Which of the following is an example of how the Principle of Beneficence can be applied to a study employing human subjects? Determining that the study has a maximization of benefits and a minimization of risks. Ensuring that risks are reasonable in relationship to anticipated benefits.

Which example of research with prisoners would be allowable under the regulations? Examining age at first arrest as a predictor of adult criminal history.

Which of the following is a general consideration for IRBs reviewing big data research? Inducements constitute an “undue influence” if they alter a potential subject’s decision-making processes, such that they do not appropriately weigh the risk-benefit relationship of the research.

Respect for Persons, Beneficence, Justice. Which statement best describes information that must be included in a consent form: A description of the research’s potential benefits and risks.

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Which of the following is the main reason why proper training in euthanasia techniques is important? Improper techniques can cause unnecessary pain and distress in animals.

What is the most appropriate course of action for a trainee who has a problem with a mentor? After trying to communicate directly with the mentor, the trainee should seek advice from a senior person that the trainee trusts.

The most important factor in determining whether a research collaboration will be successful is: Whether there is ongoing communication among the team members about their goals and responsibilities. Which of the following statements is true regarding the regulations that govern research?

The Bayh-Dole Act, formerly known as the Patent and Trademark Act Amendments, is a federal law enacted in 1980 that enables universities, nonprofit research institutions and small businesses to own, patent and commercialize inventions developed under federally funded research programs within their organizations.

Data life cycle management (DLM) is a policy-based approach to managing the flow of an information system’s data throughout its life cycle: from creation and initial storage to the time when it becomes obsolete and is deleted. Hierarchical storage management (HSM) is one type of DLM product.

Benefits of Data Lifecycle Management for companies DLM allows you to meet the requirements applied by each industry sector so that data storage can be met. Availability of useful, clean and accurate data available to all users. Thus increasing the agility and efficiency of the company’s processes.

The data lifecycle represents all of the stages of data throughout its life from its creation for a study to its distribution and reuse. The data lifecycle begins with a researcher(s) developing a concept for a study; once a study concept is developed, data is then collected for that study.

Data Creation Data Acquisition: acquiring already existing data which has been produced outside the organisation. Data Entry: manual entry of new data by personnel within the organisation. Data Capture: capture of data generated by devices used in various processes in the organisation.

A data quality issue can be defined as a matter that causes the high quality of the data to be in dispute. Data quality is concerned with the accuracy and completeness of the data among other key factors, and it needs to be fit for its intended uses.

For example, if the data is collected from incongruous sources at varying times, it may not actually function as a good indicator for planning and decision-making. High-quality data is collected and analyzed using a strict set of guidelines that ensure consistency and accuracy.

Data quality issues can stem from duplicate data, unstructured data, incomplete data, different data formats, or the difficulty accessing the data. In this article, we will discuss the most common quality issues with data and how to overcome these.

No matter what the exact use case for your data, data quality is important because without it, the data can’t fulfill its intended purpose. Errors within a database of addresses would prevent you from using the data to reach customers effectively.

Here are four options to solve data quality issues:

1. Fix data in the source system. Often, data quality issues can be solved by cleaning up the original source.
2. Fix the source system to correct data issues.
3. Accept bad source data and fix issues during the ETL phase.
4. Apply precision identity/entity resolution.

Detecting data quality issues (this post)…Those common issues include:

1. Blanks.
2. Nulls.
3. Outliers.
4. Duplicates.
5. Extra spaces.
6. Misspellings.
7. Abbreviations and domain-specific variations.
8. Formula error codes.