scholarly journals Evaluating the Fitness for Use of Citizen Science Data for Wildlife Monitoring

2021 ◽  
Vol 9 ◽  
Author(s):  
Heather A. Fischer ◽  
Leah R. Gerber ◽  
Elizabeth A. Wentz
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Ecological Monitoring ◽  
Global Scale ◽  
Third Party ◽  
Data Sets ◽  
Science Data ◽  
Spatial And Temporal Scales ◽  
Fine Grained ◽  
Assessment Approach

Contributory citizen science programs focused on ecological monitoring can produce fine-grained and expansive data sets across spatial and temporal scales. With this data collection potential, citizen scientists can significantly impact the ability to monitor ecological patterns. However, scientists still harbor skepticism about using citizen science data in their work, generally due to doubts about data quality. Numerous peer-reviewed articles have addressed data quality in citizen science. Yet, many of these methods are not useable by third-party scientists (scientists who are not directly involved in the citizen science program). In addition, these methods generally capture internal data quality rather than a dataset’s potential to be used for a specific purpose. Assessing data fitness for use represents a promising approach to evaluating data accuracy and quality for different applications and contexts. In this article, we employ a Spatial, Temporal, Aptness, and Application (STAAq) assessment approach to assess data fitness for use of citizen science datasets. We tested the STAAq assessment approach through a case study examining the distribution of caribou in Denali National Park and Preserve. Three different datasets were used in the test, Map of Life data (a global scale citizen science mobile application for recording species observations), Ride Observe and Record data (a program sponsored by the park staff where incentivized volunteers observe species in the park), and conventionally collected radio collar data. The STAAq assessment showed that the Map of Life and Ride Observe and Record program data are fit for monitoring caribou distribution in the park. This data fitness for use approach is a promising way to assess the external quality of a dataset and its fitness to address particular research or monitoring questions. This type of assessment may help citizen science skeptics see the value and potential of citizen science collected data and encourage the use of citizen science data by more scientists.

scholarly journals The Verification of Ecological Citizen Science Data: Current approaches and future possibilities

2021 ◽  
Vol 5 ◽  
Author(s):  
Emily Baker ◽  
Jonathan Drury ◽  
Johanna Judge ◽  
David Roy ◽  
Graham Smith ◽  
...  
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Applied Research ◽  
Automated Verification ◽  
Ecological Data ◽  
Science Data ◽  
Spatial And Temporal Scales ◽  
The Past ◽  
Published Research ◽  
Critical Process

Citizen science schemes (projects) enable ecological data collection over very large spatial and temporal scales, producing datasets of high value for both pure and applied research. However, the accuracy of citizen science data is often questioned, owing to issues surrounding data quality and verification, the process by which records are checked after submission for correctness. Verification is a critical process for ensuring data quality and for increasing trust in such datasets, but verification approaches vary considerably among schemes. Here, we systematically review approaches to verification across ecological citizen science schemes, which feature in published research, aiming to identify the options available for verification, and to examine factors that influence the approaches used (Baker et al. 2021). We reviewed 259 schemes and were able to locate verification information for 142 of those. Expert verification was most widely used, especially among longer-running schemes. Community consensus was the second most common verification approach, used by schemes such as Snapshot Serengeti (Swanson et al. 2016) and MammalWeb (Hsing et al. 2018). It was more common among schemes with a larger number of participants and where photos or video had to be submitted with each record. Automated verification was not widely used among the schemes reviewed. Schemes that used automation, such as eBird (Kelling et al. 2011) and Project FeederWatch (Bonter and Cooper 2012) did so in conjunction with other methods such as expert verification. Expert verification has been the default approach for schemes in the past, but as the volume of data collected through citizen science schemes grows and the potential of automated approaches develops, many schemes might be able to implement approaches that verify data more efficiently. We present an idealised system for data verification, identifying schemes where this hierachical system could be applied and the requirements for implementation. We propose a hierarchical approach in which the bulk of records are verified by automation or community consensus, and any flagged records can then undergo additional levels of verification by experts.

scholarly journals A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 875
Author(s):  
Jesus Cerquides ◽  
Mehmet Oğuz Mülâyim ◽  
Jerónimo Hernández-González ◽  
Amudha Ravi Shankar ◽  
Jose Luis Fernandez-Marquez
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Graphical Model ◽  
Real Life ◽  
Scientific Journals ◽  
Probabilistic Framework ◽  
Science Data ◽  
Label Aggregation ◽  
Evaluation Of Data ◽  
Model Formalism

Over the last decade, hundreds of thousands of volunteers have contributed to science by collecting or analyzing data. This public participation in science, also known as citizen science, has contributed to significant discoveries and led to publications in major scientific journals. However, little attention has been paid to data quality issues. In this work we argue that being able to determine the accuracy of data obtained by crowdsourcing is a fundamental question and we point out that, for many real-life scenarios, mathematical tools and processes for the evaluation of data quality are missing. We propose a probabilistic methodology for the evaluation of the accuracy of labeling data obtained by crowdsourcing in citizen science. The methodology builds on an abstract probabilistic graphical model formalism, which is shown to generalize some already existing label aggregation models. We show how to make practical use of the methodology through a comparison of data obtained from different citizen science communities analyzing the earthquake that took place in Albania in 2019.

scholarly journals The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

2021 ◽  
Vol 444 ◽  
pp. 109453
Author(s):  
Camille Van Eupen ◽  
Dirk Maes ◽  
Marc Herremans ◽  
Kristijn R.R. Swinnen ◽  
Ben Somers ◽  
...  
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Model Performance ◽  
Distribution Model ◽  
Science Data ◽  
Quality Filtering ◽  
The Impact

Three Frontiers for the Future of Biodiversity Research Using Citizen Science Data

BioScience ◽  
2020 ◽  
Author(s):  
Corey T Callaghan ◽  
Alistair G B Poore ◽  
Thomas Mesaglio ◽  
Angela T Moles ◽  
Shinichi Nakagawa ◽  
...  
Keyword(s):  
Citizen Science ◽  
Secondary Data ◽  
Science Research ◽  
Single Species ◽  
Data Sets ◽  
Biodiversity Data ◽  
Science Data ◽  
Biodiversity Research ◽  
Research Areas ◽  
The Future

Abstract Citizen science is fundamentally shifting the future of biodiversity research. But although citizen science observations are contributing an increasingly large proportion of biodiversity data, they only feature in a relatively small percentage of research papers on biodiversity. We provide our perspective on three frontiers of citizen science research, areas that we feel to date have had minimal scientific exploration but that we believe deserve greater attention as they present substantial opportunities for the future of biodiversity research: sampling the undersampled, capitalizing on citizen science's unique ability to sample poorly sampled taxa and regions of the world, reducing taxonomic and spatial biases in global biodiversity data sets; estimating abundance and density in space and time, develop techniques to derive taxon-specific densities from presence or absence and presence-only data; and capitalizing on secondary data collection, moving beyond data on the occurrence of single species and gain further understanding of ecological interactions among species or habitats. The contribution of citizen science to understanding the important biodiversity questions of our time should be more fully realized.

scholarly journals Citizen science monitoring reveals a significant, ongoing decline of the Endangered Carnaby's black-cockatoo Calyptorhynchus latirostris

Oryx ◽  
2015 ◽  
Vol 50 (4) ◽  
pp. 626-635 ◽  
Author(s):  
Matthew R. Williams ◽  
Colin J. Yates ◽  
William D. Stock ◽  
Geoff W. Barrett ◽  
Hugh C. Finn
Keyword(s):  
Citizen Science ◽  
Wildlife Conservation ◽  
Sampling Site ◽  
Significant Decline ◽  
Science Data ◽  
Spatial And Temporal Scales ◽  
Site Characteristics ◽  
Declining Species ◽  
Scientific Value ◽  
Survey Effort

AbstractCitizen science monitoring programmes are making increasingly important contributions to wildlife conservation, often at spatial and temporal scales unachievable by individual or teams of researchers. They are particularly valuable in estimating population trends and management impacts, and thus informing effective conservation decisions for declining species. The quality and potential biases of citizen science data are of concern, however, and appropriate experimental design and analysis are needed to ensure that the maximum scientific value is extracted. We investigated these issues in a citizen science survey of the Endangered Carnaby's black-cockatoo Calyptorhynchus latirostris. Since 2010, citizen scientists have conducted synchronized annual counts of Carnaby's black-cockatoo at roost sites to estimate the population trend. Survey effort was substantial, with c. 150 sites surveyed by > 260 volunteers each year. Relatively few sites were occupied, however, and only 42 (16%) of the 265 sites surveyed in total accounted for 95% of all observations. Many sites were empty and survey effort was often inconsistent. Taking these issues into account, analysis showed a statistically significant decline in roost occupancy rate and a non-significant decline in the mean size of roosting flocks, with an estimated overall trend of 14% decline per annum in the number of roosting birds. We highlight three important issues for citizen science monitoring programmes: the need to maintain regular surveys of sample sites to avoid patchy data, use an appropriate model that accounts for variable survey effort, high frequency of zero counts, and sampling site turnover, and incorporate information on site characteristics to help explain variation.

scholarly journals Perspectives on Citizen Science Data Quality

2021 ◽  
Vol 3 ◽  
Author(s):  
Robert R. Downs ◽  
Hampapuram K. Ramapriyan ◽  
Ge Peng ◽  
Yaxing Wei
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Quality Assessment ◽  
Citizen Science ◽  
Data Reuse ◽  
Quality Information ◽  
Science Data ◽  
Conducting Research

Information about data quality helps potential data users to determine whether and how data can be used and enables the analysis and interpretation of such data. Providing data quality information improves opportunities for data reuse by increasing the trustworthiness of the data. Recognizing the need for improving the quality of citizen science data, we describe quality assessment and quality control (QA/QC) issues for these data and offer perspectives on aspects of improving or ensuring citizen science data quality and for conducting research on related issues.

scholarly journals Improving estimation of species distribution from citizen-science records using data-integration models

2021 ◽  
Author(s):  
Viviane Zulian ◽  
David A. W. Miller ◽  
Goncalo Ferraz
Keyword(s):  
Data Integration ◽  
Spatial Autocorrelation ◽  
Citizen Science ◽  
Geographic Range ◽  
Sampling Effort ◽  
Data Sets ◽  
Science Data ◽  
Integration Model ◽  
Data Set ◽  
Joint Likelihood

Mapping species distributions is a crucial but challenging requirement of wildlife management. The frequent need to sample vast expanses of potential habitat increases the cost of planned surveys and rewards accumulation of opportunistic observations. In this paper, we integrate planned survey data from roost counts with opportunistic samples from eBird, WikiAves and Xeno-canto citizen-science platforms to map the geographic range of the endangered Vinaceous-breasted Parrot. We demonstrate the estimation and mapping of species occurrence based on data integration while accounting for specifics of each data set, including observation technique and uncertainty about the observations. Our analysis illustrates 1) the incorporation of sampling effort, spatial autocorrelation, and site covariates in a joint-likelihood, hierarchical, data-integration model; 2) the evaluation of the contribution of each data set, as well as the contribution of effort covariates, spatial autocorrelation, and site covariates to the predictive ability of fitted models using a cross-validation approach; and 3) how spatial representation of the latent occupancy state (i.e. realized occupancy) helps identify areas with high uncertainty that should be prioritized in future field work. Our results reveal a Vinaceous-breasted Parrot geographic range of 434,670 square kilometers, which is three times larger than the Extant area previously reported in the IUCN Red List. The exclusion of one data set at a time from the analyses always resulted in worse predictions by the models of truncated data than by the full model, which included all data sets. Likewise, exclusion of spatial autocorrelation, site covariates, or sampling effort resulted in worse predictions. The integration of different data sets into one joint-likelihood model produced a more reliable representation of the species range than any individual data set taken on its own improving the use of citizen science data in combination with planned survey results.

scholarly journals Assessing citizen science data quality: an invasive species case study

2011 ◽  
Vol 4 (6) ◽  
pp. 433-442 ◽  
Author(s):  
Alycia W. Crall ◽  
Gregory J. Newman ◽  
Thomas J. Stohlgren ◽  
Kirstin A. Holfelder ◽  
Jim Graham ◽  
...  
Keyword(s):  
Invasive Species ◽  
Data Quality ◽  
Citizen Science ◽  
Science Data

scholarly journals Visualising reporting patterns of Citizen Science observations and their importance for the interpretation of data and visualisations: the example of ornithological observations

2020 ◽  
Vol 2 ◽  
pp. 1-1
Author(s):  
Jana Moser ◽  
Johannes Wahl ◽  
Stephan Schwan ◽  
Julia Moritz ◽  
Tom Hoyer ◽  
...  
Keyword(s):  
Citizen Science ◽  
Data Transfer ◽  
Regional Distribution ◽  
Bird Species ◽  
Distribution Patterns ◽  
Data Sets ◽  
Great Effort ◽  
Science Data ◽  
Advantages And Disadvantages ◽  
Data Collections

Abstract. Every day, nature enthusiasts collect valuable information about local animal and plant populations. This has resulted in approximately 50 million data sets until now for the bird observations at ornitho.de alone, with the observation numbers having risen sharply during the last three years. Such data may not always meet all scientific criteria, but due to their quantity they allow statements to be made, e.g. about regional distribution patterns or local occurrences. However, the citizens involved in the survey and the interested public are still unable to use these data, or can only use them in very low resolution or at great effort. According to the current Citizen Science Guidelines, data collecting citizens and other interested parties should be able to use such data and, for example, discover the ornithological diversity on their own doorstep. For this reason, the “Experiencing biodiversity” project has developed web maps, which, by the way, should inspire even more people to record biodiversity.The presentation will firstly address the visualization and interpretation of citizen science data collections. In order to create awareness that numerous animal and plant species are in danger and need special protection, an overview of the current species inventory is necessary. The aim of the project is to provide this overview by visual means to the participating citizen scientists but also to the interested public. The maps should be easy to understand and take into account the varying degrees of citizen science participation in different regions. Thus, in addition to the occurrence of a bird species, the reporting activity is important and therefore displayed on the map. This is unique and shows how many reports are available for all bird species recorded. Thus, it is possible to estimate the amount and distribution of the available data and to compare them with the reported occurrence of a single bird species. As a result, data gaps or changes over time become visible and support the interpretation of the data. As there are no negative reports, only this additional level allows an interpretation at all whether a bird species is "not present" or whether it was just "not reported" in certain areas. At the same time, the project wants to contribute to a better understanding of the visualizations, but also of the underlying data basis.Secondly, the presentation will also address the sometimes contradictory relationship between visualization – i.e. making visible – and the most diverse interests of protection – i.e. avoiding making visible. And thirdly, challenges in the visualization of mass data in the sense of a performant presentation of such data to a broad public will be discussed. A special challenge with regard to performance issues are rapidly increasing data volumes and questions of data protection, data transfer to the visualization but also permanent costs for the provision of data which should not be underestimated.Due to the ongoing status of the project, interim results are presented and mirrored with currently ongoing scientific studies on the use of the maps by Citizen Scientists and the influence of the visualisations on the motivation of ornithologists. Advantages and disadvantages of this visualisation will be discussed with regard to its transferability to other projects.

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