phd tools – Translating Data

Set-up MacMini 1.4Ghz Intel Core i5 4GB (OS X El Capitan 10.11.2)

$ sudo easy_install pip $ sudo pip install -U setuptools already installed on El Cap $ sudo pip install pyserial install MariaDB as that is what is installed on pew == make sure Xcode is uptodate or install it (this triggered an OS install, do it) $ xcode-select –install install homebrew $ ruby -e… Continue reading Set-up MacMini 1.4Ghz Intel Core i5 4GB (OS X El Capitan 10.11.2)

LiveCode

Source: Products | LiveCode App development resource based in Edinburgh.

Writing a Good Introduction

Source: Writing a Good Introduction

Rubular: regular expression editor and tester

Rubular is a Ruby-based regular expression editor and tester. It’s a handy way to test regular expressions as you write them. Rubular is an especially good fit for Ruby and Rails developers, since it uses Ruby on the server to evaluate regexes, but should also be useful for those working in other programming languages and… Continue reading Rubular: regular expression editor and tester

Writing tips – tenses, etc

https://c.ymcdn.com/sites/www.acpm.org/resource/resmgr/careers-files/preparing_articles_publicati.pdf

sepans Cheats – sepans

A cheat sheet containing scripts that I normally use (shell, sql, python .etc) Source: sepans Cheats – sepans contains some nice text sorting reg ex bits

How to construct an abstract

Source: CJ0q7lqUsAAaEFC.png:large (1024×673)

Time-Series Databases blog from Xaprb

Time-Series DB requirements Time-Series Databases and InfluxDB · Xaprb. Why use MySQL?

Tools and resources for learning data analysis, visualization, social media

list of BEST tools and resources for learning data analysis, visualization, and working with social media data – Google Docs. from Lev Manovich

100 open source Big Data architecture papers

100 open source Big Data architecture papers for data professionals. | Anil Madan | LinkedIn.

ACM Computing Classification System, 2012 Revision

http://delivery.acm.org/10.1145/2380000/2371137/ACMCCSTaxonomy.html?ip=138.37.32.43&id=2371137&acc=OPEN&key=BF07A2EE685417C5%2E53A024A4F41048F4%2E4D4702B0C3E38B35%2E6D218144511F3437&CFID=683826054&CFTOKEN=51013308&__acm__=1434451434_4215bafbd152f8c206a318a68f6020fd

Choosing R or Python for data analysis? An infographic

Choosing R or Python for data analysis? An infographic.

Orange Data Mining

Orange Data Mining. Tool for visualising diff data mining techniques

Tutorial Learn Python in 10 minutes Stavros’ Stuff…

Tutorial – Learn Python in 10 minutes – Stavros’ Stuff.

PySerial

python tutorials and resources

writeLaTeX – tool

writeLaTeX .

TreeSoft: TreeBeST

TreeSoft: TreeBeST. TreeBeST, which stands for (gene) Tree Building guided by Species Tree, is a versatile program that builds, manipulates and displays phylogenetic trees. It is particularly designed for building gene trees with a known species tree and is highly efficient and accurate. Potential tool to look into to see the NMR data on http://naked-mole-rat.org/gene_expression/

The Nature of Code [book]

The Nature of Code. Online book with processing code and tutorials by Daniel Shiffman from ITP. Great.

Thematic analysis

Thematic analysis – Wikipedia, the free encyclopedia.

Thematic analysis is the most common form of analysis in qualitative research.[1] It emphasizes pinpointing, examining, and recording patterns (or “themes”) within data.[2] Themes are patterns across data sets that are important to the description of a phenomenon and are associated to a specific research question.[3] The themes become the categories for analysis.[4] Thematic analysis is performed through the process of coding in six phases to create established, meaningful patterns. These phases are: familiarization with data, generating initial codes, searching for themes among codes, reviewing themes, defining and naming themes, and producing the final report.[5]

Ontologies – Data Mining, Datatypes, Emotion,

Ontobee: A web server aimed to facilitate ontology visualization, query, and development. Ontobee provides a user-friendly web interface for displaying the details and its hierarchy of a specific ontology term. Meanwhile, Ontobee provides a RDF source code for the particular web page, which supports remote query of the ontology term and theSemantic Web. Ontology Development… Continue reading Ontologies – Data Mining, Datatypes, Emotion,

Sara Hendren

I like the way Sara talks about herself and her work, and separates her blog and her own work. Use for framework for Trans Nature and Trans Data about |.

Writing Skeletons

“In order to get into the hang of academic writing it is sometimes helpful to examine closely the way in which other writers structure their work. Swales and Feak offer the use of skeleton sentences to achieve this. This where all of the content is stripped out of a paragraph in order to reveal the syntactic moves.… Continue reading Writing Skeletons

Definitions

Of Living: Biological
Data whose origin is directly linked to something that is alive. Data that occurs without conscious origin (i.e. not from a human typing). Often from sensors. Examples: a) species migration reported by a sensor; b) quantified self data such as output from a heart-rate monitor; c) a bird-call.

Of Living: Environmental
Data whose origin is directly linked to the natural world. Often from sensors. Examples: a) ocean temperature, b) solar storm activity, c) seed bank information.

Of Non-Living: Object
Data whose origin is a physical object or device. Object data is often generated for machine to machine communication, however, the Internet of Things will see a greater machine to (human) consumer communication. Examples: a) a fridge’s energy use; b) a CCTV camera; c) a smart watch.

Of Social Context: Commercial
Data produced by or about a corporate entity. Examples: a) 10 years of financial information about a company; b) the expiry date on a chocolate bar.

Of Social Context: Personal
Data produced by or about an individual. Certain types will have restricted access, and some legal and technical protections. Other will be accessible by some, if not all, of the general public. Examples: a) Google’s search analysis profile of a non-anonymised individual’s interests; b) International travel logs held at border controls; c) a recording of a private telephone conversation; d) family photos publicly tagged on Flickr; e) your social network feed.

Of Social Context: Social
Data produced by or about a social group or society. Examples; a) global number of births each day; b) voting preference in a London borough; c) immigration figures.

Of Social Context: State
Data produced by or about a government or ruling authority. Examples: a) the economy of the eurozone; b) legislation documents.

Of Licence: Closed
Closed data is generally only accessible to people within an organisation or to certain individuals. Examples: a) company personnel files; b) national security documents.

Of Licence: Open
Open data can be accessed, used, and shared by anyone. Examples: a) state-owned weather records, b) earthquake monitoring data.

Of Licence: Shared
Shared data is data available to a specific group of people for a specific purpose. Examples: a) the electoral register; b) anonymized supermarket shopping patterns.

Of Time/Space: Geospatial
Data describing, is relevant to, or is derived from a space or geographic area. Examples: a) GPS coordinates from a cross-country walk; b) the number of people visiting the Tate Modern art gallery; c) the area of a baseball pitch; d) longitude and latitude.

Of Time/Space: Live
Data which is, or was, captured in real-time. The recording does not necessarily get played-back at the same rate, or in the same moment. Examples: a) a football match on TV; b) animal tracking data.

Of Time/Space: Real-time
Data that is created, captured and disseminated in an immediate (ish) time-frame relative to the context of its use; it changes over time. Examples: a) smart-meter reporting electricity usage every 30 seconds (real-time data acquisition with a relevant-time display); b) feeds from sensors such as a webcam on a birds nest, a GPS location of a mobile phone, or a humidity reading in an gallery space.

Of Time/Space: Static
Data in which the items do not change once created, but the dataset can grow over time. Includes historical datasets and archive indexes. Examples: a) historical global population size; b) a recording in the sound archive at the British Library.

Of Time/Space: Temporal
Data which is time-based in its nature, relevant to a specific time or which may only exist for a short time period (transient). Examples: a) the value of a kilogram of rice over time; b) your date of birth; c) the radio signals received from an exploding star.

Of Type: Anecdata
Anecdotal information gathered and then presented as evidence. Anecdata is often not precisely measurable, has no reliable provenance, is hard to compare, and /or cannot be unproven by the scientific method. Examples: a) a collection of comments on a product website; b) proverbs such as ”Never look a gift horse in the mouth”.

Of Type: Causal
Data in which it is (or is made) obvious to the observer what its origin is. Example: a vocal recording.

Of Type: Generated
Data created by a software program. Examples: a) algorithmic music; b) cellular automaton; c) a model of a galaxy exploding.

Of Type: Metadata
Data about data. Data which describes information about other data. Examples: a) the number of rows in a database; b) the time and date a phone call was made.

Of Type: Processed
Data which has been calculated, altered or processed in some way. Examples: a) a sonification of stock market figures; b) aggregated statistics; c) a colourful digital photograph reduced to black and white.

Of Type: Retrieved
Data made available on request by machine or user. Examples: a) compilation of weather data from the past 24 hours as a single CSV file; b) availability status of a library book.

Of Type: Streamed
The technical means of delivering real-time data as a contiguous stream. The primary use-cases are where there is no requirement for data storage, or that the data-sets involved are too large to be manipulated in any other manner (the entire Twitter back catalogue). Examples: a) real-time audio and video from a carnival procession; b) on-demand replay of a film from 1960; c) music playing from a digital radio.

Of Disclosure: Anonymised
Data that has had any identifiable in- formation about a person, animal, or thing removed. Examples: a) CCTV camera footage containing people which have been blurred or obfuscated; b) all bicycle hire users across a city with user IDs and names removed.

Disclosure: Identifiable
Data in which the direct source within it (person, animal, or thing) can be identified. Examples: a) a Facebook data export including friend names; b) a set of mobile phone numbers with owner address details.

Of Disclosure: Unknown
Data which contains information about a person, animal, or thing but in which it is not clear if it is adequately anonymised. Examples: a) a live Twitter feed containing some geolocated photos of people and animals; b) a sound recording from a public space that includes ambient conversation.