About Data Management
- Computer-Assisted ResearchThe possibilities for use of computer-assisted data management methods in research are numerous and ongoing with ever-expanding developments in computer technology and software design. Packages that perform statistical computations have long been available, and computer software to support the analysis of narrative text now is commonplace.
A software package is a collection of computer programs that work together to carry out a variety of designated functions. Some computer packages require a special site license. The best-known computer statistical packages for quantitative data analysis are the Statistical Analysis System (SAS) and the Statistical Package for the Social Sciences (SPSS). Others are BMDP, MINITAB (popular with statisticians for the teaching of statistics), and SYSTAT. Office spreadsheet programs, such as Microsoft Excel, have increased capabilities to do statistical analyses as well... - Data ManagementData management refers to operations in place for effectively and systematically collecting, storing, retrieving, and manipulating data. The concept is relevant for all researchers, since the need for a planned data management approach applies to any type of investigation. Design of the data management system should occur prior to actual data collection. Organizational strategies generally involve some combination of paper and computerized filing systems tailored to the needs of the particular research. Computer-assisted research software can serve many functions. However, software limitations and capabilities should not be allowed to direct the analysis. The researcher is responsible for choosing a data management strategy that facilitates conducting the research as planned.
- Institutional Research Data SystemsInstitutional research data systems is a broad term that aims to capture the ways and structures by which institutions—universities, research centres, and other higher education organisations— manage the large and increasingly complex amounts of information that their operations generate. These systems rely on research data technology, which refers to the concrete technical instruments, such as computing devices, databases, and software, by which research data are collected and organised. As computing power has increased, the kinds of data collected and the systems in which the data are organised have been greatly transformed. Thus, data technology is a central part and key enabler of research management systems.
The idea of collecting data—including sorting, coding, storing data, and transferring information within organisations—is a long-standing one. However, as organisations have become more knowledge focused and as the amount of data generated by research has increased, more attention has been given to the development of new concepts as well as practices regarding data management. The idea of managing data reveals that data systems and technologies are usually a means to an end, rather than ends in themselves. The better a data system is, the easier it should be for its users to collect, store, and retrieve data. This, in turn, should result in better and faster knowledge production.
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Perspectives
Making Numbers Count
A clear, practical, first-of-its-kind guide to communicating and understanding numbers and data--from bestselling business author Chip Heath. How much bigger is a billion than a million? Well, a million seconds is twelve days. A billion seconds is...thirty-two years. Understanding numbers is essential--but humans aren't built to understand them. Until very recently, most languages had no words for numbers greater than five--anything from six to infinity was known as "lots." While the numbers in our world have gotten increasingly complex, our brains are stuck in the past. How can we translate millions and billions and milliseconds and nanometers into things we can comprehend and use? Author Chip Heath has excelled at teaching others about making ideas stick and here, in Making Numbers Count, he outlines specific principles that reveal how to translate a number into our brain's language. This book is filled with examples of extreme number makeovers, vivid before-and-after examples that take a dry number and present it in a way that people click in and say "Wow, now I get it!" You will learn principles such as: -SIMPLE PERSPECTIVE CUES: researchers at Microsoft found that adding one simple comparison sentence doubled how accurately users estimated statistics like population and area of countries. -VIVIDNESS: get perspective on the size of a nucleus by imagining a bee in a cathedral, or a pea in a racetrack, which are easier to envision than "1/100,000th of the size of an atom." -CONVERT TO A PROCESS: capitalize on our intuitive sense of time (5 gigabytes of music storage turns into "2 months of commutes, without repeating a song"). -EMOTIONAL MEASURING STICKS: frame the number in a way that people already care about ("that medical protocol would save twice as many women as curing breast cancer"). Whether you're interested in global problems like climate change, running a tech firm or a farm, or just explaining how many Cokes you'd have to drink if you burned calories like a hummingbird, this book will help math-lovers and math-haters alike translate the numbers that animate our world--allowing us to bring more data, more naturally, into decisions in our schools, our workplaces, and our society.
Managing Your Research Data and Documentation
In the behavioral sciences today, there is increasing emphasis on transparency, and the need for research studies to be made replicable. This book presents a straightforward approach to managing and documenting one's data so that other researchers can repeat the study. While data management may seem intimidating to new researchers, this book shows how easy it can (and should!) be. The first chapter presents a basic structure of folders and subfolders for organizing data files, and then each subsequent chapter delves into details for a specific folder. Step by step, readers learn to label and archive different kinds of project documents and data files, including original, processed, and working data. Readers also learn to write command codes showing exactly how the original data are analyzed. Examples illustrate how to document the most common types of research (an online survey, a paper questionnaire, and a multiple-trial experiment). Since major research funders now require recipients to meet strict standards for data handling, this book will foster a vital career skill for students and promote transparency and replicability of research.
Data Visualization for Dummies
A straightforward, full-color guide to showcasing data so your audience can see what you mean, not just read about it Big data is big news! Every company, industry, not-for-profit, and government agency wants and needs to analyze and leverage datasets that can quickly become ponderously large. Data visualization software enables different industries to present information in ways that are memorable and relevant to their mission. This full-color guide introduces you to a variety of ways to handle and synthesize data in much more interesting ways than mere columns and rows of numbers. Learn meaningful ways to show trending and relationships, how to convey complex data in a clear, concise diagram, ways to create eye-catching visualizations, and much more! Effective data analysis involves learning how to synthesize data, especially big data, into a story and present that story in a way that resonates with the audience This full-color guide shows you how to analyze large amounts of data, communicate complex data in a meaningful way, and quickly slice data into various views Explains how to automate redundant reporting and analyses, create eye-catching visualizations, and use statistical graphics and thematic cartography Enables you to present vast amounts of data in ways that won't overwhelm your audience Part technical manual and part analytical guidebook, Data Visualization For Dummies is the perfect tool for transforming dull tables and charts into high-impact visuals your audience will notice...and remember.
Managing research data
Data management has become an essential requirement for information professionals. This title defines what is required to achieve a culture of effective data management offering advice on the skills required, legal and contractual obligations, strategies and management plans and the data management infrastructure of specialists and services.
The Cycle of Data Management
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Study what happens after you gather data. It must first be stored, then organized, integrated with data from other sources, and analyzed. Now you are ready to act on the information that the data provides. Determine how this cycle works in practice, and uncover some hidden pitfalls.