Group items tagged

Data that could drive new discoveries or innovation is not always as available as it could be.

make the most of PSREs, which have the potential to deliver broad public policy objectives and help innovation translation enable work across institutions to solve the grand challenges of our time make the most of our institutions, to use research to improve both UK and devolved policy outcomes and to measure and refine programme performance

Crucially, we must embrace the potential of open research practices. First, we will require that research outputs funded by the UK government are freely available to the taxpayer who funds research. Such open publication will also ensure that UK research is cited and built on all over the world. We will mandate open publication and strongly incentivise open data sharing where appropriate, so that reproducibility is enabled, and knowledge is shared and spread collaboratively. Second, we will ensure that more modern research outputs are recognised and rewarded. For example, we will ensure that digital software and datasets are properly recognised as research outputs, so that we can minimise efforts spent translating digital outputs into more traditional formats. Third, we will consider the case for new infrastructure to enable more effective sharing of knowledge between researchers and with industry to accelerate open innovation where possible.

PSREs and other publicly funded research institutes Public sector research establishments (PSREs) and other publicly funded institutes – including UKRI-funded institutes and institutes funded by the devolved administrations, are a diverse collection of bodies carrying out research. This research supports government objectives, including informing policy-making, statutory and regulatory functions and providing a national strategic resource in key areas of research. They can also provide emergency response services. They interact with businesses around a wide array of innovation-related functions. We want to get the most value out of the whole range of PSREs and publicly funded institutes, laboratories and campuses. The current PSRE and institute landscape is complex. There is an opportunity to raise awareness and support development of strategic national laboratory capability, develop closer relationships across the network of PSREs and institutes to address cross-cutting priorities and develop more consistent and co-ordinated, accessible funding for PSREs. Most programmes do not include funding for the full costs of overheads – this sometimes prevents our national laboratories from participating in UK government priority programmes without making a loss. A more flexible approach and funding a higher proportion of the economic costs would increase spending efficiency and encourage more effective investments and maximise their benefits. Building on the 2019 Science Capability Review, we will: champion the development of a truly strategic, national laboratory capability and identify opportunities to strengthen their capabilities and ability to collaborate, especially with the private sector, devolved administrations and local civic authorities work to understand current capacity and capability, including spare capability, and to ensure that national labs, PSREs and other publicly funded institutes are working together as part of business as usual rather than only in times of crisis explore the potential for all PSREs to have access to more funding opportunities from UKRI so that PSREs are viewed as national assets rather than the property of UK government departments

it enables people to contribute data about them and defer authority to it to decide who can access the data. A high-profile proposal of this pattern comes in the form of ‘bottom-up data trusts’ — Mozilla Fellow Anouk Ruhaak has described scenarios where multiple people “hand over their data assets or data rights to a trustee”. Some personal data stores and personal information management systems will also operate under this kind of delegated authority within particular parameters or settings.

people entrust it to mediate their relationships with services that collect data about them. This is more related to decisions about data collection rather than decisions about access to existing data, but involves the stewardship of data nonetheless. For example, Tom Steinberg has described a scenario whereby “you would nominate a Personal Data Representative to make choices for you about which apps can do what with your data.. [it] could be a big internet company, it could be a church, it could be a trade union, or it could be a dedicated rights group like the Electronic Frontier Foundation”. Companies like Disconnect.Me and Jumbo are newer examples of this type of approach in practice.

it enables people to collect or create new data. Again, this pattern describes the collection rather than the re-use of existing data. For example, OpenBenches enables volunteers to contribute information about memorial benches, and OpenStreetMap does similar at much larger scale to collaboratively create and maintain a free map of the world. The ODI has published research into well-known collaboratively maintained datasets, including Wikidata, Wikipedia and MusicBrainz, and a library of related design patterns. I’ve included this pattern here as to me it represents a way for people to be directly involved in the stewardship of data, personal or not.

it collects data in providing a service to users and, on a case-by-case basis, users can share that data directly with third parties. This pattern enables users to unlock new services by sharing data about them (such as via Open Banking and other initiatives labelled as ‘data portability’), or to donate data for broader notions of good (such as Strava’s settings that enable its users to contribute data about them to aggregate datasets shared with cities for planning). I like IF’s catalogue of approaches for enabling people to permit access to data in this way, and its work to show how services can design for the fact that data is often about multiple people.

it collects data by providing a service to users and shares that data directly with third parties as provisioned for in its Terms and Conditions. This typically happens when we agree to Ts&Cs that allow data about us to be shared with third parties of an organisation’s choice, such as for advertising, and so might be considered a ‘dark’ pattern. However, some data collectors are beginning to do this for more public, educational or charitable purposes — such as Uber’s sharing of aggregations of data with cities via the SharedStreets initiative. Although the only real involvement we have here in stewarding data is in choosing to use the service, might we not begin to choose between services, in part, based on how well they act as data institutions?

I echo the point that Nesta recently made in their paper on ‘citizen-led data governance’, that “while it can be useful to assign labels to different approaches, in reality no clear-cut boundary exists between each of the models, and many of the models may overlap”

The collection of observation data is a global effort. The Global Observing System consists of around 11,000 ground-based monitoring stations supplemented with thousands of sensors installed on weather balloons, aircraft and ships. 3 Observations are also collected from a network of radar installations and satellite-based sensors. As we see later, the ‘official’ observation system is increasingly being supplemented with new sources of observation data from the Internet of Things (IoT).

Ensemble model forecasts aim to give an indication of the range of possible future states of the atmosphere and oceans (which are a significant driver of the weather in the atmosphere). This overcomes errors introduced by using imperfect measurement of initial starting conditions that are then amplified by the chaotic nature of the atmosphere. Increasing the number of forecast members over a global scale and at higher resolutions result in data volumes increasing exponentially .

Created in 1950, The World Meteorological Organisation (WMO) is made up of 191 member states and territories around the world. The WMO was founded on the principle that global coordination was necessary to reap the benefits of weather and climate data. This includes a commitment to weather data and products being freely exchanged around the world (Obasi, 1995).

While the WMO has a global outlook, its work is supplemented by regional meteorological organisations like the European Centre for Medium Range Weather Forecasts (ECMWF) and NMSs, such as the Met Office in the UK

There are increasing new sources of weather observation data. In recent years, services like Weather Underground and the Met Office’s Weather Observation Website have demonstrated the potential for people around the world to contribute weather observations about their local areas – using low-cost home weather stations and sensors, for example. But there is now potential for sensors in cities, homes, cars, cell towers and even mobile phones to contribute observational data that could also be fed into forecast models.

Accreditation is a means of assessing, in the public interest, the technical competence and integrity of organisations offering evaluation services.Accreditation, with its many potential benefits for the quality of goods and in the provision of services throughout the supply chain, underpins practical applications of an increasingly wide range of activities across all sectors of the economy, from fishing to forestry, construction to communications. Independent research has confirmed that accreditation has a positive economic value of nearly £1bn on the UK economy each year. 

Some serial numbering systems include built in error-checking to deal with copying errors, using a check digit.

The second way of providing unique identifiers is using a name or code.

These are typically still assigned by a central authority, sometimes known as a registration agency, but they are constructed in different ways.

Encoding information about geography and hierarchy within codes can be useful. It can make them easier to validate.

It also mean you can also manipulate them,

But encoding lots of information in identifiers also has its downsides. The main one being dealing with changes to administrative areas that mean the hierarchy has changed. Do you reassign all the identifiers?

some identifier systems look at reducing the burden on that central authority.

federated assignment. This is where the registration agency shares the work of assigning identifiers with other organisations.

Another approach to reducing dependence on, and coordination with a single registration agency, is to use what I’ll call “local assignment“.

A simplistic approach to local assignment is “block allocation“: handing out blocks of pregenerated identifiers to organisations which can locally assign them.

Here the registration agency still generates the identifiers, but the assignment of identifier to “thing” is done locally.

A more common approach is to use “prefix allocation“. In this approach the registration agency assigns individual organisations a prefix within the identifier system.

One challenge with prefix allocation is ensuring that the rules for locally assigned suffixes work in every context where the identifier needs to appear.

In distributed assignment of identifiers, anyone can create an identifier. Rather than requesting an identifier, or a prefix from a registration agency, these systems operate by agreeing rules for how unique identifiers can be constructed.

A hash based identifier takes some properties of the thing we want to identify and then use that to construct an identifier. 

Diamond diversity data is collected via the online platform Silvermouse, which is already used by many broadcasters and production companies to collect and manage other kinds of information about the programmes they make.

Diamond does not collect: • Data from programmes which have not been commissioned by the five Diamond broadcasters • Data on people working across broadcasting more generally, outside of production (in other words, our data are not overall workforce statistics ) • Data where it is impractical to do so and where relevant privacy notices cannot be given. (Diamond does not collect data from every person appearing on television as part of a crowd scene, for example.)