Open Data

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= "The technological perspective represented by the Open Data Movement. Open data is a philosophy and practice requiring that certain data be freely available to everyone, without restrictions from copyright, patents or other mechanisms of control." [1]

URL = http://okfn.org/opendata/

Definition

1. Open data is data that can be freely used, shared and built-on by anyone, anywhere, for any purpose. [2]


2. OpenDefinition.org: “Open data is data that can be freely used, reused and redistributed by anyone – subject only, at most, to the requirement to attribute and sharealike.” - [3]

3. From the Wikipedia at http://en.wikipedia.org/wiki/Open_Data

"Open Data is a philosophy and practice requiring that certain data are freely available to everyone, without restrictions from copyright, patents or other mechanisms of control. It has a similar ethos to a number of other "Open" movements and communities such as Open Source and Open access.

Open Data is often focussed on non-textual material such as maps, genomes, chemical compounds, mathematical and scientific formulae, medical data and practice, bioscience and biodiversity. Problems often arise because these are commercially valuable or can be aggregated into works of value. Access to, or re-use of, the data are controlled by organisations, both public and private. Control may be through access restrictions, licenses, copyright, patents and charges for access or re-use. Advocates of Open Data argue that these restrictions are against the communal good and that these data should be made available without restriction or fee. In addition, it is important that the data are re-usable without requiring further permission, though the types of re-use (such as the creation of derivative works) may be controlled by license."


What is Open?

"The full Open Definition provides a precise definition of what open data is. There are 2 important elements to openness:

  • Legal openness: you must be allowed to get the data legally, to build on it, and to share it. Legal openness is usually provided by applying an appropriate (open) license which allows for free access to and reuse of the data, or by placing data into the public domain.
  • Technical openness: there should be no technical barriers to using that data. For example, providing data as printouts on paper (or as tables in PDF documents) makes the information extremely difficult to work with. So the Open Definition has various requirements for “technical openness,” such as requiring that data be machine readable and available in bulk.


There are a few key aspects of open which the Open Definition explains in detail. Open Data is useable by anyone, regardless of who they are, where they are, or what they want to do with the data; there must be no restriction on who can use it, and commercial use is fine too.

Open data must be available in bulk (so it’s easy to work with) and it should be available free of charge, or at least at no more than a reasonable reproduction cost. The information should be digital, preferably available by downloading through the internet, and easily processed by a computer too (otherwise users can’t fully exploit the power of data – that it can be combined together to create new insights).

Open Data must permit people to use it, re-use it, and redistribute it, including intermixing with other datasets and distributing the results.

The Open Definition generally doesn’t allow conditions to be placed on how people can use Open Data, but it does permit a data provider to require that data users credit them in some appropriate way, make it clear if the data has been changed, or that any new datasets created using their data are also shared as open data.

There are 3 important principles behind this definition of open, which are why Open Data is so powerful:

  • Availability and Access: that people can get the data
  • Re-use and Redistribution: that people can reuse and share the data
  • Universal Participation: that anyone can use the data,"

(http://blog.okfn.org/2013/10/03/defining-open-data/)

Characteristics

1. The Open Definition gives full details on the requirements for ‘open’ data and content. Key features are:

  • Availability and Access: the data must be available as a whole and at no more than a reasonable reproduction cost, preferably by downloading over the internet. The data must also be available in a convenient and modifiable form.
  • Reuse and Redistribution: the data must be provided under terms that permit reuse and redistribution including the intermixing with other datasets. The data must be machine-readable.
  • Universal Participation: everyone must be able to use, reuse and redistribute – there should be no discrimination against fields of endeavour or against persons or groups. For example, ‘non-commercial’ restrictions that would prevent ‘commercial’ use, or restrictions of use for certain purposes (e.g. only in education), are not allowed."

(http://okfn.org/opendata/)


2. Fundamental Open Data Rights:

"Arguments made on behalf of Open Data include:


  • "Data belong to the human race". Typical examples are genomes, data on organisms, medical science, environmental data.
  • Public money was used to fund the work and so it should be universally available.
  • It was created by or at a government institution (this is common in US National Laboratories and government agencies)
  • Facts cannot legally be copyrighted.
  • Sponsors of research do not get full value unless the resulting data are freely available
  • Restrictions on data re-use create an anticommons
  • Data are required for the smooth process of running communal human activities (map data, public institutions)
  • In scientific research, the rate of discovery is accelerated by better access to data."

(http://en.wikipedia.org/wiki/Open_Data)


3. Relation to other open activities:

"There are a number of other "Open" philosophies which are similar to, but not synonymous with Open Data but which may overlap, be supersets, or subsets. Here they are briefly listed and compared.


  • Open Source Software is concerned with the licenses under which computer programs can be distributed and is not normally concerned primarily with data.
  • Open Content has similarities to Open Data and may be seen as a superset but differs in that it emphasizes creative works while Open Data is more oriented towards factual data and the output of the scientific research process.
  • Open Knowledge. The Open Knowledge Foundation argues for Openness in a range of issues including, but not limited to, those of Open Data. It covers (a) scientific, historical, geographic or otherwise (b) Content such as music, films, books (c) Government and other administrative information

(http://en.wikipedia.org/wiki/Open_Data)


4. Open Data are opposed by Closed Data:

"Several intentional or unintentional mechanisms exist for restricting access to or re-use of data. They include:


  • compilation in databases or websites to which only registered members or customers can have access.
  • use of a proprietary or closed technology or encryption which creates a barrier for access.
  • copyright forbidding (or obfuscating) re-use of the data.
  • license forbidding (or obfuscating) re-use of the data
  • patent forbidding re-use of the data (for example the 3-dimensional coordinates of some experimental protein structures have been patented)
  • restriction of robots to websites, with preference to certain search engines
  • aggregating factual data into "databases" which may be covered by "database rights" or "database directives" (e.g. Directive on the legal protection of databases)
  • time-limited access to resources such as e-journals (which on traditional print were available to the purchaser indefinitely)
  • political, commercial or legal pressure on the activity of organisations providing Open Data (for example the American Chemical Society lobbied the US Congress to limit funding to the National Institutes of Health for its Open Pubchem data."

(http://en.wikipedia.org/wiki/Open_Data)


How-To

3 Key Rules

"There are three key rules we recommend following when opening up data:

Keep it simple. Start out small, simple and fast. There is no requirement that every dataset must be made open right now. Starting out by opening up just one dataset, or even one part of a large dataset, is fine — of course, the more datasets you can open up the better.

Remember this is about innovation. Moving as rapidly as possible is good because it means you can build momentum and learn from experience — innovation is as much about failure as success and not every dataset will be useful.

Engage early and engage often. Engage with actual and potential users and re-users of the data as early and as often as you can, be they citizens, businesses or developers. This will ensure that the next iteration of your service is as relevant as it can be. It is essential to bear in mind that much of the data will not reach ultimate users directly, but rather via ‘info-mediaries’. These are the people who take the data and transform or remix it to be presented. For example, most of us don’t want or need a large database of GPS coordinates, we would much prefer a map. Thus, engage with infomediaries first. They will re-use and repurpose the material.

Address common fears and misunderstandings. This is especially important if you are working with or within large institutions such as government. When opening up data you will encounter plenty of questions and fears. It is important to (a) identify the most important ones and (b) address them at as early a stage as possible." (http://okfn.org/opendata/)

The Four Steps

"These are in very approximate order – many of the steps can be done simultaneously.

  • Choose your dataset(s). Choose the dataset(s) you plan to make open. Keep in mind that you can (and may need to) return to this step if you encounter problems at a later stage.
  • Apply an open license.
  • Determine what intellectual property rights exist in the data.
  • Apply a suitable ‘open’ license that licenses all of these rights and supports the definition of openness discussed in the section above on ‘What Open Data’
  • Make the data available – in bulk and in a useful format. You may also wish to consider alternative ways of making it available such as via an API.
  • Make it discoverable – post on the web and perhaps organize a central catalog to list your open datasets."

(http://okfn.org/opendata/)

Discussion

Why open data may be more important than open source

Ian Davis:

"data outlasts code which lead me to then assert that therefore open data is more important than open source. This appears to be controversial.

First, it’s important to note what I did not say. I did not say that open source is not important. On the contrary I said that open source was extremely important and it has sounded the death knell for proprietary software. Later speakers at the conference referred to this statement as controversial too :). (What I actually meant to say was that open source has sounded the death knell for propietary software models). I also mentioned that open source and free software has a long history and that open data is where open source was 25 years ago (I am using the term open source and free software interchangeably here).

I also did not say that code does not last nor that algorithms do not last. Of course they last, but data lasts longer. My point was that code is tied to processes usually embodied in hardware whereas data is agnostic to the hardware it resides on. The audience at the conference understand this already: they are archivists and librarians and they deal with data formats like MARC which has had superb longevity. Many of them deal with records every day that are essentially the same as they were two or three decades ago. Those records have gone through multiple generations of code to parse and manipulate the data.

It’s true that you need code to access data, but critically it doesn’t have to be the same code from year to year, decade to decade, century to century. Any code capable of reading the data will do, even if it’s proprietary. You can also recreate the code whereas the effort involved in recreating the data could be prohibitively high. This is, of course, a strong argument for open data formats with simple data models: choosing CSV, XML or RDF is going to give you greater data longevity than PDF, XLS or PST because the cost of recreating the parsing code is so much lower.

Here’s the central asymmetry that leads me to conclude that open data is more important than open source: if you have data without code then you could write a program to extract information from the data, but if you have code without data then you have lost that information forever.

Consider also, the rise of software as a service. It really doesn’t matter whether the code they are built on are open source or not if you cannot access the data they manage for you. Even if you reproduce the service completely, using the same components, your data is buried awayout of your reach. However, if you have access to the data then you can achieve continuity even if you don’t have access to the underlying source of the application. I’ll say it again: open data is more important than open source.

Of course we want open standards, open source and open data. But in one or two hundred years which will still be relevant? Patents and copyrights on formats expire, hardware platforms and even their paradigms shift and change. Data persists, open data endures.

The problem we have today is that the open data movement is in its infancy when compared to open source. We have so far to go, and there are many obstacles. One of the first steps to maturity is to give people the means to express how open their data is, how reusable it is. The Open Data Commons is an organisation explicitly set up to tackle the problem of open data licensing. If you are publishing data in any way you ought to check out their licences and see if any meet with your goals. If you licence your data openly then it will be copied and reused and will have an even greater chance of persisting over the long term." (http://iandavis.com/blog/2009/03/open-data-open-source)


Resources

Open Data Policies

RECOMMENDATIONS from the U.S. Public Policy Committee of the ACM (USACM):


  • Data published by the government should be in formats and approaches that promote analysis and reuse of that data.


  • Data republished by the government that has been received or stored in a machine-readable format (such as online regulatory filings) should preserve the machine-readability of that data.


  • Information should be posted so as to also be accessible to citizens with limitations and disabilities.


  • Citizens should be able to download complete datasets of regulatory, legislative or other information, or appropriately chosen subsets of that information, when it is published by government.


  • Citizens should be able to directly access government-published datasets using standard methods such as queries via an API (Application Programming Interface).


  • Government bodies publishing data online should always seek to publish using data formats that do not include executable content.


  • Published content should be digitally signed or include attestation of publication/creation date, authenticity, and integrity.

(http://www.acm.org/public-policy/open-government)


Open Data Organizations

Open Data Companies

"“Open data is to media what open source is to technology. Open data is an approach to content creation that explicitly recognizes the value of implicit user data. The internet is the first medium to give a voice to the attention that people pay to it. Successful open data companies listen for and amplify the rich data that their audiences produce.” (http://www.attentiontrust.org/node/430)

  • Adaptive Blue- Extended browsing
  • Aggregate Knowledge- Outsourced recommendations
  • Atten.TV- Attention media
  • Buzzlogic- Tracking influence
  • ClearForest – Text analytics
  • Daylife- Hi-touch algorithmic news
  • Feedburner- RSS content management
  • Lijit Networks- Ranking people
  • Majestic Research- Online behavior for investors
  • Meetup- America offline
  • MyBlogLog- Reader communities
  • Omnidrive- Open data storage
  • Right Media- Transparent ad network
  • Stumbleupon- The "forward" button

(http://www.attentiontrust.org/node/428)

Open Data Repositories

open data sets availiable on the Web.

Examples include Wikipedia, Geonames, MusicBrainz

See also: PubChem


Open Data Domains

The concept of Open Data is used in different contexts, i.e. mostlhy as either the availability of scientific raw data and as open access to publicly funded, 'government' information.

(There is of course an obvious overlap when the scientific data are produced by public funding or government institutions.)


Open Access to Government Information

See Open Access to Government Information, as well as Open Government Data and Open Public Data


Open Data in Science

See: Open Data in Science



Status Report 2007

Peter Suber:

"With or without mandates, more governments committed themselves to OA for publicly funded data. Norway adopted an OA mandate for public geodata. Canada, Ireland, and Australia began providing OA to publicly funded digital mapping data, without a mandate. After long resistance, the UK Ordnance Survey began to do the same, at least experimentally. (Earlier in the year, a legal analysis by Charlotte Waelde, an expert on intellectual property at the University of Edinburgh, concluded that the data are not protected by copyright but at most, only by the database right; a JISC report recommended a general UK policy of OA for research data; and the new UK Prime Minister Gordon Brown endorsed the principle of public access to public data.) The Committee of Ministers of the Council of Europe recommended "wide public access to research results to which no copyright restrictions apply" (i.e. data). Publishing consultant Eve Gray reported that the South African government was moving toward a policy of OA for publicly funded research data. The Australian government proposed an Australian National Data Service to promote OA and re-use of publicly funded research data. The Organisation for Economic Co-operation and Development (OECD) issued principles and guidelines to implement its 2004 Declaration on Access to Research Data from Public Funding. California is about to adopt the strongest and broadest OA mandate for greenhouse gas data in the US, and Pennsylvania is about to join the other 49 states in mandating OA for state statutes. And the UN Convention on Long-range Transboundary Air Pollution (LRTAP) adopted an OA mandate for most kinds of data covered by the convention.

The US Government Accountability Office called on four major federal funding agencies (DOE, NASA, NOAA, and NSF) to enforce their existing policies on data sharing. Twenty-two US federal government agencies formed an Interagency Working Group on Digital Data (IWGDD), plan to deposit the data generated by their research grantees in a network of OA repositories, and are considering an OA mandate. The US National Archives joined the OA web portal Geospatial One Stop. The NSF Office of Cyberinfrastructure launched a data interoperability project (INTEROP). Google created a Public Sector Initiative to improve its crawling of OA databases hosted by federal, state, and local government agencies in the US. A group of open government activists convened by O'Reilly Media and Public.Resource.Org drafted principles for open government data. For the first time the US made progress toward OA for its three most notorious non-OA government resources: PACER (Public Access to Court Electronic Records), the database of federal court docket information; NTIS (National Technical Information Service), the online databases of research and business data; and CRS Reports, the highly regarded reports from the Congressional Research Service. The first two began offering OA to selected portions of their content, previously TA, and the third is the subject of a new bill in the Senate to mandate OA.

Nature editorialized in favor of e-notebook science and data sharing, and Nature Biotech recommended "that raw data from proteomics and molecular-interaction experiments be deposited in a public [OA] database before manuscript submission." Maxine Clarke, Publishing Executive Editor at Nature, said that the journal would consider requiring and not merely recommending OA for multimedia data if there were a suitable OA repository supporting annotation and long-term preservation. Wiley threatened legal action when Shelley Batts, a graduate student at the University of Michigan, posted a chart from a Wiley article from the Journal of the Science of Food and Agriculture on her blog; when she replaced it with her own chart of the same data and blogged Wiley's threat, the blogosphere exploded and Wiley said it was all a misunderstanding.

Data-sharing policies were adopted by the UK Medical Research Council, the Ethics Committee of France's Centre National de la Recherche Scientifique (CNRS), the Audiovisual Communications Laboratory at Switzerland's Ecole Polytechnique Fédérale de Lausanne, and the International Telecommunications Union. The NIH launched a new data-sharing program for its neuroscience research. There are too many new OA databases to name separately, but since I've mentioned the NIH, I should add that it launched the Database of Genotype and Phenotype (dbGaP) and SHARe (SNP Health Association Resource). It described SHARe as "one of the most extensive collections of genetic and clinical data ever made freely available to researchers worldwide."

Google began helping researchers exchange datasets up to 120 terabytes in size, too large for ordinary online uploads and downloads. At no charge to the researchers, it will ship a brick-sized box of hard drives from one research team to another, provided that the data have no copyright or licensing restrictions and the bricks stop first at Google headquarters for copying and offline storage. In time, Google hopes to make the datasets OA. The company also began sharing files of its own data with researchers on the condition that they make the results of their research OA.

The year 2007 saw a wave of general OA data repositories spring up, many with built-in features for graphics and analysis: for example, Dabble, Data360, Freebase, Many Eyes, Open Economics, StatCrunch, Swivel, and WikiProteins. At the same time, several projects worked to facilitate the deposit of data in OA repositories, such as EDINA's DataShare and JISC's SPECTRa (Submission, Preservation and Exposure of Chemistry Teaching and Research Data), or to enhance the interface between data repositories and literature repositories, such as JISC's StORe (Source-to-Output Repositories).

By my informal estimate, the fields with the largest advances in OA data during 2007 were archaeology, astronomy, chemistry, the environment (including climate change), geography (including mapping), and medicine (especially, genomics and clinical drug trials)." (http://quod.lib.umich.edu/cgi/t/text/text-idx?c=jep;view=text;rgn=main;idno=3336451.0011.110)


Discussion

Open data and the Commons

an old story? by Simon Chignard:

"There is a direct link between the open data movement and the philosophy of common goods. Open data are an illustration of the notion of common informational goods proposed by Elinor Ostrom, winner of the 2009 Nobel Prize for economics. Open data belong to everyone and, unlike water and air (and other common goods), they are non-exclusive: their use by one does not prevent others. If I reuse an open data set, this does not prevent other reusers from doing so. This proximity between the commons and open data is also suggested by the presence of the initiator of Creative Commons licences, Lawrence Lessig, at the 2007 Sebastopol meeting in which the concept of open data itself was defined.

But despite the strong conceptual and historical linkages, it seems that we, as actors of open data, are often shy to reaffirm the relationship. In our efforts to encourage public and private bodies to embrace open data, we seem almost embarrassed of this cornerstone philosophy. The four proposals I make here aim at one thing: not letting it drop!" (http://blog.okfn.org/2013/01/10/4-ideas-for-defending-the-open-data-commons/)

More Information

  1. Open Science
  2. Open Notebook Science