Scientific Research Data Validation Survey Checklist

Ensure data integrity & research accuracy. Our Scientific Research Data Validation Survey Checklist guides you through rigorous verification, minimizing errors and maximizing the reliability of your findings. From initial collection to final analysis, maintain quality and build trust in your scientific work.

This Template was installed 2 times.

Data Source Verification

1 of 10

Confirm the integrity and origin of the data being validated.

Primary Data Source

Source Identifier (e.g., DOI, URL, Database ID)

Data Acquisition Date

Version Number (if applicable)

Notes on Source Reliability and Potential Biases

Data License/Usage Rights

Data Entry Accuracy

2 of 10

Assess the precision of data input into the system.

Verify Recorded Numerical Value

Check Recorded Text String against Original

Confirm Date Entry Accuracy

Validate Time Entry Precision

Confirm Selection from Defined Options

Count number of entries with errors

Unit Consistency

3 of 10

Ensure uniform usage of units of measurement throughout the dataset.

Primary Unit of Measurement (e.g., meters, kilograms)

If 'Other' specified above, what is the primary unit?

Confirm use of SI units where applicable?

Conversion Factor (if units were converted)

Describe any instances of inconsistent units and how they were addressed.

Range Validation

4 of 10

Check data points against expected ranges based on scientific principles.

Observed Value

Minimum Expected Value

Maximum Expected Value

Is Value Within Expected Range?

Justification for Range Selection

Notes on Value Deviation (if applicable)

Outlier Identification

5 of 10

Detect and investigate unusual data points that deviate significantly from the norm.

Observed Value

Expected Range (Lower Bound)

Expected Range (Upper Bound)

Potential Explanations for Outlier

Impact on Analysis?

Action Taken?

Supporting Documentation (e.g., raw data)

Metadata Completeness

6 of 10

Verify that all necessary metadata (author, date, instrument details, etc.) is recorded.

Principal Investigator Name

Project Title

Date of Data Collection

Sample ID

Data Collection Method

Instrument Model and Serial Number

Notes Regarding Data Collection

Calibration Certificates (if applicable)

Data Transformation Review

7 of 10

Evaluate any data transformations applied (e.g., normalization, calibration) for correctness.

Describe the data transformation applied (e.g., normalization, calibration).

Specify the transformation parameters (e.g., scaling factor, offset).

Was a standard transformation library or function used?

Upload the transformation script or code (if applicable).

Was the transformation reversible?

Quantify the error introduced by the transformation (e.g., mean squared error).

Software/Hardware Integrity

8 of 10

Confirm proper functioning of the software and hardware used for data collection and processing.

Software Version Number

Hardware Serial Number

Operating System

Last Calibration Date (Hardware)

Time of Last Software Restart

Software Licensing Status

Documentation and Traceability

9 of 10

Check for clear documentation of data validation procedures and ability to trace back to original sources.

Date of Initial Validation

Validator's Initials/ID

Detailed Description of Validation Process Followed

Version Number of Validation Protocol Used

Upload of Original Data Source File (e.g., raw data file)

Validation Method Used (e.g., manual, automated)

Related Project ID

Compliance with Protocols

10 of 10

Verify adherence to established data collection and validation protocols.

Protocol Version Adherence

Number of Revisions to Protocol

Date of Protocol Implementation

Deviations from Protocol (if any)

Training Record Verification

Protocol Documentation

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