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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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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
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CADRE: A Collaborative, Cloud-Based Solution for Big Bibliographic Data Research in Academic Libraries.

Patricia L Mabry1, Xiaoran Yan2, Valentin Pentchev2

  • 1HealthPartners Institute, Bloomington, MN, United States.

Frontiers in Big Data
|March 11, 2021
PubMed
Summary

Libraries and researchers face challenges with big bibliographic datasets. A proposed cloud-based platform, the Collaborative Archive and Data Research Environment (CADRE), offers a shared infrastructure to improve data access and support big data analytics.

Keywords:
bibliographic big databibliographic research resourcelibrariesopen accessplatform-as-a-servicereproducibility

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Area of Science:

  • Bibliometrics and Digital Humanities
  • Library Science and Information Management
  • Computational Science

Background:

  • Libraries face significant cost and expertise barriers to hosting and managing large bibliographic datasets.
  • Limited stewardship of big datasets by libraries hampers researchers' access to valuable information.
  • The increasing volume and complexity of bibliographic data necessitate new approaches for accessibility and analysis.

Purpose of the Study:

  • To outline the theoretical framework for the Collaborative Archive and Data Research Environment (CADRE) project.
  • To propose a shared, cloud-based infrastructure addressing the joint needs of libraries and researchers for big bibliographic datasets.
  • To identify and address potential barriers to implementing a collaborative data environment.

Main Methods:

  • Developing a theoretical framework for a shared cloud-based infrastructure.
  • Defining the CADRE project based on five pillars: Community, Access, Data-Centric, Reproducibility, and Empowerment.
  • Analyzing the challenges faced by academic libraries and researchers regarding big datasets.

Main Results:

  • A proposed solution, the Collaborative Archive and Data Research Environment (CADRE), built on five key pillars.
  • CADRE aims to provide accessible, affordable, and data-centric services for bibliographic data.
  • The framework emphasizes fostering reproducible research and empowering researchers with big data analytics capabilities.

Conclusions:

  • A shared cloud-based infrastructure like CADRE is essential to overcome current limitations in managing and accessing big bibliographic datasets.
  • Addressing barriers through community collaboration and strategic planning is crucial for successful implementation.
  • CADRE has the potential to revolutionize scientific research by enhancing access to and analysis of large-scale bibliographic data.