{"id":3349,"date":"2026-02-26T17:15:11","date_gmt":"2026-02-26T22:15:11","guid":{"rendered":"https:\/\/hemi.jhu.edu\/caimee\/?page_id=3349"},"modified":"2026-03-11T12:10:16","modified_gmt":"2026-03-11T16:10:16","slug":"data-handling","status":"publish","type":"page","link":"https:\/\/hemi.jhu.edu\/caimee\/center-facilities\/aimd-l\/data-handling\/","title":{"rendered":"Data Handling"},"content":{"rendered":"

[vc_row][vc_column width=”1\/6″][vc_btn title=”AIMD-L Overview” shape=”square” align=”center” css=”” button_block=”true” link=”url:https%3A%2F%2Fhemi.jhu.edu%2Fcaimee%2Fcenter-facilities%2Faimd-l%2F|title:AIMD-L%20Test%20Page”][\/vc_column][vc_column width=”1\/6″][vc_btn title=”MAXIMA” shape=”square” align=”center” css=”” button_block=”true” link=”url:https%3A%2F%2Fhemi.jhu.edu%2Fcaimee%2Fcenter-facilities%2Faimd-l%2Fmaxima%2F|title:MAXIMA%3A%20Multimodal%20Automated%20X-ray%20Investigation%20of%20Materials”][\/vc_column][vc_column width=”1\/6″][vc_btn title=”HELIX” shape=”square” align=”center” css=”” button_block=”true” link=”url:https%3A%2F%2Fhemi.jhu.edu%2Fcaimee%2Fcenter-facilities%2Faimd-l%2Fhelix%2F|title:HELIX%3A%20High-throughput%20Extreme%20Laser%20Impact%20eXperiments”][\/vc_column][vc_column width=”1\/6″][vc_btn title=”SPHINX” shape=”square” align=”center” css=”” button_block=”true” link=”url:https%3A%2F%2Fhemi.jhu.edu%2Fcaimee%2Fcenter-facilities%2Faimd-l%2Fsphinx%2F|title:SPHINX%3A%20Scanning%20Probe%20for%20High-resolution%20INdentation%20eXperiments”][\/vc_column][vc_column width=”1\/6″][vc_btn title=”Sample Handling” shape=”square” align=”center” css=”” button_block=”true” link=”url:https%3A%2F%2Fhemi.jhu.edu%2Fcaimee%2Fcenter-facilities%2Faimd-l%2Fsample-handling%2F|title:Sample%20Handling”][\/vc_column][vc_column width=”1\/6″][vc_btn title=”Data Handling” shape=”square” align=”center” i_icon_fontawesome=”fa fa-solid fa-star” css=”” button_block=”true” add_icon=”true” link=”url:%23|title:current%20page%20(Data%20Handling)”][\/vc_column][\/vc_row][vc_row][vc_column][vc_tta_tabs][vc_tta_section title=”Data Handling Overview” tab_id=”1772132395951-6e648c81-b5b5″][vc_column_text css=””]<\/p>\n

Data Handling Overview<\/h2>\n

[\/vc_column_text][vc_single_image image=”3295″ img_size=”full” css=””][vc_column_text css=””]Linked, high-quality metadata and data are the foundation for autonomous laboratory control and a central challenge in programmable cloud laboratories is automating this full data provenance. Data provenance and automated handling is also critical to training and deployment of AI\/ML models for materials discovery and optimization. AIMD-L has developed a seamless, event-driven system to automate integration of high-volume data from multiple instruments and diverse sample histories.<\/p>\n

AIMD-L integrates high-volume data from multiple instruments into linked datasets that are the foundation for autonomous control of the laboratory as well as training AI\/ML models for materials discovery and optimization.<\/p>\n

As shown in the figure above, the foundation of the AIMD-L data architecture is the\u00a0physical layer<\/em>: the instruments, robotics, and data center. At the top level is the\u00a0application and control layer<\/em>, where human and AI agents interact with the laboratory. These are linked by the\u00a0event data layer<\/em>, which is responsible for both data automation (via OpenMSIStream) and automation of instruments and sample handling (through an OPC UA server-client system).<\/p>\n

AIMD-L allows teams to design, run, and analyze complex experiments through secure network access and provides the flexibility to accommodate both human and agentic users as peers. Persistent identifiers (DOIs) are assigned to both projects and samples, creating a linked data model that includes metadata knowledge graphs to make all event data accessible and actionable. In AIMD-L, data streams are processed autonomously in real time, producing decision-ready results that trigger next steps and downstream lab events. The linked data model integrates scientific knowledge representation with data governance, providing full data provenance across sample histories and data lifecycle. This architecture is readily extensible to include work across multiple, interconnected, programmable laboratories.[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”Research Team” tab_id=”1772132478619-91e0fcbb-6180″][vc_column_text css=””]<\/p>\n

Research Team<\/h2>\n

Principal Investigator: David Elbert<\/p>\n

Additional Researchers: Kacper Kowalik* (Research Scientist), Akhila Ponugoti (Assistant Research Engineer), Ali Rachidi (Assistant Research Scientist), Matt Turk* (Associate Professor)<\/p>\n

*University of Illinois Urbana-Champaign[\/vc_column_text][\/vc_tta_section][\/vc_tta_tabs][\/vc_column][vc_column][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

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