Group items tagged

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”

Over the centuries, libraries, archives, and museums have shown the practical and policy advantages of preserving sources of knowledge for society. Research and other types of data constitute a relatively new subject that requires our serious attention. Although some research data repositories were founded in the 1960s and even earlier, the data that are now being generated have resulted in the establishment of many new repositories and related infrastructure. Societies need such repositories to ensure that the most useful or unique data are preserved over the long term.

First, there are substantial and positive efficiency impacts, not only reducing the cost of conducting research, but also enabling more research to be done, to the benefit of researchers, research organisations, their funders, and society more widely

substantial additional reuse of the stored data, with between 44% and 58% of surveyed users across the studies saying they could neither have created the data for themselves nor obtained them elsewhere.

While these studies tend to provide a snapshot of the repository's value, which can be affected by the scale, age and prominence of the data repository concerned, it is important to note that in most cases, data archives are appreciating rather than depreciating assets. Most of the economic impact is cumulative and it grows in value over time, whereas most infrastructure (such as ships or buildings) has a declining value as it ages. Like libraries, data collections become more valuable as they grow and the longer one invests in them, provided that the data remain accessible, usable, and used.

Openness of public information strengthens freedom and democratic institutions by empowering citizens, and supporting transparency of political decision-making and trust in governance. It is no coincidence that the most repressive regimes have the most secretive institutions and activities (Uhlir, 2004). Open factual datasets also enhance public decision-making from the national to the local levels (Nelson, 2011), and open data policies demonstrate confidence of leadership and generally can broaden the influence of governments (Uhlir and Schröder, 2007). Countries that may be lagging behind socioeconomically frequently can benefit even more from access to public data resources (NRC, 2012b, 2002).

The survey of repositories undertaken for this and the previous RDA-WDS study classified the principal research data repository revenue sources as follows: • Structural funding (i.e. central funding or contract from a research or infrastructure funder that is in the form of a longer-term, multi-year contract). We use the term “structural” to underline the difference between this and project funding. The research data repository is considered as a form of research infrastructure or as providing an ongoing service. Although the funding may be regularly reviewed, it is a form of funding that is substantively different to project funding.

Host institution funding and support (i.e. direct or indirect support from a host institution). Some research data repositories are hosted by a research performing institution, e.g. a university, and receive direct funding or indirect (but costed) support from their host. • Data deposit fees (i.e. in the form of annual contracts with depositing institutions or per-deposit fees). As indicated, this can take the form of a period contract or a charge per deposit. In either case, the cost is borne by the entity that wishes to ensure that the data are preserved and curated for the long term. • Access charges (i.e. charging for access to standard data or to value-added services and facilities). This covers charges of various sorts (e.g. contract or per-access charges) and can be levied either for standard data or value-added services. In all cases, the cost is borne by the entity that wishes to access and use the data. • Contract services or project funding (i.e. charges for contract services to other parties or for research contracts). This covers short-term contracts and projects for various activities not covered above (i.e. these are not contracts to deposit or access data, but cover other services that may be provided). Similarly, this category of funding is distinct from structural funding because, although it may come from a research or infrastructure funder, it is for specific, time- and objective-limited projects, rather than for ongoing services or infrastructure.

The 47 data repositories analysed reported 95 revenue sources, an average of two per repository. Twenty-four repositories reported funding from more than one source, and seven reported more than three revenue sources. Combining revenue sources is an important element in developing a sustainable research data infrastructure.

A large majority (more than 80%), said they would not be considering any revenue sources that are incompatible with the open data principle.

The stage of development of a repository, its institutional or disciplinary context, its scale, and level of federation are also important determinants of what might be a sustainable business model. Referring to the dynamic of the evolution of firms, some economists draw a human parallel, talking of the phases as births, deaths, and marriages (and sometimes divorces). All phases are needed and should be accommodated. Indeed, sometimes it may not be desirable, effective, or efficient for a repository to be sustainable - provided that the data can continue to be hosted elsewhere.

This is the situation facing research data repositories. To be sustainable, data repositories need to generate sufficient revenue to cover their costs, but setting a price above the marginal cost of copying and distribution will reduce net welfare

Actions needed to develop a successful research data repository business model include: • Understanding the lifecycle phase of the repository's development (e.g. the need for investment funding, development funding, ongoing operational funding, or transitional funding) • Identifying who the stakeholders are (e.g. data depositors, data users, research institutions, research funders, and policy makers) • Developing the product/service mix (e.g. basic data, value-added data, value-added services and related facilities, and contract and research services) • Understanding the cost drivers and matching revenue sources (e.g. scaling with demand for data ingest, data use, the development and provision of value-adding services or related facilities, research priorities, and policy mandates) • Identifying revenue sources (e.g. structural funding, host institutional funding, deposit-side charges, access charges, and value-added services or facilities charges ) • Making the value proposition to stakeholders (e.g. measuring impacts and making the research case, measuring value and making the economic case, informing, and educating) (Figure 6).

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.)

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.

While it will continue to be important for NSIs to increase their use of administrative data and the many sources of Big Data, these will rarely be able to be used as stand-alone sources. More often these sources will need to be combined with well-designed survey data to produce a blended, improved product.

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

As a result, the AICPA issued Statement on Auditing Procedure (SAP) No. 1 in October 1939 and it required that auditors inspect inventories and confirm receivables. Consequently, auditors became responsible for auditing the business entity itself rather than simply relying upon management verification routines.

First, in 1961 Felix Kaufman wrote Electronic Data Processing and Auditing. The book compares auditing around and through the computer. Historically, auditing around the computer entails traditional manual procedures in which the existence of automated equipment is ignored. As such, the computer is treated as a black box. In this context, auditors rely upon physical inputs to and outputs from automated devices and do not concern themselves with how processing actually occurs within the system(s). Conversely, auditing through the computer involves actual use of computer systems in testing both controls and transactions. Finally, auditing with the computer entails direct evaluation of computer software, hardware, and processes. Consequently, auditing through the computer or with the computer is able to provide a much higher level of assurance when contrasted with auditing around the computer.

Although some aspects of the traditional audit will continue to hold value, the audit of the future provides opportunities to increase the use of automated tools and remains a key for offering improved assurances relative to the responsible management and utilization of stakeholder assets.

As previously mentioned, basic CAATS contain capabilities to enhance audit effectiveness and efficiency. However, they do not operate on a 24/7 basis and therefore fail to construct a truly continuous auditing environment whereby exceptions and anomalies may be identified as they occur. Alternatively stated, they do not work with real-time or close to real-time data streams and, thus, are not able to address questionable events such as potential fraud or irregularities in an optimized fashion. Cangemi (2010) argues that, given the recent advances in business technologies, the continuing emphasis on the backward looking audit is simply an outdated philosophy. Instead, he believes that real-time solutions are needed.

Future audit approaches would likely require auditors, regulators, and standards setters to make significant adjustments. Such adjustments might include (1) changes in the timing and frequency of the audit, (2) increased education in technology and analytic methods, (3) adoption of full population examination instead of sampling, (4) re-examination of concepts such as materiality and independence, and (5) mandating the provisioning of the audit data standard. Auditors would need to possess substantial technical and analytical skills