Complex Insight - Understanding our world
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Complex Insight  - Understanding our world
A few things the Symbol Research team are reading.  Complex Insight is curated by Phillip Trotter (www.linkedin.com/in/phillip-trotter) from Symbol Research
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PLOS Computational Biology: Ten Simple Rules for Creating a Good Data Management Plan

PLOS Computational Biology: Ten Simple Rules for Creating a Good Data Management Plan | Complex Insight  - Understanding our world | Scoop.it

"Research papers and data products are key outcomes of the science enterprise. Governmental, nongovernmental, and private foundation sponsors of research are increasingly recognizing the value of research data. As a result, most funders now require that sufficiently detailed data management plans be submitted as part of a research proposal. A data management plan (DMP) is a document that describes how you will treat your data during a project and what happens with the data after the project ends. Such plans typically cover all or portions of the data life cycle—from data discovery, collection, and organization (e.g., spreadsheets, databases), through quality assurance/quality control, documentation (e.g., data types, laboratory methods) and use of the data, to data preservation and sharing with others (e.g., data policies and dissemination approaches). "

Phillip Trotter's insight:

Good article - along with workflows - data management lifecycle and scientifiic workflow lifecycle management are critical in quality research and likely gowth areas in lifesciences tech for quite some time. Worth reading.

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GDELT: Global Database of Events, Language, and Tone

GDELT: Global Database of Events, Language, and Tone | Complex Insight  - Understanding our world | Scoop.it
GDELT: Global Database of Events, Language, and Tone
Phillip Trotter's insight:

The Global Database of Events, Language, and Tone (GDELT) is an initiative to construct a catalog of human societal-scale behavior and beliefs across all countries of the world over the last two centuries down to the city level globally, to make all of this data freely available for open research, and to provide daily updates to create the first "realtime social sciences earth observatory." Nearly a quarter-billion georeferenced events capture global behavior in more than 300 categories covering 1979 to present with daily updates. Data is available for data scientists to mine and analyze - See more at: http://gdelt.utdallas.edu/#sthash.04oK6DrA.dpuf

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Nate Silver addresses assembled statisticians at this year's JSM

Nate Silver addresses assembled statisticians at this year's JSM | Complex Insight  - Understanding our world | Scoop.it
by Joseph RIckert Nate Silver of 538 fame gave the President's invited address this year at the Joint Statistical Meeting in Montreal.
Phillip Trotter's insight:

Nat Silver's talk at the JSM focused on  the links between journalism and statistics by presenting 11 principles for journalists that apply equally well to business and data analysts. 

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DataGotham 2012 - Michael Flowers, City of New York

Michael P. Flowers is the Director of both New York City's Policy and Strategic Planning Analytics Team, which has been leading the charge on data analytics ...
Phillip Trotter's insight:

Good presentation on how New York City use data science and analytics to effectively allocate resources.

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Here’s how startups can access the wisdom of 70,000 data scientists

Here’s how startups can access the wisdom of 70,000 data scientists | Complex Insight  - Understanding our world | Scoop.it
Is your startup struggling to solve a tricky data challenge? Data science startup Kaggle is launching a program today to rally its community of 70,000 statisticians from around the world to solve y...
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My First Recommendation to New Scientific Coders: Learn Visualization

My First Recommendation to New Scientific Coders: Learn Visualization | Complex Insight  - Understanding our world | Scoop.it

Good article by Vince Buffalo on learning data visualization and tools to get strated with R.

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bioinformatics-databases

bioinformatics-databases | Complex Insight  - Understanding our world | Scoop.it
Bioinformatics - Databases
Phillip Trotter's insight:

Dr.Stefan Gruenwalds scoop-it list of bioinformatics databases is an essential resource for anyone looking to discover which datasets are available and accessible. Awesome.

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Robustness of skeletons and salient features in networks

Real world network datasets often contain a wealth of complex topological information. In the face of these data, researchers often employ methods to extract reduced networks containing the most important structures or pathways, sometimes known as `skeletons' or `backbones'. Numerous such methods have been developed. Yet data are often noisy or incomplete, with unknown numbers of missing or spurious links. Relatively little effort has gone into understanding how salient network extraction methods perform in the face of noisy or incomplete networks. We study this problem by comparing how the salient features extracted by two popular methods change when networks are perturbed, either by deleting nodes or links, or by randomly rewiring links. Our results indicate that simple, global statistics for skeletons can be accurately inferred even for noisy and incomplete network data, but it is crucial to have complete, reliable data to use the exact topologies of skeletons or backbones. These results also help us understand how skeletons respond to damage to the network itself, as in an attack scenario.

 

Robustness of skeletons and salient features in networks
Louis M. Shekhtman, James P. Bagrow, Dirk Brockmann

http://arxiv.org/abs/1309.3797


Via Complexity Digest, Eugene Ch'ng
Phillip Trotter's insight:

 Very relevent to some current work  we are doing on data modeling and data mining -  Awesome scoop  - big thanks Eugene and Complexity Digest..

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5 Free Excel Add-Ins to Help Digital Marketers Decipher Big Data

5 Free Excel Add-Ins to Help Digital Marketers Decipher Big Data | Complex Insight  - Understanding our world | Scoop.it
As the volume of data grows by the hour, take comfort in knowing \
Phillip Trotter's insight:

Great set of tools for Exel to support better data analysis.

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New Modeling Approach Transforms Imaging Technologies

New Modeling Approach Transforms Imaging Technologies | Complex Insight  - Understanding our world | Scoop.it
Researchers are improving the performance of technologies ranging from medical CT scanners to digital cameras using a system of models to extract specific information from huge collections of data and then reconstructing images like a jigsaw puzzle...
Phillip Trotter's insight:

Traditionally, imaging sensors and software are designed to detect and measure a particular property. The new approach does the inverse, collecting huge quantities of data and later culling specific information from this pool of information using specialized models and algorithms. The approach aims to capture as many measurements we possible with given sensors and then use models to extract and reconstruct features.  The new approach is called model-based iterative reconstruction, or MBIR and echoes how humans solve problems by trial and error, assessing probability and discarding extraneous data and is applicable to both scientific and consumer data processing problems such as image recognition. Great article and very interesting tech. Click on image or title to learn more.

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Designing Great Visualizations

Designing Great Visualizations | Complex Insight  - Understanding our world | Scoop.it

From the Eponymous Pickle (a great blog): Tableau provides a paper on data visualization. Requires Registration. " ... This paper traces the history of visual representation, from early cave drawings through the computer revolution. It also examines the different styles of data visuals, discusses some of the barriers to making effective visuals and the methods used to overcome those barriers. ... Read this paper to learn about: the importance of context in visualizations ... human perception's capabilities and limitations .... the power of using data to tell stories . Click on the image or title to learn more.

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Data scientists explained

Sometimes the people we work with are biologists, systems scientists, engineers, analysts, ecologists, economists etc. When ever we explain what we do the common pattern is one part data, one part data analysis, one part simulation, one part data visualization and one part subject matter expertise. All parts are essential in finding solutions to complex problems but sometimes i stumble explaining who uses our tools  and how come they are in lots of different professions.  DO we build tools for animators, biologists, engineers, analysts. system scientists? Well actually all of them -but to explain why eloquently, i should have simply asked IBM's James Kobielus James points out that all these folks are investigators and now have a new name - data scientists.  Short video and worth a watch.  

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