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Deadwood type and tree diversity structure saproxylic Hymenoptera host-parasitoid communities
This poster investigates the effects of deadwood type and tree diversity on deadwood cavity-nesting Hymenoptera host-parasitoid communities. The study was conducted in a large forest diversity experiment ("BEF-China"). The results show that deadwood position (standing or lying) is more important than forest tree diversity and stand structure in shaping the nesting communities. Lying deadwood supported less diverse and abundant communities that were largely a nested subset of those occupying standing deadwood while retaining approximately twice as much moisture, suggesting that substrate moisture is an important factor limiting bee and wasp communities nesting in deadwood. By contrast, neither ant exclusion nor observed ant occurrence was associated with host or parasitoid responses in our study.
Abridged Retinal Fluorescence Lifetimes in Glaucoma
This study investigates the relationship between glaucoma and retinal fluorescence lifetimes using FLIO. The results show that participants with glaucoma have shorter mean lifetimes compared to those without glaucoma, even after adjusting for age and diabetes status. The study suggests that glaucoma may be associated with altered metabolic activity in the retina.
Drupal as the platform of choice for Skolr
Science posters are a window into the state of knowledge and research at hundreds of universities and research laboratories. These posters are designed to highlight the latest achievements... years before these are published. The equivalent of centuries of work dumped into landfills without a trace each year. Skolr will add hundreds of thousands of S/E posters to the Poster Commons. The digital meeting poster service offers added value even before the meeting, as organization members can search and browse posters, and even subscribe to receive information when a poster matches their research interests.
Evaluating the Performance of LLMs in Drafting Data Management Plans
Most funders now require Data Management Plans (DMPs) to be submitted as part of a grant proposal. A DMP is a document that describes how researchers will manage, preserve, and share data from their projects in line with the Findable, Accessible, Interoperable, and Reusable (FAIR) principles and other relevant disciplinary guidelines. Its aim is to ensure researchers plan in advance for effective data management and sharing. Preparing a DMP can be difficult for researchers because they often lack training and knowledge in data management practices. Librarians are available to assist at larger institutions, but they face similar challenges and do not always have the resources to assist with all aspects of a DMP. Open-source platforms like DMPTool provide funder guidance, sample DMPs, and templates, but researchers and librarians still need to manually complete sections that vary based on domain and funder requirements.
Posters.science: Building Open Infrastructure for FAIR and AI-ready Scientific Posters
Posters are an important form of scientific communication, but the vast majority of knowledge from posters evaporates when conferences end. Posters.science aims to address the challenges of poster sharing and reuse by providing a single place to learn about why and how to share posters, share FAIR and AI-ready posters easily, and discover interoperable posters conveniently.
Standardizing Survey, Vision Care, and Cognitive Assessment Data Generalizable Framework for Mapping Research Questionnaires to the OMOP Common Data Model
The AIREADI project aims to generate high-quality, ethically sourced data from 4,000 participants of diverse backgrounds across the spectrum of type 2 diabetes mellitus. The dataset consists of 47 survey instruments that capture clinical information, behavioral elements, social determinants of health, and many other data points related to participants' health status and factors influencing their disease outcomes. The heterogeneity of data sources limits interoperability. We developed a reproducible framework for harmonizing survey instruments into the Observational Medical Outcomes Partnership (OMOP) Common Data Model for the AIREADI study.
Clinical Dataset Structure: A Universal Framework for Structuring Clinical Research Datasets
Each year, a rapidly growing volume of datasets is shared across the research community. In clinical studies, many data types are collected per participant, such as surveys, vital signs, eye images, and more. There is currently no consensus on how to organize multimodal data and include related information known as metadata. Standards like BIDS (Brain Imaging Data Structure) exist but only for structuring individual modalities. To address this challenge in the AI Ready and Exploratory Atlas for Diabetes Insights (AI-READI) project, we developed the Clinical Dataset Structure (CDS). The CDS provides a simple and intuitive way to organize clinical research data and metadata in line with the FAIR Principles. It recommends organizing multimodal clinical research datasets into one folder per data type, named as per a set convention, where applicable standard structure must then be followed within each folder.
PosterSentry: A Lightweight Multimodal Classifier for Scientific Poster Quality Assurance in Open Repositories
Scientific posters are one of the most common ways researchers share results, with millions presented every year. They hold rich, early findings, yet are rarely shared or reused afterward. The Posters.science project builds an index of FAIR (Findable, Accessible, Interoperable, Reusable) posters by pulling machine-actionable metadata from PDFs in open-access repositories. PosterSentry is a lightweight, open-source multimodal classifier that decides whether a PDF is a true scientific poster by combining three feature streams: static text embeddings, visual features, and structural cues. It is designed to be small and fast, with a 10 KB model head and about 60 MB embedding backbone, and can classify PDFs in under 1 second per file on CPU. PosterSentry is the cheap gate at the front of the pipeline, run before costly LLM-based metadata extraction downstream.
Pose Tracking for Automated Video-Oculography Using Handheld, Mobile Devices
To investigate Pixel3DMM and ARKit as candidate pose estimators for VOG using handheld, mobile devices, particularly in the presence of saccadic eye movement.