How Networks of Citizen Observatories Can Increase the Quality and Quantity of Citizen-Science-Generated Data Used to Monitor SDG Indicators

Round 1

Reviewer 1 Report

This manuscript presents an interesting analysis of how distributed observatories can contribute to sustainable development goals. There is some general vagueness around the connection between these distributed network of observatories and necessary educational/awareness/motivation outcomes of citizens contributing observations in these spaces. The linkages between data contribution and broader outcomes related to environmental education and/or translation to motivation/action is much more vague than the authors present here. It would be helpful to have a more thought out model or identification of the mechanism that would drive these outcomes as a result of engagement with these observatories beyond, or in line with, traditional citizen science programs. In other words, yes the improved data as a result of contributions is good, but the authors point to this additional benefit of educational/behavioral outcomes in line with SDG, but not a clear pathway as to how or why in these spaces other than the assumption that other projects show some gains in these spaces so therefore so must we.

Author Response

Dear reviewer,

Thank you for taking the time to read and feedback on our manuscript.

Your constructive criticisms have allowed us to make important additions to the paper, and we hope that you find the manuscript much improved.

Please see below our specific responses to your comments and where you can find these edits to the manuscript.

We have also made efforts to improve the grammar of the paper throughout.

 

This manuscript presents an interesting analysis of how distributed observatories can contribute to sustainable development goals.

We thank the reviewer for finding the manuscript interesting.

There is some general vagueness around the connection between these distributed network of observatories and necessary educational/awareness/motivation outcomes of citizens contributing observations in these spaces. The linkages between data contribution and broader outcomes related to environmental education and/or translation to motivation/action is much more vague than the authors present here. It would be helpful to have a more thought out model or identification of the mechanism that would drive these outcomes as a result of engagement with these observatories beyond, or in line with, traditional citizen science programs. In other words, yes the improved data as a result of contributions is good, but the authors point to this additional benefit of educational/behavioral outcomes in line with SDG, but not a clear pathway as to how or why in these spaces other than the assumption that other projects show some gains in these spaces so therefore so must we.

We thank the reviewers for pointing out the need to explain further the mechanism through which we seek to make a more meaningful contribution to education through this project.

We have supported and implemented a number of case studies of educational activities and projects based on specific educational scenarios. These case studies were implemented and evaluated, and the data analysis so far provides rich evidence of additional educational benefits from the integration of the project's observatories into school practice, apart from those gained from traditional citizen science programs.

We have included this information, lines 521 - 552.

With these additions we think that the process through which we have enabled and supported educational activities based on the use of the project's technologies is now better understood

Reviewer 2 Report

The current article discusses the potential of citizen science in monitoring the indicators of sustainable development goals. The authors used the example of Cos4Cloud project as an example and Pl@ntNet as a technological service app used in the study. There is a great lack of connectivity between these two, introduction and results which makes it difficult to understand the ms. I also have some additional queries, which may be considered while revising the ms. 
I understand that Pl@ntNet is a technological service that could support the public to identify plants in a natural ecosystem. What are the scientific interventions made, to support the identification of a new species, if a citizen scientist uploads an image which is not available in the library? Pl@ntNet being a tool for the identification of plants, there is every chance of biased use by citizens. They may take images of selected plants that they would like to identify and may not contribute to addressing the diversity of plants in the area. How this limitation of the app is considered. Are there any specifications on the camera and image resolution for the picture of plants? It is not clear, how the current application contributes to enriching the quality of environmental education and sustainability. Does this application aim at making any additional interpretations on environmental pollution, sustainability, climate change based on the types of plants growing in an area?
More details of the development of pl@ntNet and image analysis may be provided. How did the data generated by citizen scientists are validated?

Author Response

Dear reviewer,

Thank you for taking the time to read and feedback on our manuscript.

Your constructive criticisms have allowed us to make important additions to the paper, and we hope that you find the manuscript much improved.

Please see below our specific responses to your comments and where you can find these edits to the manuscript.

We have also made efforts to improve the grammar of the paper throughout.

 

The current article discusses the potential of citizen science in monitoring the indicators of sustainable development goals. The authors used the example of Cos4Cloud project as an example and Pl@ntNet as a technological service app used in the study. There is a great lack of connectivity between these two, introduction and results which makes it difficult to understand the ms.

Thank you for highlighting this weakness within the manuscript.

We have now added a paragraph to the introduction which helps to clarify the connections between citizen science, citizen observatories and Cos4Cloud; the services being developed within Cos4Cloud (including Pl@ntNet and Cos4Bio) and how through interoperability, co-design and education, these services can support citizen observatories, exemplified via case studies.

This information has been added lines 122-133

The subtitles of the results section have also been edited to better reflect this.

I also have some additional queries, which may be considered while revising the ms. I understand that Pl@ntNet is a technological service that could support the public to identify plants in a natural ecosystem. What are the scientific interventions made, to support the identification of a new species, if a citizen scientist uploads an image which is not available in the library?

Thank you for this question which allows us to clarify how this service would work in such a case.

When an image of a species which is not yet illustrated in Pl@ntNet is submitted, the AI plant identification model usually provides a “no result” message to the citizen scientist. The citizen scientist can then share their plant observation without any species name. In this instance, more experienced botanists would have to suggest the most probable plant species name visible on the image, which would then need to be validated by the users’ network community. This mechanism has thus allowed for the expansion of 800 plant species illustrated in the first version of the Pl@ntNet mobile app (launched in February 2013), to more than 38 000 plant species in the actual version.

This information has been added, lines 263 - 271

Pl@ntNet being a tool for the identification of plants, there is every chance of biased use by citizens. They may take images of selected plants that they would like to identify and may not contribute to addressing the diversity of plants in the area. How this limitation of the app is considered.

This is indeed an important issue. Thank you for allowing us to answer it.

The encouragement of users to produce and share observations of interest for biodiversity studies is carried out thanks to the partnership with many NGOs (local and national ones) and scientific partners (universities, botanical gardens, research organisations) with which Pl@ntNet collaborates. These actors, who promote a better knowledge and protection of biodiversity, encourage the networks of users they train to implement good practices for identification, but also to contribute to the less known and more interesting species for scientific purposes. This encouragement is formulated both during face-to-face training sessions and in the communication materials that they distribute.

This information has been added, lines 369 - 377

Are there any specifications on the camera and image resolution for the picture of plants?

The most relevant types of views to achieve the best recognition rates are indicated in visual aids within the application. Pictures should be centred on individual specimens, avoiding strong contrast, as well as the presence of visual noise (such as fingers, or any manufactured objects).

This information has been added, lines 248 - 250

Regarding the size and resolution of the images, they are reduced at 1200 pixels along their longest side, and at a resolution of 72 dots per inch before being sent from the mobile apps.

This information was not considered integral to the manuscript and has not been added.

It is not clear, how the current application contributes to enriching the quality of environmental education and sustainability.

As we state in the manuscript, “the main contribution of Cos4Cloud to SDG monitoring is via networking; strengthening, enriching, and multiplying the work offered by each citizen observatory alone. This networking will be enabled by: (1) improved interoperability; (2) the use of co-design; and (3) education and learning for sustainable development. These three elements are discussed as case studies in the following section”.

The first case study, Pl@ntNet, is an example of improved interoperability, which improves data use between citizen observatories and therefore, supports progress towards the SDGs. The third case study focuses on education.

Nevertheless, we can provide examples of uses of Pl@ntNet in environmental education. Indeed, many teachers of secondary schools in Europe use Pl@ntNet in the framework of educational trainings, to identify biodiversity (whether it is urban biodiversity in the city, or species in the countryside). By facilitating access to species identification, participants quickly discover a large number of species around them, the contributions they make (indicated in the application) while being able to analyse the criteria that differentiate them from the closest visually similar species. By training students to use Pl@ntNet, they become autonomous to continue discovering their environment outside of school activities, allowing them to characterize the diversity of the environments they explore.

This information has been included in the third case study (education), lines 506 - 510

Does this application aim at making any additional interpretations on environmental pollution, sustainability, climate change based on the types of plants growing in an area?

The application does not provide such interpretations. It is the data it collects that contributes to these interpretations, as can be seen from the 154 publications that have used Pl@ntNet data shared on GBIF (https://www.gbif.org/resource/search?contentType=literature&publishingOrganizationKey=da86174a-a605-43a4-a5e8-53d484152cd3)

This information has been added, lines 360 - 363

More details of the development of pl@ntNet and image analysis may be provided. How did the data generated by citizen scientists are validated?

Pl@ntNet app allows plant names – common and scientific - to be shared in free text form. However, the validation of a plant observation is based on both the evaluation of the image quality, and the species identification. The scientific name of a species can only be validated if (i) the name suggested by the citizen scientist is registered in one of the taxonomic reference lists provided by the Pl@ntNet data providers (for example, Kew Gardens); and (ii) scientists with a high confidence score on their user profile, validate the initial determination proposal of the citizen scientist.

This information has been added, lines 342 - 348

Round 2

Reviewer 2 Report

All questions are addressed in the revised ms. The current form of ms is recommended for publication