fairdataihub/poster-sentry-training-data huggingface dataset:
A multi-robot platform to study the collective adaptive mechanisms in mixed societies F. Bonnet1, A. Gribovskiy1, L. Cazenille2, Y. Chemtob2, J. Halloy2 and F. Mondada1 1 Laboratory of Robotic Systems, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland 2 Laboratory of Tomorrows Energies, Université Paris Diderot, Paris, France Introduction This work was supported by the EU-ICT project ASSISIbf, No. 601074. AMAM2017 Framework Adaptation Multi-level control Future work Social animals are one of the most fascinating example of collective adaptive systems, for which complex collective behaviors can arise from social interaction that leads to self-organization of the group. In the past few years, we designed a system to investigate the fish collective behaviors, by having robotic agents inserted among the fish societies. • Different levels of controllers have been implemented in order to automatically control the robots so that they can reproduce the fish collective behavior, and also adapt to it. • A software running in a computer retrieves the position of the agents in real-time using visual tracking • Models of zebrafish shoaling based on the vision of zebrafish are implemented to generate fish-like trajectories for the mobile robots. • Control commands are then sent to the robots so that they reach these trajectories, using specific low-level controllers that mimic the locomotion of the fish. • Thanks to the framework set in place, the study of collective adaptive behavior can be investigated with a mixed group composed of several robots and animals. • Evolutionary algorithms and optimization methods can be used to produce appropriate decentralized controllers for the robotic agents, that adapt in real-time in experiments with mixed groups of animal and robots • This allows the robots to better adapt to the animals and better integrate fish shoals. The system designed allows the collective adaptation of behaviors for two different societies, an artificial one composed of robotic agents, and a group of animals. Thanks to the adaptive mechanisms, some parameters of the robots’ controllers can be automatically adapt according to the animals, which offers perspectives for learning more on the adaptive mechanisms in social animals. • Wheeled mobile robots are moving underneath a tank and steer fish-lures inside the tank using magnets • The lures are equipped with an actuated tail that can beat with different amplitudes and frequencies, so that they can send signals that are relevant for zebrafish social behaviors • The robots are controlled in a closed-loop using visual tracking, that retrieves the individual positions of the robots as well as the positions of the fish in real-time. • The wheeled mobile robots are continuously powered so that we can study the adaptive mechanisms of the agents during long duration experiments.
Motivation Inconsistent Flux in Different Sectors Conclusion TESS CROWDSAP Errors from White Dwarf Observations Pipeline vs Estimated CROWDSAP The graph compares pipeline CROWDSAP values to our estimates for white dwarf stars. We obtain our CROWDSAP estimates by recalibrating the light curves to achieve average fluxes that match the estimated TESS magnitudes from the TESS Input Catalog (TIC) [3]. Since white dwarfs should be constant in brightness on average, the estimates based on the TIC for these stars should be reliable (perhaps not for any contaminant stars). Nikoo Hosseininezhad1; Keaton J. Bell1; Swan Yi Htet1; Isabel L. Colman2 1 Queens College and the Graduate Center, City University of New York, New York, NY 2 American Museum of Natural History, New York, NY CROWDSAP is a metric within the TESS analysis pipeline, the fraction of light in the aperture estimated to come from the targeted star. This study specifically targets the TESS light curves of White Dwarfs observed across various sectors, with the goal of evaluating the uncertainties associated with CROWDSAP values. White dwarfs, characterized by their stable luminosity [1], theoretically should display uniform flux readings across observational sectors. TESS observations of WDs offer a unique opportunity to assess CROWDSAP errors. CROWDSAP inaccuracies will cause systematic errors in critical measurements such as transit depth and signal amplitude. The graph shows the flux measured by TESS in different sectors for WD 1856+534 (TIC 267574918, average CROWDSAP = 0.038) that was first discovered by [2] to have a giant plant transiting it. The average flux measured for this white dwarf is inconsistent in different sectors. The actual flux should be constant, so these differences represent errors in calibration of the TESS light curves. [1] Hermes, J.J., Gänsicke, B.T., Gentile Fusillo, N.P., et al. 2017, MNRAS, 468, 1946 [2] Vanderburg, A., Rappaport, S. A., Xu, S., et al. 2020, Nature, 585, 363 [3] Stassun, K.G., Oelkers, R.J., Paegert, M., et al. 2019, AJ, 158, 138 The differences between the pipeline and estimated CROWDSAP values give a sense of uncertainty of light curve calibrations. This is part of an ongoing effort to characterize sources on uncertainty in TESS data which includes modeling the distribution of flux from different sources in the TESS pixel data.
H5N1 Antibodies and Antigens for Research Applications Creative Diagnostics, a leading manufacturer and supplier of antibodies, antigens and assay kits, is excited to announce the launch of its new line of H5N1 Antibodies and Antigens to support cutting-edge research on this highly pathogenic influenza A virus subtype. These innovative products offer researchers and scientists invaluable tools for studying and combating the H5N1 influenza virus. Influenza A virus is a genus of virus in the family Orthomyxoviridae and causes influenza in birds and some mammals. Influenza A virus strains of all subtypes have been isolated from wild birds, but disease is rare. Some isolated influenza A viruses cause severe disease in poultry and, in rare cases, in humans. Sometimes the virus is transmitted from wild waterfowl to poultry, which can lead to outbreaks or cause human influenza pandemics. H5N1 is a subtype of influenza A virus and belongs to the genus Alpha influenza virus in the family Orthomyxoviridae. The genome of this RNA virus is segmented and consists of eight negative-sense single-stranded RNAs, abbreviated as PB2, PB1, PA, HA, NP, NA, MP, and NS. The major glycoproteins on the surface of the virus are hemagglutinin (H) and neuraminidase (N). H5N1 refers to the fact that the virus has a type 5 hemagglutinin protein and a type 1 neuraminidase protein. These proteins are antigenic when the virus infects host cells and are the primary targets of antibodies and antiviral drugs. Highly pathogenic avian influenza H5N1 viruses were first detected in domesticated waterfowl in southern China in 1996. Since 2020-2021, H5N1 2.3.4.4b clade viruses have become widespread in wild birds worldwide, leading to outbreaks in poultry and other animals. In the spring of 2024, H5N1 2.3.4.4b clade viruses have also been detected in milk samples from cows and in raw milk in several states in the United States, causing significant global concern due to their serious impact on poultry and wildlife populations and potential risk to human health. Influenza A viruses contain multiple subtypes and strains that frequently undergo genetic drift and transformation, posing a threat to human and animal health and life worldwide. Creative Diagnostics offers a wide range of high-quality antibody and antigen products related to the H5N1 influenza virus that are validated for use in a variety of assays, including Inactivated IAV H5N1 Native Antigens, Recombinant IAV H5N1 Antigens, and IAV H5N1 Antibodies. Creative Diagnostics is committed to providing researchers with the tools they need to drive scientific discovery. By offering a comprehensive portfolio of H5N1 Antibodies and Antigens, the company is supporting the global effort to develop effective strategies for preventing and treating H5N1 infections. To learn more about these antibodies and other research tools, please visit
Collections are Helping Avian Biomechanics Research Take Off Ms I. Szabo1, Dr V. B. Baliga2, Dr C. Harvey3, Ms J. Wong2, Ms F. Ciocca2, Dr D. L. Altshuler2 1University of British Columbia Beaty Biodiversity Museum, Vancouver, Canada, 2Department of Zoology, University of British Columbia, Vancouver, Canada, 3Department of Aerospace, University of Michigan, Ann Arbor, United States Museum collections have a role to play in biomimicry research. At the University of British Columbia Beaty Biodiversity Museum (BBM), the Cowan Tetrapod Collection is assisting members of the UBC Altshuler Lab by providing cadavers and post experiment, archiving a subset of the biological material used. Museums hold the necessary permit plus have frozen inventories not available elsewhere, enabling researchers to tap into a broad catalogue of biodiversity. For one of the studies highlighted, frozen avian cadavers representing 50% of the worlds bird orders were procured and supplied by the museum. The study of how animals fly is an intrinsically multidisciplinary field that involves aspects of anatomy, biomechanics, and evolutionary biology (among many others). Integrating laboratory experiments with evolutionary comparisons increases our understanding of how flight control evolved. Inspired by avian morphology, the search in on to design aircraft that can change the shape of its wings during flight. This ability, termed “morphing”, allows birds to fly in diverse conditions and shift between aerodynamic optima. Natural history collections, as the repositories of biological diversity and libraries of comparative morphology, are natural partners in this quest to understand how living organisms fly. By providing insights on avian global diversity and avian ecology, museum curatorial staff are invaluable informal advisors and mentors to physiology and engineering graduate students. Knowledge gained from working with natural history specimens and especially from unprepared cadavers is contributing to the next generation of aerial vehicles which may transport us out of this world.
Agrosystem models are increasingly important for decision-making support under global change. However, the application of valuable data from agricultural Long-Term Experiments (LTEs) in crop yield modeling faces several challenges: ⭙Data from the numerous global LTEs are rarely published ⭙Individual LTEs typically study only one or a few crop yield constraints ⭙Published LTE data often lack metadata and have heterogeneous quality Susanne Lachmuth (susanne.lachmuth@zalf.de); Carsten Hoffmann; Wahib Sahwan; Gunnar Lischeid (all Leibniz Centre for Agricultural Landscape Research, ZALF) partnering with ZB MED, UBN, TUM in UC 4 On behalf of the FAIRagro consortium Bridging Gaps in Long-Term Experiments (LTE): Data Integration and Plausibility Assessment for Advanced Yield Modeling Use Case 4 Objectives Action 1: Support and standardize new LTE data publication • FAIR-compliant metadata schema and guidelines (M 3.1) • Data publication schema for harmonized data management (M 3.1) • Publish LTE data in FAIRagro infrastructure (M.2.5, data steward support) Action 2: Data Curation for Published Incomplete LTE Data • Transform existing metadata to the new model (M 3.1) • Check completeness and update metadata (M.2.5) • Using web forms of the search service (M.4.3) Action 3: Implement Data Quality Assessment Service • Develop expert-based plausibility database (M.3.3) • Create approach based on statistical models • Implement feedback loop to enrich original datasets with new information (M.3.3) • Integrate with FAIRagro Portal ( M.4.1) Advanced Statistics for Data Integration and Quality Assessment Automated procedures for: • Providing qualitative contextual information beyond the published metadata (e.g., via text mining of LTE associated papers, UC2) • Quantitative imputation of (meta) data gaps • Stepwise integration of the updated but still incomplete data sets into crop yield modeling pipeline (UC6) • Plausibility and fitness-for-use assessment (M.3.3) Use Case 4 – 'Learning from incomplete data’ supports LTE data publication and quality. It automates context (meta)data integration and quality checks, bridging gaps between LTE data collection and reuse. Just started Oct 1st 2024 Icons from thenounproject.com 1st FAIRagro Plenary, 13.-15. November 2024 , Berlin
Towards a fully-automated site tie system at SARAO Hartebeesthoek RC Botha α, 1 & M Nickola β, 1 α 0000-0002-5767-1476; β 0000-0001-9526-4380 ¹ South African Radio Astronomy Observatory (SARAO) 10.5281/zenodo.10726248 P r o c e d u r e s • Link optical measurements to WGS84 utilizing a Reference Network of prisms which are co-axial with GNSS antennas • Total station positions determined via resection Total station positions in WGS84 • Measurements done by Total Stations are in WGS84 M e a s u r e m e n t s • Perform regular (semi- to fully- automated) measurements of optical targets in Reference and Network • For antenna axis and IVP, use a combined measurement schedule with: • pre-determined antenna pointing coordinates (antenna steer) • pre-determined prism positions (Leica GeoMoS) • Use IVP in local tie determinations Figure 1: A design for a co-axial optical and GNSS reference points (1) with a) the reference pillar and plate, b) the optional levelling plate, c) the GPH1P Prism Pillar and d) GNSS antenna. GNSS reference stations HRAX (2), ESA’s HRTB (3) and NASA HRAO (4), DLR’s HRAG (5) and the Roscosmos UARJ station (6). Figure 2: Dual prism setup (1) for co-axial mounting, and magnetic prisms (2). Co-axial prism on the M6 telescope (3), co-axial prism on the South-side of the 26m polar shaft (4). R e f e r e n c e N e t w o r k M e a s u r e m e n t N e t w o r k Presented on 4 March 2024 at: 1 2 4 5 6 3 1 2 2 4 3
New perspectives, breaking up traditions! Consultancy of Publication strategies in research proposals Career development of ECR Implementing research related policies Dr. Anneke Meyer Leibniz University Hannover Research and Innovation Services www.uni-hannover/forschungsservice.de @annekemeyer2 The Role of Research Managers in the Advocacy of Open Science Dr. Reingis Hauck Leibniz University Hannover Research and Innovation Services www.uni-hannover/forschungsservice.de @rsilvia77 Radical collaboration, breaking down silos! Building teams & networks with experts What is the right mix of knowledge on individual and team level? Activities of Research Managers with links to Open Science Advocacy of Open Science by building competencies & networks 2D Framework of knowledge Open Access level 3 No more distinguishing between professions but levels of competence! Librarian EC/ Horizon 2020 level 2 FAIR Data level 1 Federal Funding level 3 Academic Prizes level 3 Responsible Metrics level 1 Open Access level 3 German Research Foundation level 3 Research Manager Level 3 - Specialist knowledge Level 2 - Core knowledge Level 1 - Basic knowledge © Foto: Michael Matthey | LUH © Foto: Michael Matthey | LUH
Indalo isekelwe Iindlela zokujonga zineenzuzo ezilungileyo. Kodwa indlela ezihlukeneyo zikhokelela kwimpembelel o ezimbi. Isithintelo kulawu Isithintelo kulawu Isithintelo kulawulo “Xa besebenza bebengafumani mrholo. Khange bayigqibe ikontrakthi. Basebenza iinyanga ezintandathu kodwa behlawula imali elinganayo. Omnye osebenza inyanga enye.” Imiqobbo yentlalo yenkcubeko Imida yenkqubo yoosolusapho exhaphakileyo kubafazi. “... ndaqala ndaba yinkokeli yokucoca itywabasini kodwa ngenxa yokungahloniphi kwafuneka ndiyeke umsebenzi ukuze ndifumane izibonelelo ezithile kuphela.” Imithetho eluntwini okanye eyingosi Abazange bangene naluphi na ulwazi Imiqobo yolwazi “Kukho ukunqongophala konxibelelwano. Abantu balapha batyikitya isivumelwamo Kanye ngomnyaka. Bafuna ngakumbi kumbutho wamadlelo, hayi kanye ngonyaka. Ulwazi lunyanzelela intsebenziswano engaphezulu.” Ezoqoqosho kwezentlalo isithintelo Ubusela bemfuyo bubeka esichengeni iindlela zemfuyo kunye namadlelo. kukho Akukho ntengiso yempahla eveliswayo yokupjila, kwaye abaphumezi beprojekthi abakwazi ukuzithenga ezikuluntu. Ukungavisisani phakathi kwendlela yokuphila kunye neendlela zokuphila Indlela zijongelwa phantsi kukunqongophala ukuqonda nentlonipha kunye nolwazi olungangqineniyo luhamba kumanqanaba karhulumente. Abathengi okanye masiko nezithethe ngamnye amaxesha zingqubana neendlela zisekelwe kwindalo. Ulawulo olungacacanga nolungquzulanayo “Ngamanye amaxesha abanalo ulwazi, kwaye abafuni kubandakanya abantu basekuhlaleni. Ngoko ke abantu abathile abanokuthi baxhamle bakhutshelwa ngaphandle. Oko akulunganga. Bafuna uluntu olungundoqo luxhamle umqobo wolwazi.” Imiqobo yolwazi Unxibelelwano olunqabileyo nolulodwa “Sithendabuza uxanduva lokuphendula kwii NGOs njengoko kungekho mali yokuhlwawula ingcaphpephe zalapha ekhaya.” Ufikelelo lwenkxaso-mali oluthintelweyo kunye namaxesha angekho nqgiqweni Uluntu lohlukene njengoko abanye belandela umbutho wamadlelo kwaye Ezinye iinkokeli zemveli, kwaye kukhokelela kwiyantlukwano yolumntu. Ndingathanda ukubandakanyeka ngenxa yokukhethwa kweendawo zokuphumla kwaye usibonda akayonxal; enye zentlalo nekcubeko yombutho wamadlelo. Ukuntsonkotha kwezakhiwo zemveli Imiqobbo yentlalo yenkcubeko Ke silwenza njani ubulungisa kwiindlela ezisekelwe kwindalo kuluntu kunye nabachaphazelekayo kwi projekthi? Yeyiphi imiqobo ekuphumezeni ukulingana ngokwentlalo kwiindlela ezisekelwe kwindalo eMzimvubu? Funded by Sibulela amalungu oluntu aseMatatiele naseMaclear nabo bonke abachaphazekekayo kwiprojekthi kunye no Nosi Mtati and Phumula Nyembezi. By Glynis Humphrey, Tali Hoffman and Petra Holden · August 2024 glynis.humphrey@uct.ac.za Email: https://bit.ly/3BGj0vR Website: Izithintelo zamandla oluntu Ngokutsho kwamalungu olumntu angama 71 Ngokwabaphumezi beprojekthi bangama shumi amabini anesixhenxe
Core global metrics of chronic kidney disease (CKD) prevalence Global CKD prevalence Data: Global Burden of Disease (GBD) Study, 2019 Institute of Health Metrics and Evaluation Numbers on the top indicate global CKD prevalence. Comparisson of global prevalence for selected noncommunicable diseases Country-level CKD prevalence metrics Color filling accounts for three parameters: (1) all-ages and (2) age-standardised rates in 2019, and (3) age-standardised rate change since 1990. Before mapping each parameter was normalised to the scale from 0 to 100 (representing lowest and highest value of the original parameter). This allows to visualize all parameters and to identify the predominant one for each country. Scientific-Tools.Org Data analysis and visualization The creation of these visualizations and infographics has not been financed by any funds. You can donate at https://scientific-tools.org/support-us/ to support this and other visualizations Suggested citation: Bikbov B. (Scientific-Tools.Org). Core global metrics of chronic kidney disease (CKD) mortality in the GBD 2019 Study. Zenodo. DOI https://doi.org/10.5281/zenodo.8312897 1990 2000 2010 2019 341.7 mln 437.7 mln 559.3 mln 697.3 mln Chronic respiratory diseases Diabetes mellitus Neoplasms Cardiovascular diseases Chronic kidney disease Mental disorders 0 500 1000 Prevalence of selected diseases, in 2019, million persons Neoplasms Cardiovascular diseases Chronic respiratory diseases Mental disorders Diabetes mellitus Chronic kidney disease −20% 0% 20% 40% 60% nge in age−standardised prevalence rate between 1990 and 2019, % Data: GBD 2019 revision; Visualization: Scientific−Tools.Org Cha Data: GBD 2019 revision; Visualization: Scientific−Tools.Org
Environmental Data Initiative (EDI): enabling reproducible ecology and environmental science Information Management Training EcocomDP: • Community ecology researchers met and defined the structure of a dataset pattern that facilitates synthesis of community datasets. • EDI personnel write code to translate community observations into this pattern EDI offers several options for education about the five stages of data publishing (Organize, Clean, Describe, Upload, and Cite) • Webinars (available on YouTube) • On-site (EDI personnel go to a professor’s lab to teach multiple grad students how to archive data) • EDI Training Lab (above, a 2018 training workshop on the EML Assembly Line R tool for generating Ecological Metadata Language (EML)) Datasets and metadata are evaluated for quality and congruence before upload into the repository • Some quality checks result in a failure to upload: • Field delimiter is a single character • URL returns data • Some quality checks return a warning or information, but do not result in a failure to upload: • Geographic Coverage present • DateTime format in metadata matches data • Record delimiter is present K. Vanderbilt1, C. Gries2, M. Servilla1 , D. Costa1 , Susanne Grossman-Clarke2, P. Hanson2 , M. O'Brien3, C. Smith2, R. Waide1 1Biology, University of New Mexico, Albuquerque, NM, 2Center for Limnology, University of Wisconsin, Madison, WI, 3Marine Science Institute, University of California Santa Barbara, Santa Barbara, CA Overview The NSF-funded Environmental Data Initiative (EDI): • Offers data curation assistance to scientists affiliated with NSF-funded programs including: • Organization of Biological Field Stations (OBFS), • Long Term Research in Environmental Biology (LTREB), • Macrosystems Biology (MSB), and • Long Term Ecological Research (LTER), as well as • Individual environmental scientists without a viable data archive. Data Repository • Built on PASTA+ software • Contains high quality, well-documented datasets • Data are documented in Ecological Metadata Language (EML), a standard that can describe many types of data in great detail Data Synthesis Support Information Management Software Development • The EML Assembly Line, an R package, makes generating EML metadata a straightforward process • The user fills out several templates and runs the program to produce EML Outreach • The six EDI Summer 2018 Fellows learned information management practices from EDI personnel before heading to their host sites to conduct information management projects • EDI hosted a “hackathon” to begin populating the Information Management Code Repository (http://imcr.ontosoft.org/) • EDI supported the 2017 AGU Data Fair and co-organized the 2018 ESA Data Help Desk. Visit us at booth 722 in the Exhibition Hall! Summer 2018 EDI Fellows & Workshop Instructors portal.edirepository.org @EDIgotdata github.com/EDIorg https://environmentaldatainitiative.org/ AGU Data Fair
Constraining stellar cores in the main sequence mass-transition zone Benard Nsamba, Marion Asasira, and Ronald Ssembatya Core evolution of stars in the main-sequence transition zone How long does core overshoot sustain a convective core on the main sequence? Radiation Vs. Convection We implemented core overshoot using the exponential decay recipe with a diffusion coefficient described by Herwig (2000), Model with no overshoot Model with overshoot *** Depending on the core extension implemented via the overshoot parameter, the mass transition region varies from [1.02 – 1.14] Solar masses. We note that metallicity also impacts on the probability of models being convective along the main-sequence phase*** Dov = D0exp( z f . H p) , where f is the overshoot parameter, Hp is the pressure scale height, z is the distance from the boundary of the convective region, and Do is the diffusion coefficient of the unstable convective region. Convective core evolution of models in the mass transition zone *** Hydrogen profile and sound speed profile are the best indicators of a uniformly mixed core region***