fairdataihub/poster-sentry-training-data huggingface dataset:
Simple tool quantifying sediment resuspension effects on marine organic carbon storage A rapid and efficient solution for integrating sediment heterogeneity in resuspension and C storage assessments. Global trawling CO2 footprint estimations3 lack • measurements of resuspension effect on org. C mineralization rates4 • accountancy for spatial variation of sediment types/ habitats5 Seafloor plays pivotal role for Earth’s climate by storing organic carbon long-term.1 Resuspension enhances mineralisation of stored organic carbon to CO2.2 Ines Bartl Tegan Evans Jenny Hillman Simon Thrush Institute of Marine Science, The University of Auckland, Auckland 1142, New Zealand ines.bartl@auckland.ac.nz Acknowledgments: This research was funded by the University of Auckland, the George Mason Centre for the Environment, the Ministry of Business, Innovation and Employment (UOAX2307), and the Oceans of Change Project. We thank Stefano Schenone, Sam Ladewig, Simon Thomas, Alessandra Valim, Li Yeoh, Keshav Chandran, Gemma Cunnington, Eliana Ferreti, Caitlin Grosvenor, Paul Caiger, Andrew Reid, Sophie Thomson, and Alanta Loucks for their assistance during the field campaigns, with special thanks to Brady Doak for both his professional support and his hospitality during the cruise. References: 1 Bianchi, T. S., Cui, X., Blair, et al. (2018). Organic Geochemistry, 115, 138–155. https://doi.org/10.1016/J.ORGGEOCHEM.2017.09.008 2 Ståhlberg, C., Bastviken, D., Svensson, B. H., & Rahm, L. (2006). Estuarine, Coastal and Shelf Science, 70(1–2), 317–325. https://doi.org/10.1016/J.ECSS.2006.06.0224 3 Sala, E., Mayorga, J., Bradley, D., et al. (2021). Nature, 592. https://doi.org/10.1038/s41586-021-03371-z 4 Hiddink, J. G., van de Velde, S. J., McConnaughey, R. A., et al. (2023). Nature 2023 617:7960, 617(7960), E1–E2. https://doi.org/10.1038/s41586-023-06014-7 5 Epstein, G., Middelburg, J. J., Hawkins, J. P., et al. (2022). In Global Change Biology (Vol. 28, Issue 9, pp. 2875–2894). https://doi.org/10.1111/gcb.161056 6 Jørgensen, B. B., Wenzhöfer, F., Egger, M., & Glud, R. N. (2022).. Earth-Science Reviews, 228, 103987. https://doi.org/10.1016/J.EARSCIREV.2022.103987 7 Sawyer, C. N., McCarty, P. L., & Parkin, G. F. (2003). Chemistry for Environmental Engineering and Science. 5th (edn) McGraw-Hill Inc. New York, 1–752. The Resuspension Assay What’s next? Identifying vulnerability of Hauraki Gulf sediments to demersal fishing. Simple  based on BOD method7 Rapid  short incubations of 3-5 hrs Efficient  7-8 sites per day in 50 km radius Affordable  inexpensive equipment https://www.usgs.gov/media/images/conceptual-drawing-ocean-trawling
Urban Growth: Effects on urban green spaces and implications for planning and policy Marta Vallejo, MACS, Heriot-Watt University Supervisor: David W. Corne How to apply a Genetic Algorithm to a Sequential Planning Problem GREEN AREAS GENETIC ALGORITHM STATISTICAL DATA Metric: POPULATION SATISFACTION PROBLEM MODEL PROCEDURE RESULTS Agent-Based Modelling – Cellular Automata POLICIES & GUIDELINES Description: Cellular Automata model a city and its surroundings. Each cell has an ecological random value. The city growth has a negative effect on these values. The city is populated with a set of agents which represent families and are modelled by the use of an Agent-Based approach. E-mail: mv59@hw.ac.uk PGR Student Poster Day - 23rd August 2012 Objective: Distributing a set of green areas throughout the city and achieving the maximum satisfaction from the inhabitants of the city. An agent is satisficed if it lives close enough of one of these areas. GA should deal with future uncertainty: need the support of external tools (Statistical Data) Experiments still in progress. Preliminary data indicates a positive improvement in the satisfaction of the population using a GA-Statistical approach. Next steps: · Include new metrics like preserve most valuable ecological areas · Increase the complexity of the model to get closer to real-world scenarios · Validate the strategy followed by the comparison with other approaches commonly used for sequential planning problems like Reinforcement Learning.
Kelle Cruz (CUNY, NY, USA), William J. Cooper (U. of Hertfordshire, UK), David R. Rodriguez (STScI, MD, USA), Sherelyn Alejandro (CUNY, NY, USA), Ella Hort (Pomona College, CA, USA), Clémence Fontanive (U. of Bern, Switz.), Niall Whiteford (American Museum of Natural History, NY, USA) The SIMPLE Archive project — the Substellar and IMaged PLanet Explorer Archive of Complex Objects — is an endeavor to create a collaboratively maintained database of low-mass stars, brown dwarfs, and directly-imaged planetary mass objects using GitHub, Python, and SQL. Contribute The vision for SIMPLE is that anyone can contribute data using a typical GitHub workflow. The chart below illustrates the workflow from inserting new data to a local database instance via Python, review of database modifications in GitHub, and then publishing them to the website. Please join the #simple-db-dev channel in the Astropy Slack workspace for data contribution and development discussion. Develop •Work on local instance •Export to JSON •Commit to branch Review •Issue pull request •Run validation tests •Merge changes Publish •Run GitHub actions •Load DB from JSON •Deploy to website The SIMPLE Archive http://simple-bd-archive.org/ A collaboratively-curated database and website of low mass stars, brown dwarfs, and exoplanets Holdings The SIMPLE Archive currently contains over 3,000 sources, including directly imaged planets and UCDs with spectral types M7 and later. SIMPLE-AstroDB/SIMPLE-db/ SIMPLE-AstroDB/SIMPLE-web/ #simple-db-dev on astropy.slack.com/ http://joinslack.astropy.org/ Explore and Search • Website: search by object name, coordinates, or full text. • Python: SQL searches using astrodbkit2. This material is based upon work supported by the National Science Foundation under Grant No. AST-1909837. • DBBrowser: Browse data using a GUI and/or SQL. For these 3,000 sources, it contains: • 11,638 photometry measurements • 4,897 proper motion measurements • 2,628 parallax measurements • 1,404 spectra
https://zarr.dev zarr-developers @zarr_dev bioimaging distributed and cloud storage geospatial chunked storage allows parallelized access and computation data science built-in compression The Zarr specification and implementations are governed by an open process. Specification changes are suggested via Zarr enhancement proposals (ZEP). Those are reviewed and voted on by the Zarr Implementors Council (ZIC) and Zarr Steering Council (ZSC). https://zarr.dev/zeps Governance process Broad Compatibility, based on a Common Specification Funding provided by the Zarr is a Sponsored Project of Confocal imaging of mouse blastocysts Skin Layers Dermis and Epidermis Neuroglancer Napari webKnossos Atmospheric Conditions from the Coupled Model Intercomparison Project Phase 6 (CMIP6) Usage Examples Example of a Bioimaging Dataset Streamed with Zarr Chunking and Compression compatible with NEUROGLANCER INTERACTIVE NOTEBOOK https://bit.ly/3QsmI1M compatible with Chunked, Compressed N-Dimensional Arrays Josh Moore German BioImaging e.V. (NFDI4BIOIMAGE) @notjustmoore josh@openmicroscopy.org
Presenter: Dr Dawn C. Rose Parkinson’s is an incurable brain condition that affects people’s movement and mood. As the disease progresses, more care is required impacting on the caregivers quality of life. Research Question: Does participating in activities together improve quality of life for caregivers of people with Parkinson’s? Mixed methods: focus group (n=4), interviews (n=8) and survey (n=75, 16 male). Results 63% Parkinson’s caregivers jointly participated in a large variety of activities Impact seemed gendered: Male caregivers reported better levels of personal care, and less anxiety, depression and stress in general. Significantly higher levels of stress revealed for females not participating in activities with the person they care for. Type of activity did not seem to impact quality of life. Authors: Lia Prado (MSc.) Rebecca Hadley (PhD.) University of Hertfordshire, UK, Dawn C. Rose (PhD.) Lucerne University of Applied Sciences and Arts, Switzerland Acknowledgments: The Parkinson’s Advisory Team (University of Hertfordshire), and caregivers of people with Parkinson’s who took part in this study. Taking Time: Carer Participation and Quality of Life APA Ref: Prado, L., Hadley, R., & Rose, D. (2020). Taking time: a mixed methods study of Parkinson’s disease caregiver participation in activities in relation to their wellbeing. Parkinson’s Disease, 2020. https://doi.org/10.1155/2020/7370810 Reasons Against… Working Taking time for self Seeing other friends Prefers other activities Promoting independence (both) Reasons For… Exercise Socialising Together Time Not enough time Empathetic Safe Space Qualitative Evidence “I just need to think about myself a bit and not always be worrying, so now when she goes out I feel that I can just relax… So to just have a morning that I don’t have to think about that it is actually quite nice. It sounds very selfish.” “I think the interesting thing is that you lose a lot of friends, because actually they don’t understand it at all.” “We both go to Parkinson’s UK. I also committed to the support group because we got Parkinson’s. It is something that we both need to cope with and we need people who understand what we are going through” 0 20 40 60 80 Physical Social Home-based entertainment Daily acitvities Types of Activities and % Participation Male Carers Female Carers 0 20 40 60 Personal and Social Activities Anxiety and Depression Self-care Stress Factors of Wellbeing and % Quality of Life Compromised Male Carers Female Carers
Snip – User Centric Collaborative ELN Markus Osterhoff, Sebastian B. Mohr, Sarah Köster Georg-August-Universität Göttingen Röntgenphysik | CIDAS | SFB 1456 internet server analysis catalogue sample DB CONTROL meta data raw data experiment discussion users discussing with colleagues mobile access …! …? …!? …? conﬁg positions, parameters images, links data decisions feedback User Centric Paradigm – Documenting Non-Linear Discussions and Experiments Examples Creative, Collaborative Collage ► ►computer-generated contents (API) ► ►handcrafted annotations (pen entry) ► ►real-time communication (web based) Curated Lab Book ► ►putting the experimental state into context ► ►via API: parameters, images, URLs ► ►via pen & text: highlights, thoughts, sketches ► ►bringing machine-readable snips into perspective Closing the Loop ► ►Experiment ⇒ Thoughts ⇒ Decisions ⇒ Experiment Acknowledgements We thank Jan Goeman for outstanding technical support. Funding by Deutsche Forschungsgemeinschaft (SFB 1456), DAPHNE4NFDI and the Göttingen Campus Institute Data Science is gratefully acknowledged. API / Data Ingestion ► ►interoperable with other software: instruments send their state, users discuss and comment ► ►public JSON-endpoint accepts standardised snips: API tokens per book for authentication ► ►custom snip types: hierarchical namepaces prevent conflicts between groups Single / dual page layout Upload / place new snips Doodle tool Hyperlinks Permission system Creative collage JSON snip sci.photos/ QR/FDM24 snip. roentgen. physik.uni- goettingen. de Current Status & Outlook Client/server architecture ► ►React in TypeScript, serviced by node.js ► ►extensible and modular, custom snips and render functions In production @ GINIX X-ray microscope (DESY, Hamburg) ► ►macros send content to lab book ► ►integration into other beamlines under development In production @ IRP ► ►lab experiments, personal ELNs; shared experiments ► ►150 books, > 20k pages; ~ 30k snips, ~ 500k doodles Feature Status live update, working collaboratively yes free-form input; pen entry, sketches; images yes machine-readable data, data ingestion via API yes; namespaces and schemas permissions (r/w; API token; r/o token) yes; user-defined groups searching and filtering yes tagging system (table of contents, flags) under discussion hyperlinks to external systems yes federated login, SSO work in progress removing contents during grace period automatic creation of lab book could be implemented Snip types ► ►Basic types: image, text, doodle URLs linking to SciCat, Sample DB, PID services etc. ► ►Array: composite snips ► ►User defined types: timestamp, motor positions, macro, log, measurement, Jupyter analysis, … Book browser MariaDB page -, book -, user data, ... Render service Preview storage trigger server side rendering preview images signup pw reset Email service real time updates Websocket trigger updates static client ﬁles API requests fetch data
KNOW W HAT YOU EAT GET THE MOBILE APP Fish supply chain traceability at your ﬁngertips, for an informed and conscious choice. NOVAFOODIES GET THE APP SCAN QR CODE DISCOVER This project has received funding from the European Union under Grant Agreement N° 101084180. © All registered trademarks and logos mentioned in this document are the property of their respective owners. Co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reﬂect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
FILM SERIES “The Human Dimensions of Climate Change” Sponsored by: Fogler Library, the Climate Change Institute, Anthropology, Communication & Journalism & Political Science. The University of Maine does not discriminate on the grounds of race color, religion, sex, sexual orientation, including transgender status and gender expression, national origin, citizenship status, age, disability, genetic information or veteran’s status in employment, education, and all other programs and activities. The following person has been designated to handle inquiries regarding nondiscrimination policies: Director, Office of Equal Opportunity, 101 North Stevens Hall, 207.581.1226. Discussant Dr. Robert Glover Assistant Professor of Political Science & Honors, Cooperating Faculty Margaret Chase Smith Policy Center This Changes Everything MARCH 29 @ 6PM Library Classroom, Fogler Library Merchants of Doubt MARCH 22 @ 6PM Library Classroom, Fogler Library Discussant Dr. Laura Rickard Assistant Professor of Communication and Journalism Discussant Dr. Daniel Dixon Research Assistant Professor, Climate Change Institute UMaine Sustainability In The Path of Resistance APRIL 5 @ 6PM Library Classroom, Fogler Library
CODATA-RDA Escuelas de Ciencia de Datos para la Investigación Marcela Alfaro Córdoba, Louise Bezuidenhout, Raphael Cóbe, Sara El jadid, Bianca Peterson, Rob Quick, Hugh Shanahan, Shanmugasun- daram Venkataraman Objetivos y Alcance • Crear una cultura dentro de las instituciones de investigación de los países de ingresos bajos y medios, que se comprometa plenamente con la era de los Datos Digitales. • Escuelas regionales para enseñar habilidades fundamentales de Ciencia de Datos, diseñadas para crear comunidades de personas investigadoras que inician su carrera en países de ingresos bajos y medios, que tienen interés en practicar la Ciencia Abierta. • Plan de estudios distribuido en dos semanas de clases. • Egresados incluyen más de 500 estudiantes de más 45 países. • Planes para futuras sesiones de formación de formadores. Convocatoria abierta a ayudantes e instruc- tores para futuras escuelas regionales. Incorporación de la Ciencia Abierta • Evitamos dar recetas. • Preparamos para la realidad en casa. • Promovemos el debate reflexivo. • Proveemos ejercicios éticos integrados en cada sesión. • Mezclamos teoría con ejercicios prácticos. Currículo Incorporamos la práctica de la Ciencia Abierta en la formación en CienciadeDatos. Tome una fotografía para visitar nuestra página web Línea de Tiempo ↑ Julio 2016 • dataTrieste16 • ICTP Trieste, Italy Julio 2017 • dataTrieste17 • ICTP Trieste, Italy Diciembre 2017 • dataSaoPaulo17 • ICTP-SAIFR Sao Paulo, Brazil Julio 2018 • dataTrieste18 • ICTP Trieste, Italy Octubre 2018 • dataKigali18 • ICTP-EAIFR Kigali, Rwanda Diciembre 2018 • dataSaoPaulo18 • ICTP-SAIFR Sao Paulo, Brazil Junio 2019 • dataAddis19 • Addis Ababa University, Ethiopia Julio 2019 • dataTrieste19 and Data Stewards • ICTP Trieste, Italy Diciembre 2019 • dataSanJose19 • CeNAT San José, Costa Rica Enero 2020 • dataPretoria20 • University of Pretoria South Africa Julio 2020 • dataTrieste20 • alumni meeting Online Enero 2021 • dataPretoria21 • University of Pretoria Online Septiembre 2021 • dataTrieste21 • ICTP, Trieste Online Mayo 2022 • dataPretoriaSaoPaulo22 • Pretoria and Sao Paulo Online Julio 2022 • dataTrieste22 • ICTP Trieste, Italy •
www.dtls.nl • https//www.helisacademy.com/en • A three year project targeting the Life Science & Health sector • Aim: Improve skills for both new staff and existing staff (lifelong learning) on identified gaps, by developing tailored training • Helis themes identified: Good manufacturing practices (GMP), Clinical testing, Business development, Product & process design, Data Analysis & Stewardship • Contact: celia.van.gelder@dtls.nl • https://zenodo.org/communities/nl-ds-pd-ls/ • Aim: professionalise the data steward function within the life-sciences domain, with a special focus on implementation of the FAIR principles and endorsed by all stakeholders in NL • First deliverable: Life Science Data Steward Function Matrix v1.1, for 2 levels of Data Stewards and 9 knowledge areas: https://zenodo.org/record/2561723 • Upcoming: defining competences and building training • Contact: celia.van.gelder@dtls.nl FAIR Data Training and Community Building activities in the Netherlands Mateusz Kuzak, Christine Staiger and Celia W. G. van Gelder (DTL/ELIXIR-NL) Interoperability, FAIR data stewardship and training researchers and data experts are themes that are core to the remit of DTL and ELIXIR Netherlands. In line with these focal points, a significant part of our DTL Learning/ELIXIR- NL training activities can be categorized as “FAIR Data Training & Community Building”. They are set up with and by the DTL and ELIXIR-NL Community in the Netherlands, and in close collaboration with the DTL Data and DTL Technologies programmes. A number of highlights are shown here, more information can be found at https://www.dtls.nl/fair-data/fair-data-training/. Community Building – The Data Stewards Interest Group Defining the Data Steward profession & Curriculum Building • https://ds-wizard.org/ • The Data Stewardship Wizard (DSW) is a web-based application for preparing smart Data Management Plans. It connects to additional learning resources and helps raise awareness about DS challenges. It can be customised by Data Stewards or Funders to meet more specific needs. • Contact: rob.hooft@dtls.nl Training in Data Analysis & Stewardship • https://www.dtls.nl/community/interest-groups/data- stewards-interest-group/ • A community hub for Data Stewardship that enables informal and inclusive knowledge and experience exchange. • Ca. 90 members (NL, BE, LU, UK, SE, FR, DE) • Mission: • Providing a platform for Data Stewards and like-minded in the Netherlands (and abroad) to share the experience • Fostering the national implementation of Data Stewardship • Joining efforts to produce hands-on solutions • Contact: Jasmin Böhmer (chair), j.k.bohmer@umcutrecht.nl Contact Celia van Gelder Programme Manager DTL Learning/ELIXIR-NL Training, celia.van.gelder@dtls.nl Mateusz Kuzak Scientific Community Manager DTL, mateusz.kuzak@dtls.nl Christine Staiger Data Steward DTL, christine.staiger@dtls.nl The ELIXIR Data Stewardship Wizard
Scientific Knowledge Graphs and Metadata Infrastructure Daan Broeder, Menzo Windhouwer, Michael Kurzmeier, Katja Moilanen CLARIN ERIC, KNAW/HuC-DI, OAEW, TAU-FSD Scientific Knowledge Graphs Scientific Knowledge Graphs (SKGs) have recently gained popularity as a rich metadata structure for describing research activities using triples. The idea that a fully connected graph better represents the real world than a hierarchical structure is now widely accepted. Current implementations are the OpenAIRE Research Graph, PID Graph (DataCite), Open Research Knowledge Graph (TIB), … OpenAIRE Graph data model Data Cite PID Graph content From Scholarly to Scientific Knowledge Graphs? Within the RDA WG SKG Interoperability Framework, some existing SKGs have been developing a standardised model for SKGs. However mainly from a perspective of publishing and measuring research output for funders. To better model the research process, entities and relationships involving data creation and processing, such as processing services and instruments, are needed The research communities collaborating in the OSTrails project are advancing this work in the SKG-IF WG. Metadata Interoperability: SKGs to the rescue? In the OSTrails project the creation of SKGs by communities is an important means for improving the Discoverability and Interoperability of the community resources. From the SSHOC science cluster a number of CLARIN, DARIAH and CESSDA partners closely collaborate. The SSHOC partners are working towards creating SKGs from their rich metadata catalogues: - CESSDA data catalogue (CDC) - CLARIN Virtual Language Observatory (VLO) - SSH Open Marketplace (SSHOMP) Existing SKG-IF model SSHOC science cluster use-cases Retrieve information from SKGs : 1.When a user creates a new item in the SSHOMP, users enter the PID and fields for the new item are populated with the results. 2.When a user visits a dataset in the VLO see if the creator has also resources in the CESSDA SKG. Metadata catalogue Metadata Repository SKG publishing JSON / RDF / … OAI-PMH +? JSONAPI + SPARQL + .. Std. vocabularies community metadata providers OAI-PMH + ? Metadata catalogue Metadata Repository SKG publishing JSON / RDF / … OAI-PMH +? JSON API + SPARQL + .. Std. vocabularies community metadata providers OAI-PMH + ? SKG based Information exchange SKGs exchanging information