maintenance of Citizen Observatories generate data that need to be managed in a way that
allows for discovering and accessing but also preserving and curating it. Good data management principles and practices maximise the value and benefit of data by ensuring that data remain robust, useful, up to date, understandable and long-lasting for our research purposes and for future uses. This will ensure that data from different origins and types can be integrated into scientific models and eventually generate applications to derive decision support tools.
You can adopt a set of data management principles for your Citizen Observatory, such as those developed and adopted by GEOSS, to enhance discoverability, accessibility, usability, curation and secure preservation of the data. This involves the elaboration of a data management plan – a time-consuming process that forces you to anticipate required practices and to recognise the need to plan for the resources to put the plan into practice. Below you can find a set of data management principles and practices that will help you to manage your data in the most effective and appropriate way.
standards. To avoid losing information and creating confusion, metadata should be produced from the start.
Data access: Data should not be kept in silos but should be accessible via online services, including, at minimum, direct download but preferably user-customisable services for visualisation and computation. Do not wait until your data is perfect. Instead, data should be made available in advance of quality control and agged in metadata as unchecked. Afterwards, quality-controlled data and the results of quality control will also be shared. The conditions for use, including licenses, should be decided upon and clearly included in the metadata that describes the data. Moreover, the use conditions of sensitive information (e.g. location of endangered species) need to be carefully chosen and indicated.
Data format: Data should be distributed using encodings that are widely accepted in the target user community. The use of open standards will lower the access barrier.
The generation of data should be guided by scientists and eventually exposed in scienti c peer-reviewed publications that describe the origin and processing history of raw observations and derived products and their many results and outcomes. During this process, persistent, resolvable identi ers should be assigned to the data.
TOOL: easyDMP is a web-service that allows a user to create, share and manage data management plans by guiding the researcher through a set of
questions tailored to the recommendations of di erent funding agencies and research authorities. The resulting
document can then be attached to the users proposal.
SCIENTIFIC PAPER: The paper
“Citizen Science 2.0: Data Management Principles to
Harness the Power of the Crowd”
addresses the challenges for engaging citizen scientist in the context of research projects.
SCIENTIFIC PAPER: The “Study on the Data Management of
Citizen Science: From the Data Life Cycle Perspective”, Data and Information Management”
analyses the lifecycle and data management processes of over 1000 citizen science and Citizen Observatory projects, identifying common themes and best
practices.
Data discoverability: To make data discoverable, metadata about the data should be elaborated and made public in a catalogue for search engines to nd it. Metadata should also state how data should be accessed, used, understood and processed, preferably via formal, structured metadata based on open
Acknowledgement: Data contributors should receive acknowledgement for the use of their data if they express a desire for that. Personal information should be kept secure and managed in conformance with the GDPR.
Data should be periodically veri ed to ensure integrity, authenticity and readability. Data should be kept up to date in accordance with reviews, and reprocessed as needed.
Elaborate data management plan
Managing Citizen Observatory data should begin by planning the processes and steps for managing data: from the collection of data; the data model used; the tools needed to collect it; the metadata recorded; the means for storing, sharing and accessing it; and the visualisation, reuse and preservation of the data. You can do this by drafting a Data Management Plan (DMP).
A DMP must also take into account a common issue in citizen science projects:
the treatment of personal and sensitive information, which in this case can come from the collection of personal data or the location of people, protected species or private properties. The need for privacy should be exible, allowing citizens to opt in for programmes that track authorship in the data collection.
Authorship is used in quality control estimations or to give acknowledgement for published contributions.
SCIENTIFIC PAPER: The Advice Note 1 from UKEOF’s series of Data Advice Notes highlights the principles of good data and information management, and suggests policies and procedures for data managers.
STANDARD: The Data Standard for Public Participation in
Scienti c Research (PPSR Core) is a set of global,
transdisciplinary data and metadata standards for use in Public Participation in Scienti c Research (Citizen Science)
projects. PPSR Core is maintained by the Citizen Science
Association (citizenscience.org) working group for Data &
Metadata.
Curation: Data should be protected from loss and preserved for future use. The cost of preservation should not be underestimated and needs to be planned head. If the data curator cannot continue, transfer procedures should be aactivated.
Some useful tools are available to facilitate the creation of a DMP, both for Citizen Observatory data and for other types of data: for example, DMPTool, OpenAIRE ARGOS, easyDMP or DMPOnline, which also includes many real DMPs as concrete examples. Having a DMP in place will ensure that you think about data management-related issues from the start. This way, you will be prepared and know subsequent budget needs.
Elaborate metadata for your data
Another good practice in data management is the selection and provision of appropriate metadata for describing data (information about the data).
Providing adequate metadata both for the individual observations and for the overall data set will simplify sharing operations and allow data repositories to work together. This also helps scientists to understand the data collected and makes the data usable.
The work done in the Citizen Science Cost Action CA15212 has led to the definition of and evolution of the Data Standard for Public Participation in Scientific Research (PPSR Core), which includes metadata models for describing projects, datasets and observations.
Example from the GBIF initiative
The Global Biodiversity Information Facility (GBIF) GBIF.org is a good example of data management at full scale. Its associated services aggregates data from the GBIF network of participants and publishers (many of them being citizen science initiatives). Their data management rules and conventions support thousands of different datasets drawn from hundreds of institutions around the world. All of the descriptions of datasets in GBIF.org rely on metadata – that is, the information about data – using the open-source EML standard. Each Darwin Core Archive includes as one of its components an EML file. Common standards are the main enabler for bringing together the hundreds of millions of primary biodiversity records in the GBIF index.
…by ensuring data quality
…by sharing our Citizen Observatory data
…by integrating data from several Citizen
Observatories/other sources
You may also be interested in:
I want to work with data…
…by collecting data
A common concern about Citizen Observatories is uncertainty regarding the quality of the collected observations. Many organisations and researchers have been asking: Can citizens provide data that are of the same quality as professional scientists? Learning and implementing best practices on how to de ne and expose the data quality information will lead to reliable and trustworthy data sets and will minimise uncertainties regarding the collected data.
How can this be done?
Ensuring data quality should be done according to the speci c scienti c discipline(s) that your Citizen Observatory needs to draw on. This varies depending on which environmental issue you are focusing on. It requires the involvement of one or more trained scientists from those elds; they can help you design the appropriate scienti c methodology that ensures your data will be t for purpose (more on how to engage key stakeholders here).
Consider key elements of data quality
There are several key elements to ensuring data quality. Apart from the obvious, namely positional (locational) accuracy, other aspects to consider are completeness, consistency, thematic accuracy and temporal homogeneity. The ISO standard ISO 19157 provides a common vocabulary for data quality concepts. It was initially designed for cartography and includes a long list of standardised quality measurements and methodologies. It is also applicable for Citizen Observatories and citizen science.
Useful Resources
WEBINAR: “Ground Truth Week 2019 – Webinar 3.2 – Data quality and interoperability”
describes the capabilities of the quality tool developed in Ground Truth 2.0 and demonstrates how to use it in a real-life scenario.
BOOK CHAPTER: “Chapter 8 Data Quality in Citizen Science”
in the book “The Science of Citizen Science” discusses the broad and complex topic of data quality in citizen science and how we can ensure the validity and reliability of data generated by citizen scientists and citizen science projects.
WEBSITE: QualityML is a dictionary based on the ISO19157 that contains hierarchically
structured concepts to precisely de ne and relate quality levels:
from quality classes to quality measurements.