Open Data

Introduction


Open data is data that is openly accessible, exploitable, editable and shared by anyone for any purpose. Open data is licensed under an open license.

Goals
The goals of the open data movement are similar to those of other "open(-source)" movements such as open-source software, open-source hardware, open content, open specifications, open education, open educational resources, open government, open knowledge, open access, open science, and the open web.

Development and Growth of Movement
The growth of the open data movement is paralleled by a rise in intellectual property rights. The philosophy behind open data has been long established (for example in the Mertonian tradition of science), but the term "open data" itself is recent, gaining popularity with the rise of the Internet and World Wide Web and, especially, with the launch of open-data government initiatives Data.gov, Data.gov.uk and Data.gov.in.

Remark
Open data can be linked data - referred to as linked open data.

Open Government Data
One of the most important forms of open data is open government data (OGD), which is a form of open data created by ruling government institutions. Open government data's importance is born from it being a part of citizens' everyday lives, down to the most routine/mundane tasks that are seemingly far removed from government.

Abbreviation
The abbreviation FAIR/O data is sometimes used to indicate that the dataset or database in question complies with the principles of FAIR data and carries an explicit data‑capable open license.

Learning Tasks

 * (FAIR Data Principles) Explain the role of properties
 * Findable,
 * Accessible,
 * Interoperable and
 * Reusable
 * for open data workflows and frameworks. Apply FAIR to your domain of expertise and identify the benefits and challenges for implementation.


 * Analyse the availability of Open Data in specific domains (e.g. Open Food Facts) and describe how the availability of the food facts can be regarded as public digital good. How can open data can benefit society (e.g. in comparison to information provided in advertisements about food and nutrition)
 * (Open Data and Machine Learning) The in- and output behavior in machine learning is determined by the training data the Open Source machine learning algorithms are trained with. Transparency allows the replication of a machine learning approach. Explain how open data can contribute to transparency of machine learning and artificial intelligence.

Learning Resource

 * /Wikibook on Open Data/ - contribute to a Wiki
 * /Understand nutrition and food labels/ - open data and nutrition
 * /Major Sources/

Overview
The concept of open data is not new, but a formalized definition is relatively new. Open data as a phenomenon denotes that governmental data should be available to anyone with a possibility of redistribution in any form without any copyright restriction.

Digital Signature
Digital Signature provides an optional method add digital signature to the open data so that shared data can be validated can be checked if they are unmodified in the redistributed form. At the same time the data remains open change to add new items to the database, correct errors.

Version Control
The application of Version Control simplifies the management of the data according to changes that are performed on the open data resource.

Definitions
One more definition is the Open Definition which can be summarized as "a piece of data is open if anyone is free to use, reuse, and redistribute it – subject only, at most, to the requirement to attribute and/or share-alike." Other definitions, including the Open Data Institute's "open data is data that anyone can access, use or share," have an accessible short version of the definition but refer to the formal definition. Open data may include non-textual material such as maps, genomes, connectomes, chemical compounds, mathematical and scientific formulae, medical data, and practice, bioscience and biodiversity.

Commercial Use of Data
A major barrier to the open data movement is the commercial value of data. Access to, or re-use of, data is often controlled by public or private organizations. Control may be through access restrictions, licenses, copyright, patents and charges for access or re-use.

Digital Common Good
Advocates of open data argue that these restrictions in specific domains detract from the common good (e.g. for humanitarian objective or application risk mitigation strategies build on open data) and therefore that data should be available without restrictions or fees as a digital common good.

Creation of Open Data and License Selection
Creators of data do not consider the need to state the conditions of ownership, licensing and re-use; instead presuming that not asserting copyright enters the data into the public domain. For example, many scientists do not consider the data published with their work to be theirs to control and consider the act of publication in a journal to be an implicit release of data into the commons.

Lack of License
The lack of a license makes it difficult to determine the status of a data set and may restrict the use of data offered in an "Open" spirit. Because of this uncertainty it is possible for public or private organizations to aggregate said data, claim that it is protected by copyright, and then resell it.

Policies and strategies
At a small level, a business or research organization's policies and strategies towards open data will vary, sometimes greatly. One common strategy employed is the use of a data commons. A data commons is an interoperable software and hardware platform that aggregates (or collocates) data, data infrastructure, and data-producing and data-managing applications in order to better allow a community of users to manage, analyze, and share their data with others over both short- and long-term timelines. Ideally, this interoperable cyberinfrastructure should be robust enough "to facilitate transitions between stages in the life cycle of a collection" of data and information resources while still being driven by common data models and workspace tools enabling and supporting robust data analysis. The policies and strategies underlying a data commons will ideally involve numerous stakeholders, including the data commons service provider, data contributors, and data users.

Grossman et al suggests six major considerations for a data commons strategy that better enables open data in businesses and research organizations. Such a strategy should address the need for:


 * permanent, persistent digital IDs, which enable access controls for datasets;
 * permanent, discoverable metadata associated with each digital ID;
 * application programming interface (API)-based access, tied to an authentication and authorization service;
 * data portability;
 * data "peering," without access, egress, and ingress charges; and
 * a rationed approach to users computing data over the data commons.

Beyond individual businesses and research centers, and at a more macro level, countries like Germany have launched their own official nationwide open data strategies, detailing how data management systems and data commons should be developed, used, and maintained for the greater public good.

Arguments for and against
Opening government data is only a waypoint on the road to improving education, improving government, and building tools to solve other real-world problems. While many arguments have been made categorically, the following discussion of arguments for and against open data highlights that these arguments often depend highly on the type of data and its potential uses.

Arguments made on behalf of open data include the following:


 * "Data belongs to the human race". Typical examples are genomes, data on organisms, medical science, environmental data following the Aarhus Convention.
 * 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 and are an important enabler of socio-economic development (health care, education, economic productivity, etc.).
 * In scientific research, the rate of discovery is accelerated by better access to data.
 * Making data open helps combat "data rot" and ensure that scientific research data are preserved over time.
 * Statistical literacy benefits from open data. Instructors can use locally relevant data sets to teach statistical concepts to their students.
 * Allowing open data in the scientific community is essential for increasing the rate of discoveries and recognizing significant patterns.

It is generally held that factual data cannot be copyrighted. Publishers frequently add copyright statements (often forbidding re-use) to scientific data accompanying publications. It may be unclear whether the factual data embedded in full text are part of the copyright.

While the human abstraction of facts from paper publications is normally accepted as legal there is often an implied restriction on the machine extraction by robots.

Unlike open access, where groups of publishers have stated their concerns, open data is normally challenged by individual institutions. Their arguments have been discussed less in public discourse and there are fewer quotes to rely on at this time.

Arguments against making all data available as open data include the following:

The paper entitled "Optimization of Soft Mobility Localization with Sustainable Policies and Open Data" argues that open data is a valuable tool for improving the sustainability and equity of soft mobility in cities. The author argues that open data can be used to identify the needs of different areas of a city, develop algorithms that are fair and equitable, and justify the installation of soft mobility resources.
 * Government funding may not be used to duplicate or challenge the activities of the private sector (e.g. PubChem).
 * Governments have to be accountable for the efficient use of taxpayer's money: If public funds are used to aggregate the data and if the data will bring commercial (private) benefits to only a small number of users, the users should reimburse governments for the cost of providing the data.
 * Open data may lead to exploitation of, and rapid publication of results based on, data pertaining to developing countries by rich and well-equipped research institutes, without any further involvement and/or benefit to local communities (helicopter research); similarly, to the historical open access to tropical forests that has led to the misappropriation ("Global Pillage") of plant genetic resources from developing countries.
 * The revenue earned by publishing data can be used to cover the costs of generating and/or disseminating the data, so that the dissemination can continue indefinitely.
 * The revenue earned by publishing data permits non-profit organizations to fund other activities (e.g. learned society publishing supports the society).
 * The government gives specific legitimacy for certain organizations to recover costs (NIST in US, Ordnance Survey in UK).
 * Privacy concerns may require that access to data is limited to specific users or to sub-sets of the data.
 * Collecting, 'cleaning', managing and disseminating data are typically labour- and/or cost-intensive processes – whoever provides these services should receive fair remuneration for providing those services.
 * Sponsors do not get full value unless their data is used appropriately – sometimes this requires quality management, dissemination and branding efforts that can best be achieved by charging fees to users.
 * Often, targeted end-users cannot use the data without additional processing (analysis, apps etc.) – if anyone has access to the data, none may have an incentive to invest in the processing required to make data useful (typical examples include biological, medical, and environmental data).
 * There is no control to the secondary use (aggregation) of open data.

Relation to other open activities
The goals of the Open Data movement are similar to those of other "Open" movements.


 * Open access is concerned with making scholarly publications freely available on the internet. In some cases, these articles include open datasets as well.
 * Open specifications are documents describing file types or protocols, where the documents are openly licensed. These specifications are primarily meant to improve different software handling the same file types or protocols, but monopolists forced by law into open specifications might make it more difficult.
 * Open content is concerned with making resources aimed at a human audience (such as prose, photos, or videos) freely available.
 * Open knowledge. Open Knowledge International 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. Open data is included within the scope of the Open Knowledge Definition, which is alluded to in Science Commons' Protocol for Implementing Open Access Data.
 * Open notebook science refers to the application of the Open Data concept to as much of the scientific process as possible, including failed experiments and raw experimental data.
 * Open-source software is concerned with the open-source licenses under which computer programs can be distributed and is not normally concerned primarily with data.
 * Open educational resources are freely accessible, openly licensed documents and media that are useful for teaching, learning, and assessing as well as for research purposes.
 * Open research/open science/open science data (linked open science) means an approach to open and interconnect scientific assets like data, methods and tools with linked data techniques to enable transparent, reproducible and interdisciplinary research.
 * Open-GLAM (Galleries, Library, Archives, and Museums) is an initiative and network that supports exchange and collaboration between cultural institutions that support open access to their digitalized collections. The GLAM-Wiki Initiative helps cultural institutions share their openly licensed resources with the world through collaborative projects with experienced Wikipedia editors. Open Heritage Data is associated with Open GLAM, as openly licensed data in the heritage sector is now frequently used in research, publishing, and programming, particularly in the Digital Humanities.

Ideas and definitions
Formally both the definition of Open Data and commons revolve around the concept of shared resources with a low barrier to access. Substantially, digital commons include Open Data in that it includes resources maintained online, such as data. Overall, looking at operational principles of Open Data one could see the overlap between Open Data and (digital) commons in practice. Principles of Open Data are sometimes distinct depending on the type of data under scrutiny. Nonetheless, they are somewhat overlapping and their key rationale is the lack of barriers to the re-use of data(sets). Regardless of their origin, principles across types of Open Data hint at the key elements of the definition of commons. These are, for instance, accessibility, re-use, findability, non-proprietarily. Additionally, although to a lower extent, threats and opportunities associated with both Open Data and commons are similar. Synthesizing, they revolve around (risks and) benefits associated with (uncontrolled) use of common resources by a large variety of actors.

The System
Both commons and Open Data can be defined by the features of the resources that fit under these concepts, but they can be defined by the characteristics of the systems their advocates push for. Governance is a focus for both Open Data and commons scholars. The key elements that outline commons and Open Data peculiarities are the differences (and maybe opposition) to the dominant market logics as shaped by capitalism. Perhaps it is this feature that emerges in the recent surge of the concept of commons as related to a more social look at digital technologies in the specific forms of digital and, especially, data commons.

Real-life case
Application of open data for societal good has been demonstrated in academic research works. The paper "Optimization of Soft Mobility Localization with Sustainable Policies and Open Data" uses open data in two ways. First, it uses open data to identify the needs of different areas of a city. For example, it might use data on population density, traffic congestion, and air quality to determine where soft mobility resources, such as bike racks and charging stations for electric vehicles, are most needed. Second, it uses open data to develop algorithms that are fair and equitable. For example, it might use data on the demographics of a city to ensure that soft mobility resources are distributed in a way that is accessible to everyone, regardless of age, disability, or gender. The paper also discusses the challenges of using open data for soft mobility optimization. One challenge is that open data is often incomplete or inaccurate. Another challenge is that it can be difficult to integrate open data from different sources. Despite these challenges, the paper argues that open data is a valuable tool for improving the sustainability and equity of soft mobility in cities.

An exemplification of how the relationship between Open Data and commons and how their governance can potentially disrupt the market logic otherwise dominating big data is a project conducted by Human Ecosystem Relazioni in Bologna (Italy). See: https://www.he-r.it/wp-content/uploads/2017/01/HUB-report-impaginato_v1_small.pdf.

This project aimed at extrapolating and identifying online social relations surrounding “collaboration” in Bologna. Data was collected from social networks and online platforms for citizens collaboration. Eventually data was analyzed for the content, meaning, location, timeframe, and other variables. Overall, online social relations for collaboration were analyzed based on network theory. The resulting dataset have been made available online as Open Data (aggregated and anonymized); nonetheless, individuals can reclaim all their data. This has been done with the idea of making data into a commons. This project exemplifies the relationship between Open Data and commons, and how they can disrupt the market logic driving big data use in two ways. First, it shows how such projects, following the rationale of Open Data somewhat can trigger the creation of effective data commons. The project itself was offering different types of support to social network platform users to have contents removed. Second, opening data regarding online social networks interactions has the potential to significantly reduce the monopolistic power of social network platforms on those data.

Funders' mandates
Several funding bodies which mandate Open Access mandate Open Data. A good expression of requirements (truncated in places) is given by the Canadian Institutes of Health Research (CIHR):


 * to deposit bioinformatics, atomic and molecular coordinate data, experimental data into the appropriate public database immediately upon publication of research results.
 * to retain original data sets for a minimum of five years after the grant. This applies to all data, whether published or not.

Other bodies active in promoting the deposition of data as well as full text include the Wellcome Trust. An academic paper published in 2013 advocated that Horizon 2020 (the science funding mechanism of the EU) should mandate that funded projects hand in their databases as "deliverables" at the end of the project, so that they can be checked for third party usability then shared.

Non-open data
Several mechanisms restrict access to or reuse of data (and several reasons for doing this are given above). They include:
 * making data available for a charge;
 * 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 statements claiming to forbid (or obfuscating) re-use of the data, including the use of "no derivatives" requirements;
 * 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);
 * "webstacles", or the provision of single data points as opposed to tabular queries or bulk downloads of data sets; and
 * 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).

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