在这个疾病网络中的数据中，疾病元素之间的关系的重要性与疾病本身一样重要。 未来会有更多信息融合到这个基础框架中，最终可以给我们呈现出更近距离的表现型与基因型特征。        - IsaacMao

交互浏览人类疾病之间的关系图：sigma 方式浏览  ；seadragon 方式浏览

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However, before we really understand the whole mechanism of Seuron Network, there’s still some questions that we have to tackle and investigate. The key first question is: How social brain will present? We can say social brain is the whole of Seuron Network, but still we need to see how this network will eventually evolve to. if “it” has super intelligence, will there be different connectome like today? or could spawn new type of formats beyond the Seuron Network?

If Human Beings differ themselves from other animal with their consciousness, Social Brain would have a higher level of presentations as well if we don’t mind call it “Social Consciousness”. Can a single Seuron sense that Social Consciousness, or simply never can? One possible direction is the Seuron Network can eventually evolve out some advanced functions, say new Seuron assemblies, adding to existing human Seurons. I would see they may be some data-sensitive algorithm-powered programs. They can run and send back signals to existing Seuron Network, which can give them better understanding of the whole network and understand itself, like human brain understand itself eventually(though not aware if a single neuron can understand the whole brain, afraid not with knowledge so far).

The structure of the universe and the laws that govern its growth may be  similar to the structure and growth of the human brain and other complex networks, such as the Internet or a social network of trust relationships between people.

So says Dmitri Krioukov, co-author of anopen access paper published by the Cooperative Association for Internet Data Analysis (CAIDA), based at the San Diego Supercomputer Center (SDSC) at the University of California, San Diego.

Simple mapping between the two surfaces representing the geometries of the universe and complex networks proves that their large-scale growth dynamics and structures are similar (credit: CAIDA/SDSC)

The paper argued that it’s not only similar on topology of structures between human brain, Internet, social networks, and cosmos, but also on the degree distribution of those non-direct-relevant complex systems.

That creativity defies scientific assumptions that the biosphere’s evolution and human activity can be reduced to physics and are fully governed by natural laws. Emergence, the theory of how complex systems self-organize into entities that are far more than the sum of their parts. Examples from the biosphere, neurobiology and economics redefines God as the fully natural, awesome, creativity that surrounds us is unlikely to convince those with a more traditional take on religion

习胡新常委发布会讲话全文标点可视化

文字来源：

• 习近平在常委见面会上的讲话(全文) : http://news.sina.com.cn/c/2012-11-15/121925587435.shtml
• 胡锦涛在新一届政治局常委与记者见面会上的讲话: http://news.sina.com.cn/o/2007-10-22/120512767987s.shtml

For anyone who design services or do research about online networks, the key is to access relevant data to eventually create insightful tools and views of it. Then when the number of services is increasing quickly, the job became tougher and tougher. Any changes or updates to one API service can break months of work, so one should be ready to follow the multiple updates from all sns. Obscure info posted on forum about API changes, new restrictions due to commercial or technical limitations, upgrade of the whole system, etc. It quickly becomes a real challenge to maintain everything and stay up-to-date with fast-growing services.

Though a few services can deliver a single endpoint for data from many services. The guys as Singly are doing an awesome job, maintaining 15+ APIs in a single place with an interesting paid system by “users” without data limitation. This project has been derived from Locker, an open-source piece of software that allow you to store locally all what you share on social networks and then build apps on it. Former Ubuntu CTO Matt Zimmerman has been involved closely in both projects.

But let’s say that you are not scared by having to deal with all those different sources of data, you will definitely need some way to harmonize the JSON mess : any vocabulary on any platforms may vary. There should be some secret correspondence between “like”, “RT” and “share”  though. So maybe an unified format to classify all social media networks will be a good idea.

Guys at Tawlk has came up with the USMF (Unified Social Media Format) which is very clever. Here is a sample :

    {
        "service" : "",           # Service Name.
        "user" : {                # User Info 
            "name" : "",              # User Name
            "real_name" : ""          # Real name of user
            "id" : "",                # Unique User ID
            "language": "",           # Spoken language of user
            "utc" : "",               # UTC time offset of user
            "geo" : "",               # Latitude/Logitude User location
            "description" : ""        # User profile description
            "avatar" : "",            # Direct href to avatar image
            "location": "",           # Plain Language User location
            "subscribers": "",        # Number of subscribers
            "subscriptions": "",      # Number of subscriptions
            "postings": "",           # Number of postings made
            "profile": "",            # Href to user profile
            "website": "",            # Href to user website
        }
        "to_users" {              # Attached link(s)
           "0": {                     # Index of link
              "name" : "",                # User Name
              "id" : "",                  # Unique User ID
              "service"                   # Name of service/Domain hosting link 
              "title" : "",               # Title of item
              "thumbnail" : "",           # Direct href to thumbnail for item
              "href" : "",                # Direct href to item
           },
        },       
        "links" {                 # Attached link(s)
           "0": {                     # Index of link
              "service"                   # Name of service/Domain hosting link 
              "title" : "",               # Title of item
              "thumbnail" : "",           # Direct href to thumbnail for item
              "href" : "",                # Direct href to item
           },
        },       
        "id" : "",                # Unique ID
        "geo" : "",               # Latitude/Logitude content creation location
        "application" : "",       # Application used to create this posting
        "location" : "",          # Plain Language content creation location
        "date" : "",              # Date posted
        "source" : "",            # User friendly link to content
        "text" : "",              # Microblog text / Video Title / Etc
        "description" : "",       # Full post text / Decription 
        "keywords" : "",          # Related Keywords
        "category" : "",          # Category of content
        "duration" : "",          # Duration of content (if video)
        "likes" : "",             # Number of users who "liked" this
        "dislikes" : "",          # Number of users who "disliked" this
        "favorites": "",          # Number of users who "favorited" this
        "comments": "",           # Number of users who "commented" this
        "rates": "",              # Number of users who "rated" this
        "rating": "",             # Average "rating" of content
        "min_rating": "",         # Minimum "rating" of content
        "max_rating": "",         # Maximum "rating" of content
    }

ShareVIZ 分享数据之美

刘勇  （@ooof ）
2012年3月，刘勇先生主讲的MOOC（大规模在线开放课程）“Gephi 中文教程”，发布在Udemy 平台，到2012年10月底吸引超过800人参加；NGO2.0 项目成员；MIT 新媒体行动实验室研究员

数据可视化是新兴的一种信息呈现技术，它以图景的形式来替换众多目不易测的数据，使人们有可能从纷繁复杂的信息中发现线索进而探寻趋势。

那么，如果用数据可视化的方式去再整理那些古老而神秘的经文会展示出一种什么样的新情景呢？

正好在阅读后秦鸠摩罗什所翻译的佛教经典《金刚经》，就想到也许可以尝试对经文做可视化处理，看能否增进理解或发现一些通常情况下不易觉察的现象，当然这也算是对可视化实用性的一个检测。

金刚经主要的情节是佛答须菩提问的几个对话回合，做分词处理后，又对佛与须菩提各自所用到的词汇做了一个区分展示出来，见下图。

说明：

1.这个图，去掉了经文中类似“谁说”、“谁对谁说”的对话关系说明，实际只包括四个词汇，即：“须菩提言”、“须菩提白佛言”、“佛言”、“佛告须菩提”。

2. 左侧由须菩提画出的线连接到须菩提所用到的词汇，右侧由佛画出的线连接到佛所用到的词汇，最上面“其它”画出的线连接的是交代经中对话场景所用到的词汇，这三者之间所用的词汇也互有交叉。

读图：

如果不细究须菩提、佛、其它三者所连接词汇内在的含义，仅从线条关系去观察，至少会得出以下描述：

1.图中“其它”连接词汇所占比例很少，而主要是须菩提与佛对话连接的词汇，可知：经文中对话背景描述很少，而大篇幅的都是佛与须菩提对话的内容；

2.从图中，佛比须菩提所连接的词汇较多，可知，佛说的比须菩提多，其实这也很容易理解，因为毕竟是须菩提请教，而佛在阐明法，所以答比问的要多。此文也与一些采访对话类文本不同，经常见一些采访文本的外观是问者一行答者一段，而经中须菩提一方面是提问者，一方面佛也反过来向须菩提做一些引导性的提问，所以须菩提这位学习者连接的词汇在经中所占的比例也并不是很小。

下面的图形与上面的图形所用数据一样，只是布局不同，所用的是胡一凡布局。