Science Current Events | Science News | Brightsurf.com
 

Biologists merge methods, results from different disciplines to find new meaning in old data

January 12, 2010
Durham, NC - A growing number of scientists are merging methods and results from different disciplines to extract new meaning from old data, says a team of researchers in a recent issue of Evolution.

As science becomes increasingly specialized and focused on new data, however, researchers who want to analyze previous findings may have a hard time getting funding and institutional support, the authors say. In a commentary piece in the journal Evolution, the authors argue for removing cultural and technological barriers to this process.

"By putting together pieces of prior research, it is possible to transform how you do science and open the doors to findings that previously were unattainable," said Brian Sidlauskas, a former postdoctoral researcher at the National Evolutionary Synthesis Center and lead author on the article. "But such an approach runs counter to the way science traditionally has been conducted, so pursuing synthetic science is somewhat risky."

"We need to reduce the risk, remove the barriers, and encourage more pursuit of synthesis," said Sidlauskas, now a professor at Oregon State University. "The potential is staggering," he added.

Some of the most important research of the last quarter-century, the authors argue, has resulted from "synthetic science" -an approach which combines concepts, tools, and data from multiple disciplines to produce new insights or discoveries.

They cite the work of J. John Sepkoski Jr., who over a 20-year period compiled a database of more than 37,000 entries tracking the first and last appearance of different organisms in the fossil record. The entries, they write, "cut across taxa, time, and geography to reveal emergent patterns over more than 500 million years of life that could not be extracted from the component data in isolation."

"That database led to previously undetermined knowledge of five separate mass extinctions through time, understanding of how major geologic events can increase or reduce biodiversity, the realization that near-shore environments produce a disproportionately large share of evolutionary novelty, and other findings," Sidlauskas said. "It also spawned a new field of synthetic paleobiology."

Sepkoski's data aggregation is one of four methods of synthesis the authors say can transform science. The others, including examples, are:

* Conceptual synthesis: The emerging discipline of evolutionary medicine is one example of how linking concepts from two distinct fields can yield new ways to approach scientific problems. For example, a recent study linked an increase in asthma rates to immune responses that might originally have helped our ancestors fend off parasites.

* Integrating methods: Integrating approaches and analyses from two distinct fields - such as genetics and evolutionary biology - has led to new ways to use modern DNA sequences. For example, researchers can now look into the past to understand the origin of genomes and reconstruct how their structure has changed over millions of years.

* Re-use of results: The authors also review a pair of landmark studies that - after combining hundreds of previous results - found that climate change alters species' distribution, abundance and morphology. These synthetic studies gathered more than 2,300 citations in just five years and substantially informed the current United States government policy on climate change.

Despite the promise, there are a number of cultural barriers to pursuing this kind of science, the researchers say. For one, it is difficult for young scientists to find appropriate training. In addition, peer review and journal publication tend to emphasize the analysis of new data rather than old, they argue. Funding from state and federal agencies is more frequently directed toward more conventional approaches, not to mention the institutional challenges with job searches, promotion and tenure - all of which are geared toward more traditional science.

The technological barriers also are daunting, but offer tantalizing potential, Sidlauskas said.

"When you're looking to synthesize data from several hundred individual studies, data formatting, storage, and accessibility become huge issues," he said. "There has been a growing movement by funding agencies and journals to permanently archive all raw data and materials in some kind of standardized format so they are not lost over time and can be used by researchers of the future."

"It's kind of an open-source approach to science," he added. "Data archives may require some kind of proprietary protection for a few months or years, but after a certain amount of time, they should become public domain. Only by saving the data that underlie today's science will we allow future scientists to use those data in ways that may far exceed what the original researchers envisioned."

National Evolutionary Synthesis Center (NESCent)


Related Science Data Current Events and Science Data News Articles


New research will help forecast bad ozone days over the western US
New research published in Nature Communications led by Meiyun Lin of NOAA's Geophysical Fluid Dynamics Laboratory and NOAA's cooperative institute at Princeton University, reveals a strong connection between high ozone days in the western U.S. during late spring and La Niña, an ocean-atmosphere phenomena that affects global weather patterns.

Using microbial communities to assess environmental contamination
First there were canaries in coal mines, now there are microbes at nuclear waste sites, oil spills and other contaminated environments.

Commons Lab releases 2 new reports on key aspects of Citizen Science
Citizen Science is a rapidly growing set of techniques that harness the power of volunteers to assist and support a wide range of scientific research. But for citizen science to continue to grow - and be used in policymaking - practitioners need to consider key ethical, legal and social implications of these projects.

UA-led HiRISE camera spots long-lost space probe on Mars
The UK-led Beagle 2 Mars Lander, thought lost on Mars since 2003, has been found partially deployed on the surface of the planet, ending the mystery of what happened to the mission more than a decade ago.

Gemini Planet Imager produces stunning observations in its first year
Stunning exoplanet images and spectra from the first year of science operations with the Gemini Planet Imager (GPI) were featured today in a press conference at the 225th meeting of the American Astronomical Society (AAS) in Seattle, Washington.

BGRF to present new data at the second BDSM Congress in Oxford
The Biogerontology Research Foundation (BGRF), a UK-based charity founded to support ageing research and address the challenges of a rapidly ageing population, will present new economic longevity research at the second Big Data Science in Medicine congress in Oxford on December 8.

NASA's LRO Spacecraft Captures Images of LADEE's Impact Crater
NASA'S Lunar Reconnaissance Orbiter (LRO) spacecraft has spied a new crater on the lunar surface; one made from the impact of NASA's Lunar Atmosphere and Dust Environment Explorer (LADEE) mission.

NASA Spacecraft Provides New Information About Sun's Atmosphere
NASA's Interface Region Imaging Spectrograph (IRIS) has provided scientists with five new findings into how the sun's atmosphere, or corona, is heated far hotter than its surface, what causes the sun's constant outflow of particles called the solar wind, and what mechanisms accelerate particles that power solar flares.

NuSTAR Discovers Impossibly Bright Dead Star
Astronomers working with NASA's Nuclear Spectroscopic Telescope Array (NuSTAR), led by Caltech's Fiona Harrison, have found a pulsating dead star beaming with the energy of about 10 million suns.

CO2 emissions set to reach new 40 billion ton record high in 2014
Carbon dioxide emissions, the main contributor to global warming, are set to rise again in 2014 - reaching a record high of 40 billion tonnes.
More Science Data Current Events and Science Data News Articles

Data Science from Scratch: First Principles with Python

Data Science from Scratch: First Principles with Python
by Joel Grus (Author)


Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.Get a crash course in PythonLearn the basics of linear algebra,...

The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists

The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists
by Carl Shan (Author), William Chen (Author), Henry Wang (Author), Max Song (Author)


The Data Science Handbook contains candid interviews with 25 of the world’s best data scientists. We sat down with them, had in-depth conversations about their careers, personal stories, perspectives on data science and life advice. In The Data Science Handbook, you will find war stories from DJ Patil, US Chief Data Officer and one of the founders of the field. You’ll learn industry veterans such as Kevin Novak and Riley Newman, who head the data science teams at Uber and Airbnb respectively. You’ll also read about rising data scientists such as Clare Corthell, who crafted her own open source data science masters program. This book is perfect for aspiring or current data scientists to learn from the best. It’s a reference book packed full of strategies, suggestions and recipes...

Data Science Interviews Exposed

Data Science Interviews Exposed
by Yanping Huang (Author), Jane You (Author), Iris Wang (Author), Feng Cao (Author), Ian Gao (Author)


Data Science Interviews Exposed offers data science career advice and REAL interview questions to help you get the six-figures salary jobs! A data science job is extremely rewarding. It empowers to you make real impact in the world! And besides, it offers competitive salaries, and it develops your creative as well as quantitative skills. No wonder the data science job is rated as one of the sexist jobs in 21st century. So what you are waiting for ? Are you still wondering how to join data science work force ? Are you lost in the tremendous amount of online data science courses and resources ? Are you endlessly searching online to find data science interview questions and answers? If you answer yes for any of the questions, Data Science Interviews Exposed is a book you absolutely want...

Data Science for Business: What you need to know about data mining and data-analytic thinking

Data Science for Business: What you need to know about data mining and data-analytic thinking
by Foster Provost (Author), Tom Fawcett (Author)


Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think...

What Is Data Science?

What Is Data Science?
by O'Reilly Media


We've all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O'Reilly said that "data is the next Intel Inside." But what does that statement mean? Why do we suddenly care about statistics and about data? This report examines the many sides of data science -- the technologies, the companies and the unique skill sets.The web is full of "data-driven apps." Almost any e-commerce application is a data-driven application. There's a database behind a web front end, and middleware that talks to a number of other databases and data services (credit card processing companies, banks, and so on). But merely using data isn't really what we mean by "data science." A data application acquires its value from the data itself, and creates more data...

Python for Data Science For Dummies (For Dummies (Computers))

Python for Data Science For Dummies (For Dummies (Computers))
by John Paul Mueller (Author), Luca Massaron (Author)


Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a...

Data Science For Dummies

Data Science For Dummies
by Lillian Pierson (Author)


Discover how data science can help you gain in-depth insight into your business – the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles in organizations. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization’s massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you’ll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides...

Building Data Science Teams

Building Data Science Teams
by Radar


As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills, perspectives, tools and processes that position data science teams for success.

Topics include: What it means to be "data driven." The unique roles of data scientists. The four essential qualities of data scientists. Patil's first-hand experience building the LinkedIn data science team.

Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data

Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data
by EMC Education Services (Editor)


Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Corresponding data sets are...

Data Science at the Command Line: Facing the Future with Time-Tested Tools

Data Science at the Command Line: Facing the Future with Time-Tested Tools
by Jeroen Janssens (Author)


This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.To get you started—whether you’re on Windows, OS X, or Linux—author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools.Discover why the command line is an agile, scalable, and extensible technology. Even if you’re already comfortable processing data with, say, Python or R, you’ll greatly improve your data science workflow by also leveraging the power of the command line.Obtain data from websites, APIs, databases, and spreadsheetsPerform...

© 2015 BrightSurf.com