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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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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Selected Data About Geographic Locations01:25

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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort.

Michał Szczepanik1, Adina S Wagner2, Stephan Heunis2

  • 1Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany. m.szczepanik@fz-juelich.de.

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Summary
This summary is machine-generated.

This study presents a multi-modal teaching approach for research data management within the DataLad ecosystem. The free, open-source training materials aim to equip early career scientists with essential data management and software skills.

Keywords:
Online courseResearch data managementSoftware documentationTutorialVersion controlWorkshop

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Data Science

Background:

  • Research data management (RDM) is crucial in modern neuroscience but often absent from graduate curricula.
  • Community-driven initiatives are vital for training early career scientists in domain-agnostic RDM skills.
  • Effective user documentation and community interaction enhance software development quality.

Purpose of the Study:

  • To detail and evaluate a multi-modal teaching approach for RDM in the DataLad ecosystem.
  • To provide accessible training materials for both general RDM principles and specific software usage.
  • To support diverse learners through flexible and open-source educational resources.

Main Methods:

  • Development of a comprehensive RDM training program within the DataLad ecosystem.
  • Creation of a multi-modal resource including an online/printed handbook, a modular course, and a knowledge base.
  • Evaluation of the teaching approach's effectiveness over five years.

Main Results:

  • The DataLad ecosystem's RDM training materials have been successfully deployed.
  • The multi-modal approach caters to various learning preferences and delivery formats (in-person/virtual).
  • The initiative has provided valuable RDM and software training to a wide range of stakeholders.

Conclusions:

  • The developed multi-modal teaching approach effectively disseminates RDM and software skills within the DataLad ecosystem.
  • Free and open-source training materials are essential for bridging educational gaps in RDM for neuroscientists.
  • Continued community engagement and resource development are key to advancing RDM practices in scientific research.