INGREDIENTS OF VISUAL INTELLIGENCE Infants can already grasp basic principles of Physics before 6 months of age. Hence, large amounts of background knowledge are needed to help a robot to correctly interpret the observed scenario. The shiny object is NEXT TO a keyboard GENERIC 2D VIEWS MOTION VISION MACHINE READING Service robots assist humans by performing tasks in a variety of scenarios, including shops, restaurants, healthcare, delivering parcels, accessing hazardous environments, and others. PhD Student: Agnese Chiatti Supervisors: Enrico Motta Enrico Daga Our visual perception is extremely fast and we can learn to recognize new objects even from our very first exposure to them. The ability to read text from images is essential to recognize labelled items and signs. We categorise objects and actions differently based on the perceived movement (or lack thereof). FOCUS ON VISUAL INTELLIGENCE RESEARCH PROBLEM PROPOSED APPROACH The images cast at the back of our eye are 2-dimensional. We construct the 3D mentally. MEET HANS! Web: https://robots.kmi.open.ac.uk Methods based on Machine Learning have shown promising results. Nonetheless, machine Visual Intelligence is still inferior to human Visual Intelligence in many ways. The human eye identifies the different objects parts and the different nearby objects separately. Our hypothesis is being tested in the case of HanS, the Health & Safety robot inspector currently under development at KMi. Service robots need to make sense of complex and fast-evolving environments. They do so by using their perceptual sensors (e.g., vision, depth, touch) to identify the objects around them. However, different objects can look very similar to a robot (e.g., in certain light conditions the shiny surface of a radiator may look like a screen to a robot) It is a monitor, not a radiator. Our work focuses on the robot’s capability to use its vision system, reasoning components and background knowledge to make sense of its environment. Photo credits: Enrico Motta, Agnese Chiatti
(Halperin et al., 2018) CyDIVE: Targeted hypermutation system for cyanobacteri> I Active module from EvolvR (Halperin et al., 2018 I Dynamic expression by rhamnose inducible promoter (Kelly et al., 2018 I Extra repression by theophylline riboswitch (inspired by Liu et al., 2020 I Multiplexed gRNA array & counter selection by MazF (Cengic et al., 2022 I Broad host RSF1010 backbone In vivo directed evolution platform development In vitro variant library generation & directed evolution In silico: D1 structural modelling to identify potential engineering target 832 D1 sequences inc. Eukaryotic D1s Structure prediction by AlphaFold2 Global structural alignment by mTM-Align Literature survey on D1 mutagenesis study “Common core” Main variable regions AA222-267 “D-E loop” AA295-317 24 representative D1s structural alignment Evolutionary engineering of Photosystem II: by far, by nature (Oliver et al., 2023) Photosystem II: Ever evolving enzymq I Great knowledge on how standard PS II works, but less on divergent typed I Functional diversity of PS II shaped by natural evolution is reflected in reaction centre protein D1 subunit diversity: Modular tuning mechanism_ I Std D1, D1’ (microoxic), rD1 (?), srD1 (Chl f synthase I Harnessing evolution as an engineering tool: Focusing on D1 as primary target, directed evolution is applied to engineer PS II beyond its known functional horizon Department of Life Sciences, Imperial College London, London, United Kingdom tom.kim@imperial.ac.uk Tom Dongmin Kim§, Daniella Pretorius, Wenxin Li, Francesca Levi & Tanai Cardona Directed evolution of Photosystem II: Status update and structural modelling for targeted engineering
RESEARCH POSTER PRESENTATION DESIGN © 2019 www.PosterPresentations.com Project Description To: Project Framework Methodology Anticipated Results University of Pretoria, u17367311@tuks.co.za Tendai Mataranyika SA Universities Institutional Repositories Publications Mapping and UNSDGs Classification Nexus Open Repositories Conference 2023, Cape Town, South Africa Research Output Clustering OpenAIRE Repository Data Sources SDG Classification Service This project still in its early stages seeks to map Institutional Repositories of selected South African universities through clustering and classification using the SDG classification service. This mapping will provide insight on which SDG research themes have been worked on by which universities. Information derived from this project will feed other research consuming communities and policy makers interested in understanding how Higher education contribute to development initiatives.
Use of barcoding, from theory to practice (PRACTIBAR) Funding Non-competitive funding mechanism. Each funder only pays for the participation of their own national researchers. Total funding € 207 000 Picture 1 Picture 1 Picture 1 Goals The main goal of the project is to raise awareness on barcoding as a generic method for pest identification and to train experts of the competent organisations, as highlighted at the end of the Euphresco project and as recommended by the EPPO Panel on Diagnostics and Quality Assurance in 2016-01- 19/21. Workshops and proficiency tests (PT) will be organised and training material developed. Research consortium EPPO (INT), AGES (AT), ILVO (BE), CFIA (CA), AU (DK), ANSES (FR), JKI (DE), SASA (GB), VAAD (LV), NVWA (NL), INIAV (PT), APHIS (US), UG (CA), FA (RS) Contact information Project coordinator: Françoise Petter fp@eppo.int Baldissera Giovani bg@eppo.int Objectives and key outputs Workshops will be organised in the framework of the project that will allow participants to be informed of the most recent advancements on the method and to receive training on a number of tools for data analysis: sequencing analysis softwares that allow assembly of raw sequence data (e.g. Geneious), online databases (Q- bank, NCBI, BOLD). Raw sequence data will cover examples from arthropods, bacteria, fungi, invasive plants, nematodes, phytoplasmas, viruses and viroids, depending on the needs and wishes of the workshops’ participants. A proficiency test will be organised at the conclusion of the workshops to determine the performance of individual laboratories. 05/2017-04/2019
KARMA.edu 1 - :مقدمــــــــة توفرKarma. edu لإلرشاد األكاديمي مجموعة متنوعة من األساليب التعليمية والخدمات والموارد المقدمة للطالب في محاولة لمساعدتهم على تسريع تقدمهم في التعلم و اللحاق بأقرانهم و تلبية معايير التعلم في الجامعة . 2 - ـ ال رؤيــــــــــا : تمكين الطالب من أن يكونوا متعلمين نشطين ومسؤولين يستفيدون بشكل كامل من العديد من الفرص التي توفرها الجامعة في كل من البيئات األكاديمية والمشاركة المجتمعية . 3 - :المهـمــــــة من خالل الشراكات المهنية الداعمة والتعاونية مع ،الطالب يكرس المستشارون األكاديميون جهودهم لتعليم الطالب كيفية الوصول إلى المعلومات األساسية واكتساب المهارات الالزمة التخاذ قرارات مستنيرة تؤدي إلى تحقيق أهدافهم التعليمية والوظيفية والحياتية. 4 - :القيـــــم يلتزم المستشارون األكاديميون بتقديم خدمات استشارية عالية الجودة وشاملة تلبي االحتياجات الفريدة لكل ،طالب ويكرس طاقم اإلرشاد األكاديمي جهوده لتهيئة بيئة تدعم متطلبات الطالب. 5 - :األهــــــداف • دعم ثقة الط الب بأنفس هم ومساعدته م كذلك اكتشاف قدراته م وا مكانياته م الدراسية وتطوير مهاراتهم لل تكيف مع بيئته م الدراسية واالجتماعية والعلمية والتغلب على أية صعوبات قد تعترض مساره م الدراسي. • تنمية وتطوير مهارة التخطيط لدى الطلبة ليطبقوا الخطة الدراسية المالءمة والمناسبة لهم. • تنمية وتطوير قدرة الطلبة على تحديد أهدافهم واختيار التخصص الذي يتالءم وقدراتهم وميولهم. • تنمية مهارات تنظيم واستثمار الوقت.
POŠKOZENÍ LANGERHANSOVÝCH OSTRŮVKŮ MIKROPORACÍ S siRNA A BEZ siRNA Tomšovská V. 1,4, Fábryová E.1,4, Leontovyč L.1, Plecitá-Hlavatá L.3, Holendová B.3, Špitálníková Veřtátová M.1, Girman P 2. Berková Z 1, Kříž J 2. 1Laboratoř Langerhansových ostrůvků, CEM, IKEM, Praha 2Centrum diabetologie, IKEM, Praha 3Laboratoř pro výzkum pankreatických ostrůvků, Fyziologický ústav AVČR, Praha 41.Lékařská fakulta, UK, Praha Úvod: Mikroporace je jednou z transfekčních metod využívajících elektrické pulzy. Lze ji použít pro zavedení nukleových kyselin (NK) do buněk. Pomocí krátkých elektrických pulzů se vytvoří mikropóry v buněčné membráně, kterými NK mohou vstoupit do buněk. Testovali jsme potenciální škodlivé účinky této metody pro transfekci siRNA do buněk Langerhansových ostrůvků (LO). Metody: Mikroporace je jednou z transfekčních metod využívajících elektrické pulzy. Lze ji použít pro zavedení nukleových kyselin (NK) do buněk. Pomocí krátkých elektrických pulzů se vytvoří mikropóry v buněčné membráně, kterými NK mohou vstoupit do buněk. Testovali jsme potenciální škodlivé účinky této metody pro transfekci siRNA do buněk Langerhansových ostrůvků (LO). Výsledky: Všechny provedené testy ukázaly neporušenou funkci beta buněk bez rozdílu mezi mikroporovanými a kontrolními LO. Schopnost uvolňování inzulinu během perifuzního testu a rychlost spotřeby kyslíku nebyly porušeny. Při použití fluorescenčního testu integrity membrán bylo procento mrtvých buněk v LO: 15,9 % u mikroporace s siRNA, 13,7 % u mikroporace a 10,6 % u kontroly. qRT-PCR analýza produkce zánětlivých faktorů rovněž neprokázala rozdíly mezi vzorky. Závěr: Mikroporace nepoškozuje Langerhansovy ostrůvky a zdá se být bezpečnou technikou pro transfekci siRNA do buněk Langerhansových ostrůvků. Podpořeno projektem Národní institut pro výzkum metabolických a kardiovaskulárních onemocnění (Program EXCELES, ID: LX22NPO5104) – Financováno Evropskou unií – Next Generation EU. Podpořeno MZ ČR – RVO („Institut klinické a experimentální medicíny – IKEM, IČ 00023001“)