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But are these fundamental principles still relevant in the changed data environment? Maintaining the principles of official statistics involves many challenges in the new data landscape. These include definitional considerations such as concerning the selection of statistical units, data items and their associated spatio-temporal nature. The major paradigm leap for official statistics is how established methodologies of data collection and statistical time series can be adapted in the context of a deluge of unstructured, private (and potentially anonymized) data recorded on electronic devices. Phones, computers and other devices lend themselves readily to standardization and the possibility of replication and scale. For statistics to function optimally, though, standards are needed. Statisticians, technology gurus and data purveyors will need to navigate this space carefully as the hard and laborious slog of carefully designed statistical operations faces a real existential challenge of easy-to-use and fashionable observational tools. Statistics – and its essence, time series – can be greatly challenged in this new environment. The truth is, in the absence of time series, all data can be rendered useless. If a zealous adaptation of technology results in the loss of time series data, it will become impossible to track trends over time. The intersection of technology, data, statistics, knowledge, finance and governance needs to be found.


25.3.6 Data assurance and quality practices


With the increasing use of complementary data alongside traditional statistics to support environmental and sustainability policy, questions of how data quality, pedigree and provenance can be assured will need to be systematically answered to determine data that are credible and fit for purpose. Environmental data may come to include digital sources, incorporating Earth observations, citizen science, environmental monitoring, development data and statistics, administrative data sets, and population- and survey-derived data.


In addition to the fundamental principles of statistical practice mentioned above, references and standard practice documents are also emerging for complementary data sources. For example, metadata standards and practices now serve as a basis for data description, including methodological description and data quality. Metadata – the documentation of data – serves the purpose of making data discoverable, usable and understandable. Many discipline-specific or community- specific metadata standards have been developed to support systems of data management and data discovery, and to capture and convey information to users. Examples include directory interchange format (DIF), ecological metadata language (EML), sensor model language (SensorML), climate science modelling language (CSML), and netCDF markup language (NcML). Additionally, the International Organization for Standardization (ISO) has developed a series of standards to describe geographical information – ISO 19115 and ISO 11179.


Data-quality assurance for citizen-science data is in its formative stages, engaging a variety of digital-platform techniques for quality checking field observations contributed by citizens. Examples include the Local Environmental Observer (LEO) Network in the North American Arctic region, using a smartphone app that uploads observations for expert checks before being used in graphic displays on maps and


The ultimate test for the data revolution of open and accessible digital data will be user satisfaction and integrated platform requirements for aligning with an array of recognized standards, practices and open source community-driven testing for methodology and data quality from across environmental, natural resource and development data.


25.4 Conclusion: Challenges, gaps and opportunities


The challenges, gaps and opportunities related to environmental data and statistics are presented below. Data and knowledge are valuable assets that need to be shared.


25.4.1 Data disaggregation


The SDGs call for a data revolution that leaves no one behind, incorporating disaggregated data and reporting at all levels of the 17 goals. As highlighted in Chapter 3 of this report, assessing the nexus between society and the environment can be done only if there is disaggregated information on different populations because not all people have the same level of dependence on the environment, nor the same impact on it. To tease out these differences, then, there is a need for information that can be disaggregated by income, gender, age, ethnicity, migratory status, disability, geographical location and other characteristics relevant in national contexts. Unfortunately, there is currently a dearth of environment- related information that can be disaggregated, and data from household surveys on access to water, energy and other natural resources is available only at the household level, which makes understanding differences at other levels difficult.


In addition to disaggregation by socioeconomic variables there is also a need for geospatial disaggregation of environmental information. Biological ecosystems do not follow national boundaries, so to understand both the state of particular ecosystems and the interactions between them and people


Future Needs for Data and Knowledge 615


in tabular data sets (LEO Network 2017). Recent workshops convened by the European Commission’s Joint Research Centre are also beginning the process of establishing principles for mobile apps and platforms.


The Group on Earth Observations has propagated its Global Earth Observation System of Systems (GEOSS) data management principles, which are being widely adopted by Earth Observation entities (Group on Earth Observations 2014). Included in the coverage of the principles are: discoverability, accessibility, usability, preservation and curation.


As open-source analytics, community-sourced query codes and custom data-integration methods advance, there will be more community curation of data sets, exchange standards and application-programming interfaces. Code sets incorporating analytics and queries are now routinely community-curated on open collaboration platforms such as the GitHub development platform.


The Research Data Alliance sets registry standards for long- term curation and for defining the parameters of data sets in Earth science and other research domains. Complementary data sources will be judged to be credible as far as they conform with these registry standards.


25


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