Which Of The Following Are Not Research Data

Research data is the backbone of evidence-based knowledge, driving innovation and progress across various fields. However, not everything that seems like data qualifies as research data. Understanding the nuances of what constitutes research data is crucial for maintaining the integrity of research, ensuring proper data management, and facilitating effective collaboration. This article will explore the concept of research data, delve into what does not qualify as research data, and provide examples to clarify these distinctions.

What is Research Data?

Research data encompasses a wide array of information collected, observed, or created for the purpose of analysis to produce original research results. According to the National Science Foundation (NSF), research data is defined as "recorded factual material commonly accepted in the scientific community as necessary to validate research findings." This definition highlights the importance of data validation and its role in supporting research conclusions.

Key Characteristics of Research Data:

Factual: Research data is based on empirical evidence, observations, or measurements.
Recorded: It is documented in a tangible form, whether physical or digital.
Used for Analysis: The primary purpose of research data is to be analyzed to answer research questions or test hypotheses.
Validates Findings: Research data is essential for verifying the accuracy and reliability of research outcomes.

What is NOT Research Data?

While the definition of research data is broad, there are certain types of information that do not fall under this category. These exclusions are important to recognize to avoid misclassifying data and to ensure appropriate data handling practices.

Here's a detailed look at what does not constitute research data:

Personal Information Unrelated to Research:
- Data that is strictly personal and does not contribute to the research objectives is not considered research data. This includes private emails, personal financial records, or health information of researchers or participants that are not relevant to the study.
- Example: A researcher's personal medical records, unless they are part of a study specifically examining health conditions, are not research data.
Administrative Data:
- Data collected for administrative purposes, such as human resources records, financial reports, or institutional data on student enrollment, are typically not classified as research data.
- Example: A university's records of student grades, unless used in an educational research study, are administrative data.
Scholarly Works Themselves:
- The final published articles, books, or reports are not considered research data. However, the underlying data used to generate these scholarly works is indeed research data.
- Example: A published journal article is not research data, but the survey responses or experimental measurements used to write the article are.
Physical Objects:
- Physical objects like laboratory equipment, specimens in a museum, or artifacts, while important to research, are not data themselves. However, the measurements, observations, and analyses derived from these objects are research data.
- Example: A fossil in a museum is not research data, but the carbon dating results and morphological measurements taken from the fossil are.
Preliminary Analyses and Drafts:
- Initial analyses, rough drafts of papers, and preliminary notes that have not been validated or finalized are generally not considered research data. These items are part of the research process but do not represent the validated findings.
- Example: A researcher's handwritten notes from an initial experiment setup are not research data until they are transcribed, organized, and used for formal analysis.
Software and Algorithms (In Some Contexts):
- While software and algorithms can be essential tools for analyzing data, they are not always considered research data in themselves. However, the output data generated by these tools, and the specific parameters or configurations used during research, can be research data.
- Example: The statistical software package used for analysis is not research data, but the dataset created and analyzed using that software is.
Data from Educational Assignments:
- Data collected by students for class assignments, such as surveys or experiments, are generally not considered formal research data unless they are part of a larger, well-defined research project with proper oversight and validation.
- Example: A student's survey responses collected for a psychology class project are not typically considered research data.
Clinical Records (When Not Part of a Study):
- Individual patient records in a hospital or clinic are typically not considered research data unless they are specifically used within a clinical research study with appropriate ethical approvals and informed consent.
- Example: A patient's medical history, unless part of a clinical trial, is not research data.
Literature Reviews:
- Summaries and reviews of existing literature, while valuable for contextualizing research, are not research data. The primary sources cited in the literature review, however, may contain research data.
- Example: A literature review on climate change is not research data, but the datasets and experimental results discussed in the reviewed articles are.

Detailed Examples and Scenarios

To further illustrate what does not qualify as research data, let's consider several detailed examples:

Scenario: Environmental Science Study
- Research Project: A study examining the impact of pollution on local river ecosystems.
- Research Data: Water samples analyzed for chemical pollutants, species counts of aquatic life, and GPS coordinates of sampling locations.
- What is NOT Research Data:
  - The researcher's personal emails discussing travel arrangements for fieldwork.
  - The purchase orders for laboratory equipment.
  - Preliminary, unvalidated notes on potential sampling sites.
  - Published articles on related topics used for background information.
Scenario: Social Science Survey
- Research Project: A survey investigating public attitudes towards renewable energy.
- Research Data: Responses to survey questions, demographic information of respondents (age, gender, location), and statistical analyses of the survey results.
- What is NOT Research Data:
  - The survey company's marketing materials used to recruit participants.
  - Internal memos discussing survey design modifications.
  - Individual contact information of survey participants (unless consent is obtained for research purposes).
  - A draft of the introductory paragraph in the survey report.
Scenario: Medical Clinical Trial
- Research Project: A clinical trial testing a new drug for treating hypertension.
- Research Data: Blood pressure measurements of patients, demographic information, and data on side effects.
- What is NOT Research Data:
  - Patients' complete medical histories (unless directly relevant to the study).
  - The hospital's financial records of trial expenses.
  - The personal opinions of doctors involved in the trial.
  - Earlier versions of the clinical trial protocol.
Scenario: Historical Research Project
- Research Project: An investigation of trade routes in the 18th century using historical documents.
- Research Data: Transcriptions of ship manifests, ledger entries from merchant accounts, maps of trade routes, and demographic information of traders.
- What is NOT Research Data:
  - The physical historical documents themselves (unless they are being analyzed as objects).
  - A historian's notes on their subjective impressions of the historical period.
  - Administrative records of the historical society where the documents are stored.
  - Published books about the historical period.
Scenario: Engineering Experiment
- Research Project: Testing the structural integrity of a new bridge design.
- Research Data: Measurements of stress and strain on bridge components, environmental data (temperature, wind speed), and simulations of bridge performance.
- What is NOT Research Data:
  - The CAD drawings of the bridge design.
  - The engineer's personal logbook with daily tasks.
  - Emails exchanging design ideas between engineers.
  - Vendor catalogs for construction materials.

Why Distinguishing Research Data Matters

Accurately identifying research data is essential for several reasons:

Data Management:
- Proper data management ensures that research data is organized, accessible, and preserved for future use. Misclassifying non-research data can clutter data repositories and make it difficult to locate relevant information.
Data Sharing and Collaboration:
- Clearly defining research data facilitates effective collaboration among researchers. It helps in determining what data should be shared and under what conditions, promoting transparency and reproducibility.
Data Security and Privacy:
- Research data often includes sensitive information about human subjects. Knowing what constitutes research data helps in implementing appropriate security measures to protect privacy and confidentiality.
Regulatory Compliance:
- Many funding agencies and institutions have specific requirements for managing and sharing research data. Understanding what qualifies as research data is essential for complying with these regulations.
Research Integrity:
- Distinguishing between research data and other types of information ensures that research findings are based on validated and reliable evidence. This supports the integrity and credibility of the research.

Practical Guidelines for Identifying Research Data

To help researchers and institutions better identify research data, here are some practical guidelines:

Consider the Purpose:
- Ask: Was the information collected, observed, or created specifically for the purpose of answering a research question or testing a hypothesis? If yes, it is likely research data.
Assess the Content:
- Determine whether the information is factual, empirical, or based on measurements and observations. Personal opinions, administrative records, and non-validated notes are usually not research data.
Evaluate the Context:
- Consider the context in which the information was generated. Data collected as part of a formal research project with ethical approvals and a clear methodology is more likely to be research data.
Check for Validation:
- Research data should be validated and verified before being used to draw conclusions. Preliminary analyses and drafts that have not been finalized are typically not considered research data.
Consult Guidelines:
- Refer to institutional policies, funding agency guidelines, and disciplinary standards for defining research data. These resources can provide valuable guidance and clarification.
Seek Expert Advice:
- When in doubt, consult with data management professionals, research librarians, or experienced researchers who can offer advice on identifying research data.

The Role of Data Management Plans

A data management plan (DMP) is a document that describes how research data will be handled during and after a research project. Creating a DMP is an excellent way to clarify what constitutes research data in a specific project. The DMP should address:

Data Types: Specify the types of data that will be collected or created during the research project.
Data Formats: Define the formats in which data will be stored (e.g., CSV, Excel, images, videos).
Metadata Standards: Describe the metadata standards that will be used to document the data.
Data Storage and Backup: Outline the procedures for storing and backing up the data.
Data Sharing and Access: Explain how the data will be shared and who will have access to it.
Data Preservation: Detail the plans for preserving the data for future use.

By carefully considering these elements in a DMP, researchers can establish a clear understanding of what constitutes research data in their project and ensure that it is managed appropriately.

Emerging Trends and Future Considerations

As research practices evolve, the definition of research data may also change. Emerging trends in data science, artificial intelligence, and big data are introducing new types of data and analytical methods. For example:

AI-Generated Data: Data created by artificial intelligence algorithms, such as synthetic datasets used for training machine learning models, may be considered research data if it is used to validate research findings.
Real-Time Data: Streaming data from sensors and monitoring devices, such as environmental sensors or wearable health monitors, presents unique challenges for data management and validation.
Linked Data: The integration of datasets from multiple sources through semantic web technologies is creating new opportunities for research, but also raises questions about data provenance and quality.

Researchers and institutions need to stay informed about these emerging trends and adapt their data management practices accordingly.

Conclusion

Understanding what constitutes research data is fundamental to maintaining the integrity, reproducibility, and impact of research. While research data encompasses a wide range of factual and validated information used for analysis and validation, it excludes personal information unrelated to research, administrative data, scholarly works themselves, physical objects, preliminary analyses, software, educational assignments, clinical records (when not part of a study), and literature reviews.

By adhering to the guidelines provided, researchers can ensure that they are appropriately managing their data, complying with regulatory requirements, and contributing to the advancement of knowledge in their respective fields. As research continues to evolve, a clear and adaptable understanding of research data will remain essential for fostering innovation and driving progress.