The [Imageomics Institute](https://imageomics.org) is hosting a 3.5-day workshop to address the scarcity of ML-ready image and video datasets focused on addressing scientific questions. The event will bring together an interdisciplinary group around a shared interest of using AI/ML to extract scientific knowledge from image and video data, including ML researchers, domain scientists, information scientists, tool developers, and data curators. Participants will work in small groups to collaboratively curate or develop FAIR datasets, best practices, tools, infrastructure, and other products targeting the motivating challenge.
The event will take place August 14-17 at The Ohio State University in Columbus, OH. To apply to participate, please fill out the [Image Datapalooza 2023 Application for Participation](https://forms.gle/TJ5LtVzWWS4qKLTh8) by the end of June 12, 2023. Funds to assist with travel expenses are available but limited, as is space. We expect to notify applicants about acceptance starting 10 days after the application due date.
## About the event
Image Datapalooza 2023 will bring together an interdisciplinary group interested in using AI/ML to extract scientific knowledge from image and video data. We expect this to include AI/ML researchers, data scientists, domain scientists, data curators, tool developers, metadata researchers, and knowledge engineers. Participants will self-organize into small groups to work hands-on and collaboratively on self-selected targets and outcomes towards the motivations and goals of the event. The process of self-organizing and choosing work targets will be facilitated, but every participant will play an equally active role in making the event a successful, rewarding experience. We aim for an event that will give everyone ample opportunities to contribute their skills and experience, acquire new knowledge, increase technological awareness, and find potential new collaborators. Although the event is primarily designed to create work outcomes, the format will leave room for participant-driven exchange of know-how and skills.
ML-ready datasets are in high demand but frequently scarce, and this presents an important obstacle to advancing ML algorithms for scientific research applications. What makes datasets ML-ready will ultimately depend on the specific application, but will usually include that they are easy to obtain, unencumbered for reuse, interoperable with applicable ML data standards, and structurally compliant with expectations for ML competition. For many research domains, such as biology, large amounts of potentially relevant data is available. However, the accessibility of these data is highly variable, and processing the data for ML or analyzing the functionality or suitability of a dataset for ML can be costly.
We aim to facilitate outcomes that address the need for ML-ready domain image datasets, including (but not limited to!) the following:
* Best practices for collecting and annotating data for ML research, in particular image and video data for answering scientific questions.
* Tools and curation infrastructure for creating datasets following these best practices, including metadata and format standards.
* Tools to analyze and visualize basic properties and metrics of datasets to evaluate fitness-for-purpose for ML tasks and model training.
* [FAIR] / [CARE]-adhering and ML-ready datasets, including in particular image and video datasets suitable for ML competitions (e.g., on Kaggle).
* Tools and infrastructure for cataloging ML-ready datasets
We are keeping the scope of possible projects broad so as not to limit participants' ideas a priori. That notwithstanding, we expect the event to connect people with domain science-focused goals, such as biologists interested in datasets that help answer biological questions, to people with ML-focused goals, such as ML researchers interested in domain science questions for which to develop algorithms and models.
We generally expect datasets curated at or for the event, as well as tools or methods developed, to satisfy FAIR principles, and where applicable also CARE principles.
### Date and Location
The event will be held August 14-17, 2023, at the Imageomics Institute’s headquarters at The Ohio State University, Pomerene Hall, in Columbus, OH.
## Who should participate
We aim to bring together a diverse group of people, including AI and ML researchers and practitioners, as well as domain scientists, information scientists, tool developers, ontologists, and data curators. The event is particularly aimed at members of organizations in the US National Science Foundation (NSF) funded Harnessing the Data Revolution ([HDR]) ecosystem, including members of their respective computer science and domain science stakeholder communities. For example, for the Imageomics Institute, this includes computer scientists working on ML for images, text, and video, and biologists interested in using image and video data at large scale to answer trait-based biological questions.
In general, people encouraged to consider applying include (but are not limited to!) the following:
* AI/ML researchers, particularly those in computer vision (CV), interested in collaboratively advancing tools, infrastructure and skill sets for producing datasets that are both ML-ready and domain science-enabling.
* Data scientists interested in developing pipelines for large-scale dataset curation for scientific applications, or for facilitating data selection, for example by automating the process of understanding a dataset’s key features of interest to domain research.
* Interdisciplinary-minded domain scientists interested in communicating research questions that would significantly benefit from efficiently using ML for processing large datasets. For example, for biologists, these could include (but are not limited to) questions in trait evolution, macroevolution, and ecological variation.
* Programmers and research software engineers with skills requisite for AI/ML (Python and applicable libraries, etc), data wrangling/management (SQL, Pandas, R, Knowledge graphs, etc) and collaborative development (Git/GitHub) who are interested in developing tools and infrastructure
* Metadata researchers, ontologists, and other experts in applicable data, metadata, and vocabulary/ontology standards (COCO in CV; Darwin Core and Audubon Core in biodiversity; OBO ontologies for anatomy and traits; etc).
* Researchers with large image-based datasets who are interested in using ML to answer specific questions.
* Graduate students and postdocs looking for an opportunity to develop their skills in interdisciplinary research at the intersection of AI/ML and domain science, or are looking for collaborators to advance their projects.
* Advanced undergraduates in computational biology, computer science (ML / CV), math, or data analytics with demonstrated interest in interdisciplinary research
Everyone participating in the event must adhere to its [Code of Conduct](https://github.com/Imageomics/Image-Datapalooza-2023/blob/main/CODE_OF_CONDUCT.md).
## About the Imageomics Institute
The Imageomics Institute is an NSF-funded HDR Institute with the vision of creating a collaborative research, training, and community-facing environment for extracting known and discovering new biological traits from images, with the necessary infrastructure for cyber, information, and model development. The Institute will advance Imageomics-enabled biology, accelerate innovations in machine learning, and create digital resources for the researchers and practitioners in biology, data science, and machine learning, as well as the broader scientific community. It will further interdisciplinary training and education, and engage the broader public in the scientific process. Accomplishing these tasks will provide unique insights and enable biological discovery over a wide range of informative organismal attributes - some not yet comprehended or studied - and across multiple scales of biological organization from individuals to species
## Organizing Team
* [Elizabeth Campolongo](https://egrace479.github.io) (Ohio State University & Imageomics Institute)
* [Kelly Diamond](https://diamondkmg.github.io/) (Rhodes College)
* [Dom Jebbia](http://www.omnomdombomb.com/) (Carnegie Mellon University)
* [Hilmar Lapp](https://orcid.org/0000-0001-9107-0714) (Duke University & Imageomics Institute)
* [Jenna Kline](https://jennamk14.github.io) (Ohio State University)
* [Chuck Stewart](https://www.cs.rpi.edu/~stewart/) (Rensselaer Polytechnic Institute - RPI & Imageomics Institute)