# Data Standardizing and Sharing > **NIH NIH P50** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $116,961 ## Abstract ABSTRACT Electrocorticography (ECoG) refers to recording from the surface of the brain. ECoG has been used for decades for select clinical purposes — most commonly to identify functional and epileptic brain areas in people with epilepsy — and occasionally for research. The important role of ECoG for basic research long been under-appreciated. Over the past several years, the unique qualities of ECoG have become widely and increasingly recognized by scientists engaged in basic and translational research. The primary sources of human ECoG data are patients with epilepsy patients, who receive subdural implants to map epileptic foci and function prior to invasive brain surgery. Thus, data collection is limited to the 125 class-IV epilepsy centers across the country that perform such surgeries, and to about 5-10 patients in each of these centers per year. Hence, larger-scale human ECoG studies and integration with animal ECoG studies, such as those proposed in this application, require that data are collected at multiple medical centers. However, each of them uses variable data collection protocols, data collection software, data collection hardware, data formats, and methods and procedures to keep track of meta information (e.g., the current cognitive status of the subject or other comments). These variations greatly impede, and in practice often prohibit the conduction of ECoGbased studies across multiple sites. Thus, achievement of the scientific goals of the proposed Center relies considerably on the effectiveness and efficiency of the mechanisms to address or mitigate these issues. The Data Management/Sharing (DMS) Core facilitates data collection, integration and sharing of human and animal electrocorticographic (ECoG) data across participating Center sites and projects. While ECoG data provide enormous opportunities for achieving the scientific goals of the Center, each of the five human ECoG sites (and the one monkey ECoG site) in the Center will provide data from only a few subjects each year, and there are important variabilities across subjects and technical setup at each of these sites. Thus, the overarching goal of the DMS Core is to remove or mitigate these variabilities so as to maximize the scientific value of the ECoG datasets produced by the Center and to minimize the difficulty in accessing them. In specific terms, AIM 1 is to provide support for implementation of experiments in standardized software; AIM 2 is to coordinate data collection at ECoG sites using standardized protocols; AIM 3 is to interface with those sites to aggregate data and to verify data integrity; and AIM 4 is to make data available to the Center's four scientific projects using robust and easy-to-use mechanisms. ## Key facts - **NIH application ID:** 9940908 - **Project number:** 5P50MH109429-04 - **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES - **Principal Investigator:** Alexandre R. Franco - **Activity code:** P50 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $116,961 - **Award type:** 5 - **Project period:** — → — ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9940908 ## Citation > US National Institutes of Health, RePORTER application 9940908, Data Standardizing and Sharing (5P50MH109429-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9940908. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*