fairdataihub/poster-sentry-training-data huggingface dataset:
IRPPS E-PUBLISHING SERVICE: PERFORMANCE ASSESSMENT AND DEVELOPMENT OF FUTURE SERVICES Roberta Ruggieri, Fabrizio Pecoraro, Marianna Nobile, Rosa Di Cesare National Research Council, Institute for Research on Population and Social Policies, Rome, Italy • Irpps library supports Open Access (OA) promoting digital scholarship as an alternative channel to diffuse niche products in Humanities and Social Sciences • In 2012 Irpps launched “Cnr-Irpps e-Publishing”, a service to manage internal editorial products based on Open Journal System (OJS). The software was customized to support the research needs of its community • Two series are published: Monographs and Working Papers (WPs) • Contents & submission: guideline for the submission and for the management of final editing • Peer review: internal peer review of the WP series, external peer review for Monographs • Copyright & licensing: creative commons, version 3.0. Free access for all contents • Access & preservation: persistent identifier and long-term preservation in Solar - CNR institutional repository Improvements • Quality of abstract • Complete metadata • Accuracy of cited references Background Background • Upgrading to OJS 3.0 with additional functionalities implemented (Altmetrics) • Digitization of previously published works that represent important achievements of IRPPS research results • Development of a data repository linked to IRPPS publications to increase the replicability and reusability of research in Social Sciences • Improvement of the practices and infrastructures to support data sharing and citations Publication workflow Publication workflow Continuity …..and changes to improve quality and quantity of IRPPS publications Continuity …..and changes to improve quality and quantity of IRPPS publications Further enhancements Further enhancements Policies Policies This work is licensed under a Creative Commons Attribution 4.0 International License. Roberta Ruggieri: r.ruggieri@irpps.cnr.it Editorial quality Editorial review and technical editing, that occur before publication in two phases: pre- and post-acceptance Scientific quality Peer review process Improvements • Relevance • Usefulness • Methodological and ethical soundness of research Library catalogue: 118 WPs + 12 Monographs 2012 1981 Page views in 2011: around 5000 2018 Cnr-Irpps e-Publishing service: 100 WPs + 10 Monographs Page views: 2013-2018 around 110000, 16000 users Page views: 2017 around 11000, 3000 users 29 WPs cited 151 citations 70000 downloads 85000 views
The Keystone Women of British and Irish Botany 1856 to 1932 The network of British and Irish Botanists created from herbari- um specimens digitised on Herbaria@home. Each dot represents a botanist, each line represents a connection between the bota- nists. These links between people are that they either collected together, swapped specimens or determined each others speci- mens. Botanists with only one connection have been filtered out to improve readability and also highlight the most connected botanists. The network has been partitioned into 9 modularity classes to give a rough indication of community structure. Fur- ther details can be found in Groom et al. (2014). Only Woman botanists have been named in order to show how well integrated they were into the network of botanists at the time. The network has been laid out using a Fruchterman Riengold algorithm using Gephi 0.8.2 (Bastian et al., 2009) Bastian M., Heymann S., Jacomy M. (2009). Gephi: an open source software for exploring and manipulating networks. Internation- al AAAI Conference on Weblogs and Social Media. Groom QJ, O’Reilly C & Humphrey T (2014). Herbarium specimens reveal the exchange network of British and Irish botanists, 1856– 1932. New Journal of Botany. 4(2) 95-103. Quentin Groom
Predic'on of an'bio'c resistance from genomic data using machine learning and deep learning approaches Valen&n Laroche, Edithe Selwa, Bogdan I. Iorga Ins$tut de Chimie des Substances Naturelles, CNRS UPR 2301, Université Paris-Saclay, Gif-sur-YveDe, France bogdan.iorga@cnrs.fr We propose a very efficient knowledge-based approach for input data encoding or dataset enrichment to overcome the intrinsic limitations (small amount of experimental sample, imbalanced datasets) encountered with the use of genomic and biomedical data in machine learning or deep learning predictive models. The KNOTE package for unbalanced dataset enrichment is freely available at the URL: hJps://github.com/IorgaLab/KNOTE Conclusion Introduction This work was supported in part by the Laboratory of Excellence in Research on Medication and Innovative Therapeutics (LERMIT) [grant number ANR-10-LABX-33], by the JPIAMR transnational project DesInMBL [grant number ANR-14-JAMR-0002] and by the Région Ile-de- France (DIM MALINF). Acknowledgements Results Three diﬀerent encoding modes of genomic data, each one with its advantages and limita'ons, were used for the prepara'on of input datasets. The performance of several machine learning and deep learning approaches was evaluated using these datasets. The best performance was obtained with Convolu'onal Neural Network approach and knowledge-based dataset encoding. However, a clear limita'on was the unequal distribu'on of classes leading The availability of increasing amounts of genomic and biomedical data makes machine learning and deep learning the methods of choice for the prediction of antibiotic resistance, a worldwide health problem of utmost importance. However, several strong limitations have hampered the development of this field. to imbalanced datasets. The dataset enrichment performed with publicly available soaware (SMOTE, ROSE) provided only liJle improvement in the predic'on accuracy, due to errors introduced during enrichment. In these condi'ons, we have developed a new tool called KNOTE (KNowledge-based Oversampling TEchnique) for the error-free enrichment of imbalanced data from genomic and biomedical studies, to be used in machine learning or deep learning predic'ons. The use of KNOTE- enriched datasets provided up to 95-98% accuracy in the predic'on of an'bio'c resistance. 1. Kos, V.N.; Déraspe, M.; McLaughlin, R.E.; Whiteaker, J.D.; Roy, P.H.; Alm, R.A.; Corbeil, J.; Gardner, H. "The resistome of Pseudomonas aeruginosa in relationship to phenotypic susceptibility" Antimicrob. Agents Chemother. 2015, 59, 427-436. Reference Materials and methods Input datasets were prepared from 390 genomes of Pseudomonas aeruginosa with known minimum inhibitory concentra'on (MIC) values for 4 an'bio'cs [1], using three approaches: knowledge-based (output of ResMiner, a bacterial genome analysis tool developed in our group), n- grams and k-mers. Data availability
Workflow Tool for Archaeological Experiments and Analytics Workflow Tool for experiments We are developing an easy-to-use visual workflow tool that will allow researchers to record and illustrate meta- and paradata: experimental design, the protocol of sample preparation, procedure or analysis. The tool incorporates different types of experimental setup, ranging from high-tech controlled automated procedures in a lab to full-scale reconstructions in the field or well-documented re-enactment by a broader public (CC). Users will be able to customize the developed prototype, and increasing familiarity with their needs will be used to improve it. Experiments, reconstruction, replication and reproduction A lack of standards and tools for recording and reporting on archaeological experiments and analytics hinders comparison, scientific knowledge exchange and the evaluation of data quality. We are trying to fill this gap by developing a flexible, easy-to-use visual work-flow tool that will allow researchers to record meta- and paradata: experiment design plus protocols of sample preparation procedures and/or analysis. The need for such a tool has long been recognized, but resources have been lacking. The aim is to produce a prototype of a tool that can be continually improved in line with the needs of our community. About the Poster NFDI4Objects Community Meeting 2024, Mainz. Leibniz-Zentrum für Archäologie (LEIZA) Ivan Calandra, Geoff Carver, João Marreiros, Allard Mees, Guido Heinz, Florian Thiery Leibniz-Zentrum für Archäologie Citation Callandra, Carver, et al. (2024). Workflow Tool for Archaeological Experiments and Analytics. Zenodo. DOI: 10.5281/zenodo.13799052. 1) Monrepos 2) https://denkmalstiftung-baden-wuerttemberg.de/wp-content/uploads/2022/10/Denkmalstiftung-Baukunst_Pfahlbau.jpg 3) https://i.pinimg.com/736x/0c/a0/87/0ca087ca4ce96a5038eaf4608a7f4f2e.jpg 4) https://www.crystalking.com/hubfs/ancientrome.jpg 5) TraCEr List and reorder tasks Start menu Generate task flowchart Populate inventory Plan an experiment 1 2 3 4 5 https://tools.leiza.de/workflowtool/
Lost in scientiﬁc literature? Too busy to stay on top of your ﬁeld? Find inﬂuential research with a click of a button. Get to highly cited articles, follow updates on new publications, browse grant database to see what is funded in your ﬁeld. Too few or too many results, having to search in numerous places - literature research can be time-consuming. Europe PMC provides free access to biomedical peer-reviewed publications and oﬀers advanced search tools to help you navigate the world of scientiﬁc knowledge. Try Europe PMC for A-Z of life sciences literature: publications, patents, theses, and more. It combines the power of PubMed and PMC with a single search for both abstracts and full text. Even most speciﬁc requirements can be addressed with Europe PMC advanced search. Find clinical reviews, or search speciﬁcally for articles that mention CRISPR in the methods section. Hard to locate primary data and ﬁnd evidence? Want to re-use a dataset from a study? Discover links to data records directly from the full text. Europe PMC links to PDBe, UniProt, ChEMBL, and many more public molecular resources. Looking for evidence to support your hypothesis? Scan articles for concepts like chemicals or organisms, and spot protein interactions or gene-disease relations with an annotation tool. No time to gather the evidence of your impact? Claim your works to your unique ORCID identiﬁer with a simple tool from Europe PMC and enjoy immediate beneﬁts - an Author Proﬁle showcasing your publication list and citation rate. Find your way with Europe PMC! www.europepmc.org
How does this discipline interpret the question? What methods would this discipline use to investigate the question? How would (someone in) this discipline know they had a good answer? Helping Hands… What expertise/ methods/norms of thought can you draw on from other disciplines to support or contrast? Additional notes. 3 Building Permeable Classroom Walls: Forming links across the curriculum What is the misperception/ boundary/ epistemology you are trying to address? Can this be checked in another classroom? Will the session(s) be co-taught? Taught separately across subjects? Draw on knowledge from other subjects Building on Current Practice 5-minute lesson plan – bridging subject boundaries Developing Epistemic Insight: How can the students be supported to answer these three questions? What is the Big or Bridging Question? Learning Objectives: Before they investigate, what Do they already need to know? How does this map on to other areas of the school curriculum? Is this building on existing knowledge? Challenging misperceptions? Acting as extension? 1 2 How ‘Green’ are electric cars? Science: Identify how fuel production is sourced RE/Stewardship: Discuss who holds responsibility and who we are responsible to and why when mining resources and taking care of the environment. Geography: Investigate the location of where fuel resources are mined Identify previous learning/gaps in learning. Science: batteries, electrolysis, disposal, rare resources/recycling RE/Stewardship: The ethics of enforcing electric cars Geography: Location of resources Economics: Affordability, Accessibility (Market forces) Science: Establishing sources for electricity (fossil fuels/renewables) and how they are mined, pollution RE/Stewardship: Consider who benefits and who is disadvantaged from electric car production (inc sourcing materials). Awareness of demand on resources, production processes, politics, monopolise materials. Science: Undertake reliable tests, and reproducible experiments. Field studies. RE/Stewardship: Investigate the impact on quality of life (i.e. emissions, noise, global versus local perspective, impact on wildlife, rights of individuals, changes to/effects on societal behaviour Science: Reliable data checked against wider sources (misperceptions by car co’s). Replicable and tested by multiple sources. RE/Stewardship: Focus on judgements, beliefs and values in religious texts, ethical principles. Justifying actions from religious texts or worldviews. Geography – location of resources, damage to the environment, impact on local population. Geography – fieldwork, data analysis focused on people, places and spaces. Geography (physical and human) - data collection is both quantitative (mapping, surveys) and qualitative (interviews). Is this purely a science question? Are the environmental benefits of electric cars overstated? Collaborate with teachers of other disciplines. Learn from colleagues by talking about the same topic/big question and share their expertise and space to break down classroom boundaries. Co-planning/co-teaching with other subject specialist to work on the same topic/question from various disciplinary perspectives.
MICROBIAL AND ANTIMICROBIAL RESISTANCE GENE DIVERSITY IN EXTRACELLULAR AND TOTAL DNA ACROSS RURAL ECOSYSTEM BARRIERS IN EUROPE Olivença D1, Cabal A2, Tenson T3, Hassan M4, Manageiro V1, Dias E1, Rosado T1, Kõiv V3, Kisand V3, Rab G5, Jeremejeva J6, Telling K3, Arbo K3, Chambers M4, Laragione R4, Voit E7, Drahošová Z8, Kořínková M8, Woegerbauer M9, Ruppitsch W2, Caniça M1, de Menezes A10 1National Institute of Health Dr. Ricardo Jorge – Lisbon, Portugal; 2Institute for Medical Microbiology and Hygiene, AGES-Austrian Agency for Health and Food Safety, Vienna, Austria; 3University of Tartu, Estonia; 4University of Surrey, Guildford, United Kingdom; 5Vienna University of Technology, Vienna, Austria; 6Estonian University of Life Sciences, Estonia; 7Georgia Institute of Technology and Emory University, Atlanta, USA; 8The National Institute of Public Health, Prague, Czech Republic; 9 Division for Risk Assessment, Data and Statistics, AGES - Vienna, Austria; 10National University of Ireland Galway, Galway, Ireland Aim: To determine the microbial community composition and antimicrobial resistance genes (ARGs) diversity in total and extracellular DNA in agricultural ecological compartments in Europe. This work is part of the OHEJP FED-AMR consortium, which aims to investigate the role of free extracellular DNA (exDNA) in the spread of AMR genes across ecosystem barriers via bacterial transformation. Methods: Sampling was carried out in Austria, Portugal Czech Republic, Estonia, UK and Ireland. Both total (tDNA) and extracellular DNA (exDNA) were extracted along one crop-growing season, and sequencing of the 16S rRNA genes and ARG-capture coupled with metagenomic sequencing were performed. Results: The 16S and ARGs sequencing results were obtained for 511 samples, including 258 exDNA samples. Naturally- transformable bacteria, capable of incorporating exDNA into their cells, were present in the samples. Conclusions: Further analyses will link microbiome and ARGs with contaminants, antibiotics, heavy metals and trace elements to better understand the importance of exDNA as a vector for AMR spread in agricultural ecosystems. WILD ANIMALS (faeces) PIG BARN (faeces, waste) Manure CROP FEED (on site/imported SOIL (different crops, fertilization WATER (field drainage) FARMERS (swab samples, faeces) WATER (surface, river, irrigation) WASTEWATER TREATMENT PLANT WATER (drinking, ground) Figure 2. Ordination plot of microbial diversity. Ecological compartment influenced microbiome and ARG diversity. Figure 3. ARG composition of exDNA of wastewater sludge from Ireland. Sludge exDNA had a large number of ARGs, showing their potential for ARG spread in the environment Figure 1. Possible routes of ARG spread across ecosystem barriers and targeted ecological compartments in FED-AMR Acknowledgements: This work was supported by funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 773830: One Health European Joint Programme; FED-AMR project.
Introduction: The 2022 crisis has significantly changed European countries' approach to energy security and demonstrated the need to become independent from Russian energy imports. It has also revived the discussion on accelerating renewable energy changes to increase the share of green energy in the total energy balance of the associated countries. The main goal of the Research: primary analysis of selected conditions for energy security of European Union countries, in the context of turbulence on energy markets in Europe after the year 2022, using the non-parametric Wilcoxon signed-ranks test Application: desirability, possibility, and necessity of building an inter-state model, which can optimize energy flow management based on the principles of sustainable development, influencing the increase of energy security in Europe. Research hypothesis: The crisis of 2022 significantly changed the approach to optimizing energy flows Variables included in research: Total Energy Supply, which includes all the energy produced in or imported to a country, minus that which is exported or stored. It represents all the energy required to supply end users in the country. Energy trade balance, which is the difference between Energy import and export Change in energy balance in the European Union after 2022 dr Beata Szetela, dr hab. Grzegorz Mentel, prof. PRz – faculty of Management, Rzeszow University of Technology, Poland Prof. Najiba EL AMRANI EL IDRISSI, Faculty of Science and Technology, USMBA, Fez, Morocco Energy mix changes in UE between 2000 - 2022 Systematic decline in the amount of coal used Increasing role of energy obtained from renewable energy sources Reduction of the natural gas demand Increase in the total European Energy imports by 3% and decline of exports by 0,5% between 2021 and 2022 independence from energy supplies from Russia- Results In the case of coal, there are no significant changes in its share in the total energy mix, especially compared to the previous four years, when a systematic decline in the amount of coal used was observed. There are statistically significant differences in wind- and solar-based produced Energy, towards significant increases in renewable energy production There are significant differences in the amount of energy imported among developed and emerging European countries. In 2022, compared to 2021, the increase in imports among developed countries ranged from 2% in Belgium to 22% in Austria, while in developing countries the largest increase was recorded in Slovenia, where it amounted to 19%. All countries that recorded a decline in fuel imports in 2021 saw a significant increase in imports in 2022, which was undoubtedly due to the turmoil in energy markets caused by Russia's attack on Ukraine. The research leading to these results has received funding from the project titled "Cluster for innovative energy" in the frame of the program "HORIZON-MSCA-2022-SE-01" under the Grant agreement number 101129820 29.02-01.03. 2024 Szczecin V Edycja Międzynarodowej Konferencji Naukowo-technicznej "Energia Nowoczesnych Miast" https://energia.usz.edu.pl/
O projeto OSTrails visa contribuir para a melhoria dos processos e instrumentos de planeamento, monitorização e avaliação dos resultados de investigação cientíﬁca. A operacionalização dos seus objetivos vai decorrer trabalhando com vários parceiros nacionais e temáticos, com vista à otimização da infraestrutura existente e estabelecendo ligações com as partes envolvidas no ecossistema cientíﬁco europeu. Desaﬁos Abordagem Interoperabilidade insuﬁciente nas diferentes fases de investigação. Sistemas de informação académica não interoperáveis ou reduzida harmonização das melhores práticas adotadas. Incerteza quanto ao cumprimento dos princípios FAIR e das práticas de PGD. Mecanismos insuﬁcientes de monitorização. Diferentes níveis de maturidade de Ciência Aberta, FAIR e EOSC. Pilotos Para deﬁnir, criar, validar e avaliar as suas atividades e resultados, o projeto vai pilotar a adoção dos seus desenvolvimentos, contando com a colaboração de várias instituições, estando previstos 15 pilotos nacionais e 9 pilotos temáticos, entre as quais a Universidade do Minho e a Fundação para a Ciência e a Tecnologia. Contributos Reduzir as barreiras na adoção dos princípios FAIR nas diferentes fases de investigação; melhorar a avaliação da investigação através de um sistema de investigação interligado. Principais resultados Planeamento: melhorar a eﬁciência dos Planos de Gestão de Dados, transformando-os em recursos interligados e acionáveis por máquina. Monitorização: estabelecer um ecossistema aberto, interoperável e de elevada qualidade de sistemas de informação de conhecimento cientíﬁco, beneﬁciando da implementação dos princípios dos dados FAIR. Avaliação: disponibilizar ferramentas de avaliação dos princípios FAIR, com vista a obter métricas acionáveis por máquina integradas nas ferramentas que apoiam cada uma das fases do ciclo de vida de investigação. Face a estes desaﬁos, propõe-se a otimização do cumprimento dos princípios FAIR, interconectividade e automatização dos sistemas, nas fases de planeamento, monitorização e avaliação da investigação. Autores: André Vieira, Paula Moura, Pedro Príncipe, Antónia Correia Universidade do Minho Rumo ao planeamento, monitorização e avaliação dos resultados de investigação cientíﬁca
a) b) c) Filling proces: pressure profiles 0 50000 100000 150000 200000 250000 Mean Pressure (Pa) -24 -20 -16 -12 -8 -4 0 4 8 12 16 20 Time (s) Mean Pressure (Pa) Experimental (-) (2) Air (1) Premixed Air+Fuel P = 0,368 bar 0,184 bar CH4 + 0,184 bar Air up to target pressure Ptarget = 2,28 bar ζ Geometry of a combustion chamber Mesh Geometry simplification Air test simulation Initial and boundary conditions Sim. pressure profile Exp. pressure profile Inputfroman air test Dead volume adjustment Non-knocking combustion simulation Initial and boundary conditions Exp. pressure profile Sim. pressure profile ‘Combustion model calibration’ Knocking combustion simulation Initial and boundary conditions Self-ignition occurence Self-ignition occurence ‘Knock model calibration’ Dead volume: 70 cm of a pipe, closed 0 1e+06 2e+06 3e+06 4e+06 5e+06 6e+06 7e+06 8e+06 9e+06 1e+07 Mean Pressure (-) Experiment Simulation Dead volume: 20 cm of a pipe, closed 0 1e+06 2e+06 3e+06 4e+06 5e+06 6e+06 7e+06 8e+06 9e+06 1e+07 Mean Pressure (Pa) Simulation Experiment λ λ λ=0,52 EQR=1,924 λ=1,29 EQR=0,775 λ=1,08 EQR=0,928 5.5 mm/s Velocity: EQR/λ: λ Pressure profile: dead vol. adjusted based on Airtest 415 0 2e+06 4e+06 6e+06 8e+06 1e+07 1.2e+07 1.4e+07 1.6e+07 Mean Pressure (-) 500 525 550 575 600 625 650 675 700 Crank Angle (-) Experimental (-) Simulated, SF 1.4 (Pa) Simulated, SF default (-) Pressure profile: dead volume tuning... 0 2e+06 4e+06 6e+06 8e+06 1e+07 1.2e+07 1.4e+07 1.6e+07 Mean Pressure (-) 500 525 550 575 600 625 650 675 700 Crank Angle (-) Exp. (aligned) (-) dead_volume (-) real_dead_vol_adj_pos_all_t (-) ..._tuned_comb_model (-) real_dead_vol_all_time (-) λ λ λ Dead volume: 70 cm of a pipe, closed 0 1e+06 2e+06 3e+06 4e+06 5e+06 6e+06 7e+06 8e+06 9e+06 1e+07 Mean Pressure (-) 500 520 540 560 580 600 620 640 660 Crank Angle (s) Experiment Sim. adjusted piston profile Sim. org. piston profile Volume profile 0 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006 0.0007 0.0008 0.0009 0.001 Total Volume (m^3) 500 520 540 560 580 600 620 640 660 Crank Angle (deg) Total Volume (m^3) org. piston (m^3) Total Volume (m^3) adjusted piston (m^3) Pressure profile: dead volume tuning... 0 2e+06 4e+06 6e+06 8e+06 1e+07 1.2e+07 1.4e+07 1.6e+07 Mean Pressure (-) 500 525 550 575 600 625 650 675 700 Crank Angle (-) Exp. (aligned) (-) real_dead_vol_all_time (-) kf 0.85 (-) kf 0.75 + piston min 0.5 mm (-)