ODI Data Infrastructure, Institutions & Data Access Initiatives

Free and public code grew in direct response to the perceived failings of expensive, proprietary commercial software. As a result, the heart of the problem with digital infrastructure is also part of what makes it so rich with potential: It is not centralized. There is no one person or entity deciding what’s needed and what’s not. There is also no one overseeing how digital infrastructure is implemented. And because the community of volunteers developing this infrastructure has a complicated relationship with what might be seen as a more traditional, or official, way of doing things, few digital infrastructure projects have a clear business model or source of revenue. Even projects that have grown to be used by millions of people tend to lack a cohesive structure and plan for sustaining the technology’s long-term development.

We need to start by educating people who are in positions to provide support. Many of them—from start-up engineers to government officials—don’t know enough about how digital infrastructure functions and what it requires, or are under the perception that public software doesn’t need support.

There are still major efforts needed to continuously improve. More focus needs to be put on measuring internal processes, costs, and components of quality other than accuracy. Documentation needs to be regularly updated, methods for incorporating Big Data developed, and flexibility improved so that adaptive methods based on paradata can be used.

it takes regular management involvement and procedures to be in place for it to succeed

Measurements are vital, but they are not the goal. This will require re-focusing on improving internal processes. It also implies recognizing the need to track costs as a component of quality.

They play an important role, helping to publish, structure and link together data.

The simplest way to generate identifiers is by a serial number.

the Ordnance Survey TOID identifier is a serial number that looks like this: osgb1000006032892. UPRNs are similar.

From its early beginnings over 150 years ago, weather forecasting at the Met Office has been driven by data. Simple observations recorded and used to hand-plot synoptic charts have been exchanged for the billions of observations received and handled every day, mainly from satellites but also from weather stations, radar , ocean buoys, planes, shipping and the public.

The key stages of the weather data value chain are as follows: Ÿ Monitoring and observation of the weather and environment, e.g. by NMSs. Ÿ Numerical weather prediction (NWP) and climate modelling carried out by NMSs to create global, regional and limited area weather forecasts. Private companies are growing their presence in the market and challenging the traditional role of NMSs to provide forecasts to the public, by statistically blending data from NMS models to create their own forecast models, for example. Other companies providing data via online channels and/or apps include The Weather Company, Accuweather or the Climate Corporation. Ÿ Communication and dissemination of forecasts by news, NMS and media organisations like the BBC, Yahoo and Google, or within consumer-targeted mobile and web applications. Ÿ Decision making by individuals and businesses across a variety of sectors, which draws on weather data and reporting.

The core data asset of our global weather data infrastructure is observation data that captures a continuous record of weather and climate data around the world. This includes temperature, rainfall, wind speed and details of a host of other atmospheric, surface and marine conditions.

ongoing engineering improvements, product development, design and research, and legal support.

The auditor's unqualified report contains three paragraphs. The introductory paragraph identifies the financial statements audited, states that management is responsible for those statements, and asserts that the auditor is responsible for expressing an opinion on them. The scope paragraph describes what the auditor has done and specifically states that the auditor has examined the financial statements in accordance with generally accepted auditing standards and has performed appropriate tests. The opinion paragraph expresses the auditor's opinion (or formally announces his or her lack of opinion and why) on whether the statements are in accordance with generally accepted accounting principles.

Major types of audits conducted by external auditors include the financial statements audit, the operational audit, and the compliance audit. A financial statement audit (or attest audit) examines financial statements, records, and related operations to ascertain adherence to generally accepted accounting principles. An operational audit examines an organization's activities in order to assess performances and develop recommendations for improvements, or further action. Auditors perform statutory audits which are performed to comply with the requirements of a governing body, such as a federal, state, or city government or agency. A compliance audit has as its objective the determination of whether an organization is following established procedures or rules.

the auditor's final report to management often includes recommendations on methodologies of improving internal controls that are in place.

advances in information technology in conjunction with real-time approaches to conducting business are challenging the auditing profession.

emphasis has historically been placed on a periodic, backward-looking approach whereby key events and activities are often identified long after their occurrence or simply undetected. Given that recent developments and technologies facilitated a movement away from the historical paradigm and toward a more proactive approach, it is essential that auditors understand what the future audit entails and how they might begin to envision a logical progression to such a state

Furthermore, refinements of audit standards generally consisted of reactionary measures that occurred in response to significant negative business events.

it enables people to contribute data about them to it and, on a case-by-case basis, people can choose to permit third parties to access that data. This is the pattern that many personal data stores and personal data management systems adopt in holding data and enabling users to unlock new apps and services that can plug into it. Health Bank enables people to upload their medical records and other information like wearable readings and scans to share with doctors or ‘loved ones’ to help manage their care; Japan’s accredited information banks might undertake a similar role. Other examples — such as Savvy and Datacoup — seem to be focused on sharing data with market research companies willing to offer a form of payment. Some digital identity services may also conform to this pattern.

it enables people to contribute data about them to it and, on a case-by-case basis, people can choose whether that data is shared with third parties as part of aggregate datasets. OpenHumans is an example that enables communities of people to share data for group studies and other activities. Owners of a MIDATA account can “actively contribute to medical research and clinical studies by granting selective access to their personal data”. The approach put forward by the European DECODE project would seem to support this type of individual buy-in to collective data sharing, in that case with a civic purpose. The concept of data unions advocated by Streamr seeks to create financial value for individuals by creating aggregate collections of data in this way. Although Salus Coop asks its users to “share and govern [their] data together.. to put it at the service of collective return”, it looks as though individuals can choose which uses to put it to.

it enables people to contribute data about them to it and decisions about what third parties can access aggregate datasets are taken collectively. As an example, The Good Data seeks to sell browsing data generated by its users “entirely on their members’ terms… [where] any member can participate in deciding these rules”. The members of the Holland Health Data Cooperative would similarly appear to “determine what happens to their data” collectively, as would drivers and other workers who contribute data about them to Workers Info Exchange.