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Data Management and Compliance for ConductScience Behavioral Equipment

Learn More about our Services and how can we help you with your research!

Introduction

Our behavioral research equipment is designed to meet the stringent compliance and data management standards outlined by the NIH for rodent behavioral research. By integrating best practices for data handling, metadata documentation, and repository selection, our systems facilitate seamless research management and regulatory adherence.

NIH Compliance Overview

The NIH mandates comprehensive data management practices for all NIH-funded research to ensure that data is Findable, Accessible, Interoperable, and Reusable (FAIR). This ensures transparency, reproducibility, and maximizes the impact of research findings.

Key Requirements for Compliance:

  • Data Management Plans (DMPs): Detailed descriptions of how data will be stored, shared, and maintained.

  • Appropriate Repositories: Datasets should be stored in repositories that offer unique persistent identifiers and meet metadata, security, and accessibility standards.

  • Metadata Documentation: Datasets must be accompanied by detailed metadata to describe experimental conditions, variable definitions, and provenance.

 

How ConductScience-MazeEngineers Equipment Ensures Compliance

ConductScience’s behavioral equipment is built to support researchers in adhering to NIH’s standards by offering features that enable structured data collection, detailed documentation, and easy integration with compliant repositories. Below is a detailed breakdown of these capabilities:

1. Data and Metadata Documentation Requirements
Requirement Description Compliance Features in ConductScience Equipment
Study Title & Description
A concise title and description of the study including scope and experimental conditions.
Automated templates for study details with customizable fields.
Study Design & Protocols
Specify design type, number of subjects, and conditions (e.g., rodent strain, age, gender).
Protocol wizards for standardized data entry.
Data Collection Procedures
Methodology for behavioral tests (e.g., maze tests), hardware settings, software used.
Real-time recording and embedded protocol reference
Outcome Measures
Definitions for key measures (e.g., latency, frequency) and measurement units.
Integrated scoring and variable documentation.
Biosample Information
Information on biosamples collected and identifiers for each subject.
Biosample tracking integrated with subject records.
Data Provenance
Complete history of dataset modifications and data transformation steps.
Automatic logging and version control.
2. Supported Data Formats
Data Type Recommended Format ConductScience Export Options
Behavioral Logs
CSV, TSV, JSON
CSV, JSON, and XML output.
Video Recordings
MP4, AVI
High-definition MP4 exports.
Imaging Data
DICOM, TIFF
TIFF format for image captures.
Annotations and Metadata
XML, JSON
XML metadata generation.

Data Management and Compliance for ConductScience Behavioral Equipment

Our behavioral research equipment is designed to meet the stringent compliance and data management standards outlined by the NIH for rodent behavioral research. By integrating best practices for data handling, metadata documentation, and repository selection, our systems facilitate seamless research management and regulatory adherence.

How Research.ConductScience.com Supports Data Compliance

Our proprietary platform, Research.ConductScience.com, is designed to serve as a compliant repository for behavioral research data. Here’s how it aligns with NIH’s standards:

1. Data and Metadata Documentation Requirements
NIH Requirement Research.ConductScience.com Capabilities
Persistent Identifiers
Each dataset is assigned a DOI, ensuring traceability and citation.
Long-Term Data Storage
Secure cloud storage with redundancy and backup plans.
Metadata Support
Auto-generation of metadata schemas compatible with MIABEx and FAIR principles.
Security and Privacy Compliance
Implements role-based access controls, encrypted data storage, and controlled access for sensitive data.
Data Curation
Offers expert curation services to verify accuracy and integrity of datasets.
Broad Access
Public and controlled-access options available, in line with researcher needs.

Selecting a Repository for Behavioral Data

Researchers must select a repository that ensures data preservation and sharing according to the type of research. NIH suggests:

  1. Discipline-Specific Repositories: If available, select a repository dedicated to behavioral research or related fields (e.g., NIMH Data Archive).
  2. Generalist Repositories: Consider options like Dryad, Zenodo, or Figshare for broad data types.

 

Research.ConductScience.com: Ideal for rodent behavioral data, as it supports metadata standards like Minimum Information about a Behavioral Experiment (MIABEx) and integrates directly with our behavioral equipment for seamless data transfer​(NIH Grants)​(NIDDK).

Example Data and Metadata Standards Table

Data Type File Type Standards and Terminologies Example Repository
Behavioral Logs
CSV, TSV, JSON
Minimum Information about a Behavioral Experiment (MIABEx)
Research.ConductScience.com, NIMH Data Archive
Video Data
MP4, AVI
MPEG-4, ISO/IEC standards
Research.ConductScience.com, Figshare
Imaging Data
DICOM, TIFF
Minimum Information about Tissue Imaging (MITI)
Cell Image Library
Genomic Data
BAM, VCF
HUGO Gene Nomenclature Committee (HGNC)
dbGaP, NCBI Gene Expression Omnibus
Clinical Data
CSV, TSV
Clinical Data Interchange Standards Consortium (CDISC)
dbGaP, NIDDK Central Repository

Get Started with ConductScience’s Compliant Solutions

Whether you’re setting up a new rodent behavioral study or need to ensure compliance for ongoing projects, our platform and equipment offer the tools you need. Contact us to learn more about our compliance solutions and how Research.ConductScience.com can serve as your NIH-compliant data repository.

Author:

Shuhan He, MD

Shuhan He, MD is a dual-board certified physician with expertise in Emergency Medicine and Clinical Informatics. Dr. He works at the Laboratory of Computer Science, clinically in the Department of Emergency Medicine and Instructor of Medicine at Harvard Medical School. He serves as the Program Director of Healthcare Data Analytics at MGHIHP. Dr. He has interests at the intersection of acute care and computer science, utilizing algorithmic approaches to systems with a focus on large actionable data and Bayesian interpretation. Committed to making a positive impact in the field of healthcare through the use of cutting-edge technology and data analytics.