NSW GOVERNMENT DATA QUALITY STATEMENT: 04 NOVEMBER 2020 Name of dataset or data source: Bellingen Riverwatch - community water quality data 2017 to current Custodian of the dataset or data source: OzGREEN Description: Bellingen Riverwatch engages volunteer citizen scientists to test water quality at multiple sites across the Bellinger and Kalang to create a long-term data set which supports the recovery of the Critically Endangered Bellinger River Snapping Turtle and other threatened species. This dataset is a record of the data collected by community citizen scientists at a number of sites across the Bellinger and Kalang catchments. After a significant mortality event for the Bellinger River Snapping Turtle in 2015, scientists and the community identified a need for consistent and ongoing water quality testing in the area. Data quality rating: ★Institutional environment - 4 ★Accuracy - 4 ★Coherence - 5 ★Interpretability - 4 ☆Accessibility - 3 ★ ✔ ✔ ✔ ✔ ✗ INSTITUTIONAL ENVIRONMENT Very Good Does the information have the potential to enhance services or service delivery? Data governance roles and responsibilities are clearly assigned for the dataset or data source Data collection is authorised by law, regulation or agreement The Custodial agency has no commercial interest or conflict of interest in the data The data are collected and managed according to a Data Quality Framework ★ ✔ ✔ ✔ ✔ ACCURACY Very Good Data has been subject to a data assurance process (For example: Checking for errors at each stage of data collection and processing, or verifying data entry and making corrections if necessary.) Data is revised and the revision is published if errors are identified No changes have been made or other factors identified (for example: weighting, rounding, de-identification of data, changes or flaws in data collection or verification methods) that could affect the validity of the data; or any changes/factors have been identified in caveats attached to the asset. The data collection met the objectives of the primary user. The data correctly represents what it was designed to measure, monitor or report. DATA DISCLAIMER NSW Government is committed to producing data that is accurate, complete and useful. Notwithstanding its commitment to data quality, NSW Government gives no warranty as to the fitness of this data for a particular purpose. While every effort is made to ensure data quality, the data is provided “as is”. The burden for fitness of the data relies completely with the User. NSW ✗ There are no known gaps in the data or if there are gaps (for example: non-responses, missing records, data not collected), they have been identified in caveats attached to the dataset. ★ ✔ ✔ ✔ ✔ ✔ COHERENCE Excellent Standard definitions, common concepts, classifications and data recording practices been used. Elements within the data can be meaningfully compared. This data is generally consistent with similar or related data sources from the same discipline The data can be analysed over time (for example, there have not been any significant changes in the way items are defined, classified or counted over time). The data does not form part of a collection or, if it is the latest in a series of data releases, there have not been any changes in methodology or external impacts since the last data release. ★ ✔ ✔ ✔ ✔ ✗ INTERPRETABILITY Very Good A data dictionary is available to explain the meaning of data elements, their origin, format and relationships Information is available about the primary data sources and methods of data collection (e.g. instruments, forms, instructions). Information is available to explain concepts, help users correctly interpret the data and understand how it can be used Information is available to explain ambiguous or technical terms used in the data Information is available to help users evaluate the accuracy of the data and any level of error ☆ ✔ ✔ ✔ ✗ ✗ ACCESSIBILITY Good Data is available online with an open licence Data is available in machine-processable, structured form (e.g. CSV format instead of an image scan of a table) Data is available in a non-proprietary format (e.g. CSV, XML) Data is described using open standards (e.g. RDF, SPARQL) and persistent identifiers (URIs or DOIs) Data is linked to other data, to provide context (e.g. employee ID is linked to employee name or species name is linked to genus) Government shall not be held liable for improper or incorrect use of the data. For more information about this dataset or data source, contact: OzGREEN Custodian email:
[email protected] The data quality statement aims to help you understand how a particular dataset could be used and whether it can be compared with other, similar datasets. It provides a description of the characteristics of the data to help you decide whether the data will be fit for your specific purpose. About the quality rating: The reporting questionnaire asks five questions for each of these data quality dimensions: Institutional Environment Accuracy Coherence Interpretability Accessibility For each question: “yes” = 1 point; “no” = 0 points The number of points determines the Quality Level for each dimension (high, medium, low). Only dimensions with four or five points receive a star. Points Quality Level Star / No Star 0 Poor No Star 1 Poor No Star 2 Fair No Star 3 Good No Star 4 Very Good Star 5 Excellent Star Quality relates to the data’s “fitness for purpose”. Users can make different assessments about the dataquality of the same data, depending on their “purpose” or the way they plan to use the data. The following questions may help you evaluate data quality for your requirements. This list is not exhaustive.Generate your own questions to assess data quality according to your specific needs and environment. What was the primary purpose or aim for collecting the data? How well does the coverage (and exclusions) match your needs? How useful are these data at small levels of geography? Does the population presented by the data match your needs? To what extent does the method of data collection seem appropriate for the information being gathered? Have standard classifications (eg industry or occupation classifications) been used in the collection of the data?If not, why? Does this affect the ability to compare or bring together data from different sources? Have rates and percentages been calculated consistently throughout the data? Is there a time difference between your reference period, and the reference period of the data? What is the gap of time between the reference period (when the data were collected) and the release date of thedata? Will there be subsequent surveys or data collection exercises for this topic? Are there likely to be updates or revisions to the data after official release? Understanding the Data Quality Statement Evaluating data quality
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