A rubric used by teachers to monitor student progress and assess final products that incorporate data.
- Subject:
- Applied Science
- Mathematics
- Material Type:
- Assessment
- Date Added:
- 06/27/2017
This collection contains materials regarding open data, research data management, and other aligned open science practices.
A rubric used by teachers to monitor student progress and assess final products that incorporate data.
Data curation primers are peer-reviewed, living documents to provide practical and concise guides on curating a specific data type or format, or addressing a particular challenge in data curation work. All the primers are developed by Data Curation Network (DCN) which is a seed funding project from the Alfred P Sloan Foundation. The target audiences of primers are data curators and/or data librarians. To date, DCN has published more than 25 primers on database, Excel, netCDF, NVivo, R, SPSS, etc.
When entering data, common goals include creating data sets that are valid, have gone through an established process to ensure quality, are organized, and reusable. This lesson outlines best practices for creating data files. It will detail options for data entry and integration, and provide examples of processes used for data cleaning, organization and manipulation.
Nebraska Data Literacies key terms defined.
Data management planning is the starting point in the data life cycle. Creating a formal document that outlines what you will do with the data during and after the completion of research helps to ensure that the data is safe for current and future use. This lesson describes the benefits of a data management plan (DMP), outlines the components of a DMP, details tools for creating a DMP, provides NSF DMP information, and demonstrates the use of an example DMP.
The ESIP Federation, in cooperation with NOAA and the Data Conservancy, seeks to share the community's knowledge with scientists who increasingly need to be better data managers, as well as to support workforce development for new data management professionals. Over the next several years, the ESIP Federation expects to evolve training courses which seeks to improve the understanding of scientific data management among scientists, emerging scientists, and data professionals of all sorts.
All courses are available under a Creative Commons Attribution 3.0 license that allows you to share and adapt the work as long as you cite the work according to the citation provided. Please send feedback upon the courses to shortcourseeditors@esipfed.org.
The Data Management Skillbuilding Hub is a repository for open educational resources regarding data management, meaning that it is a collection of learning resources freely contributed by anyone willing to share them. Materials such as lessons, best practices, and videos, are stored in the DataONEorg GitHub repository as well as searchable through the Data Management Training Clearinghouse. We invite you submit your own educational resources so that the Data Management Skillbuilding Hub can remain an up-to-date and sustainable educational tool for all to benefit from. You can easily contribute learning materials to the Skillbuilding Hub via GitHub online.
Quality assurance and quality control are phrases used to describe activities that prevent errors from entering or staying in a data set. These activities ensure the quality of the data before it is collected, entered, or analyzed, as well as actively monitoring and maintaining the quality of data throughout the study. In this lesson, we define and provide examples of quality assurance, quality control, data contamination and types of errors that may be found in data sets. After completing this lesson, participants will be able to describe best practices in quality assurance and quality control and relate them to different phases of data collection and entry.
When first sharing research data, researchers often raise questions about the value, benefits, and mechanisms for sharing. Many stakeholders and interested parties, such as funding agencies, communities, other researchers, or members of the public may be interested in research, results and related data. This lesson addresses data sharing in the context of the data life cycle, the value of sharing data, concerns about sharing data, and methods and best practices for sharing data.
Some research funders have a mandate for data resulting from their funded research to be shared. This presentation provides a general definition of data sharing and how scholars can identify and follow data sharing mandates.
Data Tree is a free online course with all you need to know for research data management, along with ways to engage and share data with business, policymakers, media and the wider public. The self-paced training course will take 15 to 20 hours to complete in eight structured modules. The course is packed with video, quizzes and real-life examples of data management, along with valuable tips from experts in data management, data sharing and science communication. The training course materials will be available for structured learning, but also to dip into for immediate problem solving.
Data Tree is funded by the Natural Environment Research Council (NERC) through the National Productivity Investment Fund (NPIF), delivered by the Institute for Environmental Analytics and Stats4SD and supported by the Institute of Physics.
To know about what is data, information, table, field and Record
Those workshops help to gain new skills in research data management. Created by MIT Libraries, under CC-BY license, others can adapt and utilize this resources to develop thier own slides in teaching data management.
The ETD+ Virtual Workshop Series, taught by Dr. Katherine Skinner, is a set of free introductory training resources on crucial data curation and digital longevity techniques. Focusing on the Electronic Thesis and Dissertation (ETD) as a mile-marker in a student’s research trajectory, it provides in-time advice to students and faculty about avoiding common digital loss scenarios for the ETD and all of its affiliated files.
About the ETDplus Project
The ETDplus project is helping institutions ensure the longevity and availability of ETD research data and complex digital objects (e.g., software, multimedia files) that comprise an integral component of student theses and dissertations. The project was generously funded by the Institute of Museum and Library Services (IMLS) and led by the Educopia Institute, in collaboration with the NDLTD, HBCU Alliance, bepress, ProQuest, and the libraries of Carnegie Mellon, Colorado State, Indiana State, Morehouse, Oregon State, Penn State, Purdue, University of Louisville, University of Tennessee, the University of North Texas, and Virginia Tech.
Acknowledgements
This project was made possible in part by the Institute of Museum and Library Services.
In this course, the student will learn the theoretical and practical aspects of algorithms and Data Structures. The student will also learn to implement Data Structures and algorithms in C/C++, analyze those algorithms, and consider both their worst-case complexity and practical efficiency. Upon successful completion of this course, students will be able to: Identify elementary Data Structures using C/C++ programming languages; Analyze the importance and use of Abstract Data Types (ADTs); Design and implement elementary Data Structures such as arrays, trees, Stacks, Queues, and Hash Tables; Explain best, average, and worst-cases of an algorithm using Big-O notation; Describe the differences between the use of sequential and binary search algorithms. (Computer Science 201)
Effective Research Data Management (RDM) is a key component of research integrity and reproducible research, and its importance is increasingly emphasised by funding bodies, governments, and research institutions around the world. However, many researchers are unfamiliar with RDM best practices, and research support staff are faced with the difficult task of delivering support to researchers across different disciplines and career stages. What strategies can institutions use to solve these problems?
Engaging Researchers with Data Management is an invaluable collection of 24 case studies, drawn from institutions across the globe, that demonstrate clearly and practically how to engage the research community with RDM. These case studies together illustrate the variety of innovative strategies research institutions have developed to engage with their researchers about managing research data. Each study is presented concisely and clearly, highlighting the essential ingredients that led to its success and challenges encountered along the way. By interviewing key staff about their experiences and the organisational context, the authors of this book have created an essential resource for organisations looking to increase engagement with their research communities.
This handbook is a collaboration by research institutions, for research institutions. It aims not only to inspire and engage, but also to help drive cultural change towards better data management. It has been written for anyone interested in RDM, or simply, good research practice.
Students learn how to determine the authority of an information source. They examine different sources of information that all use the same dataset. Students define each source’s type of authority and recognize the context in which the data are being used. They learn to consider the source of authority for various information sources, and understand the ways that information sources with different levels of authority can base their credibility on the same dataset.
This lesson introduces undergraduates to personal digital archiving (PDA) as an instructional bridge to research data management.
PDA is the study of how people organize, maintain, use and share personal digital information in their daily lives. PDA skills closely parallel research data management skills, with the added benefit of being directly relevant to undergraduate students, most of whom manage complex personal digital content on a daily basis.
By teaching PDA, librarians encourage authentic learning experiences that immediately resonate with students' day-to-day activities. Teaching PDA builds a foundation of knowledge that not only helps students manage their personal digital materials, but can be translated into research data management skills that will enhance students' academic and professional careers.
List of exercises, presentation slides, and poster on research data management and scholarly communication topics by Chealsye Bowley.
This unit will explore the concepts of bias and confirmation bias and how they affect people's presentation and interpretation of data. It includes 5 days of lessons and independent work that culminate in students being able to show what bias and confirmation bias are and how they affect the way we interpret data.