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Related Concept Videos

Archival Research01:40

Archival Research

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Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
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In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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Schemata01:17

Schemata

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A schema is a mental construct that organizes related concepts, allowing the brain to process information efficiently. Upon activation, schemata facilitate assumptions about people or objects.
Two types of schemata are:
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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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Describing Dataset Archetypes.

Leigh Dodds1

  • 1Open Data Institute, London N1 9AG, UK.

Patterns (New York, N.Y.)
|December 18, 2020
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Summary
This summary is machine-generated.

Applying the FAIR principles requires understanding data user needs and data characteristics. This opinion introduces ten dataset archetypes to guide data access, usage, and sharing plans.

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Area of Science:

  • Data Science
  • Information Science
  • Research Data Management

Background:

  • The Findable, Accessible, Interoperable, and Reusable (FAIR) principles are crucial for effective data sharing.
  • Successful implementation of FAIR principles necessitates a contextual approach, considering specific data types and user requirements.
  • Current data sharing strategies often lack the granularity to address diverse data characteristics and user needs.

Discussion:

  • This opinion proposes ten distinct dataset archetypes to facilitate contextual application of FAIR principles.
  • These archetypes serve as a framework for understanding the unique properties of different datasets.
  • Recognizing dataset characteristics is essential for tailoring data management and sharing strategies.

Key Insights:

  • Ten dataset archetypes are introduced to aid in the contextual application of FAIR principles.
  • Understanding user needs and data characteristics is paramount for effective data sharing.
  • Dataset archetypes provide a practical tool for planning data access, usage, and sharing.

Outlook:

  • The proposed dataset archetypes can inform the development of more nuanced and effective data management policies.
  • Adoption of these archetypes can enhance data discoverability and usability across scientific disciplines.
  • Future work should focus on validating and refining these archetypes based on real-world data sharing scenarios.