Find research datasets worth reusing

opencc-zeroDec 2016View details →

Data from: Using citizen science monitoring data in species distribution models to inform isotopic assignment of migratory connectivity in wetland birds

Stable isotopes have been used to estimate migratory connectivity in many species. Estimates are often greatly improved when coupled with species distribution models (SDMs), which temper estimates in relation to occurrence. SDMs can be constructed using from point locality data from a variety of sources including extensive monitoring data typically collected by citizen scientists. However, one potential issue with SDM is that these data oven have sampling bias. To avoid this potential bias, an approach using SDMs based on marsh bird monitoring program data collected by citizen scientists and other participants following protocols specifically designed to maximize detections of species of interest at locations representative of the species range. We then used the SDMs to refine isotopic assignments of breeding areas of autumn-migrating and wintering Sora (Porzana carolina), Virginia Rails (Rallus limicola), and Yellow Rails (Coturnicops noveboracensis) based on feathers collected from individuals caught at various locations in the United States from Minnesota south to Louisiana and South Carolina. Sora were assigned to an area that included much of the western U.S. and prairie Canada, covering parts of the Pacific, Central, and Mississippi Flyways. Yellow Rails were assigned to a broad area along Hudson and James Bay in northern Manitoba and Ontario, as well as smaller parts of Quebec, Minnesota, Wisconsin, and Michigan, including parts of the Mississippi and Atlantic Flyways. Virginia Rails were from several discrete areas, including parts of Colorado, New Mexico, the central valley of California, and southern Saskatchewan and Manitoba in the Pacific and Central Flyways. Our study demonstrates extensive data from organized citizen science monitoring programs are especially useful for improving isotopic assignments of migratory connectivity in birds, which can ultimately lead to better informed management decisions and conservation actions.

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Supplementary material from "Utilising citizen science data to rapidly assess changing associations between wild birds and avian influenza outbreaks in poultry."

Stephen H. Vickers, Jayna Raghwani, Ashley C. Banyard, Ian H. Brown, Guillaume Fournie and Sarah C. Hill

opencc-by-4.0Dec 2023View details →

opencc-by-4.0Nov 2023View details →

Dataset for Citizen Science as a Relevant Approach to the Challenges of Complex Thinking Development in Higher Education

Dataset with questions and analysis for the article Citizen Science as a Relevant Approach to the Challenges of Complex Thinking Development in Higher Education: Mapping and Bibliometric Analysis

opencc-by-4.0Feb 2024View details →

Data from: Mapping wing morphs of Tetrix subulata using citizen science data: flightless groundhoppers are more prevalent in grasslands near water

To analyse the correlation between groundhopper wing morph and landscape characteristics, we collected the following data. GBIF observations of Tetrix subulata (Linnaeus, 1758) in the Netherlands were annotated with wing morph and sex (based on the images), and weather and landscape information (based on the location). The weather information is based on an interpolation of data from the Royal Netherlands Meteorological Institute (KNMI). Landscape and habitat information was characterised by determining the area of different area types, from the Basisregistratie Topografie (BRT) TOP10NL dataset, in a radius around the observation. The landscape information also includes Dutch physical-geographical regions. To check for possible effects of seasonality, the event date of the observation is included. To check for the effect of the landscape radius on the analysis, those analyses were repeated for different radiuses. Depending on the precision of the coordinate location, some radiuses could not be tested for some observations.  The file "data.csv" contains one row per observation-radius combination, with the following columns (* repeated for each radius): <table> <tbody> <tr> <td>Columns</td> <td>Description</td> </tr> <tr> <td>gbifID*</td> <td>ID of the observation on GBIF</td> </tr> <tr> <td>eventDate*</td> <td>Date of observation (YYYY-MM-DD)</td> </tr> <tr> <td>wing_morph*</td> <td>Wing morph annotation (long; short)</td> </tr> <tr> <td>sex*</td> <td>Sex annotation (female; male; obscured; multiple; reevaluate i.e. unknown)</td> </tr> <tr> <td>region*</td> <td>Physical-geographical region in which the observation was made</td> </tr> <tr> <td>temperature*</td> <td>Predicted temperature (average over 1991-2020; degrees Celsius)</td> </tr> <tr> <td>windspeed*</td> <td>Predicted windspeed (average over 1991-2020; meters per second)</td> </tr> <tr> <td>precipitation*</td> <td>Predicted precipitation (average over 1991-2020; millimeters)</td> </tr> <tr> <td>landscape_radius</td> <td>Landscape radius (50, 100, 200, 500, or 1000 meters)</td> </tr> <tr> <td>deciduous forest, grass, mixed forest, ...</td> <td>Total surface area of given area type in given radius around observation (square meters)</td> </tr> </tbody> </table>

opencc-by-4.0Mar 2024View details →

Citizen Science Assoziationen Word Cloud

Assoziationen, die Teilnehmer*innen des Forums Citizen Science (November 2023 in Freiburg, <a href="https://www.buergerschaffenwissen.de/veranstaltungen/forum-citizen-science-2023">https://www.buergerschaffenwissen.de/veranstaltungen/forum-citizen-science-2023</a>) mit dem Begriff Citizen Science in Verbindung brachten. Dargestellt als Word Cloud mit Inkscape. Beantwortet wurde die Frage “Welche drei Begriffe fallen dir zu Citizen Science ein?” schriftlich von 31 Personen. Es wurden 97 Antworten genannt, davon 72 unterschiedliche. Sehr lange Antworten wurden für die grafische Darstellung vereinfacht (insgesamt zwei Begriffe, siehe Dokument Citizen Science Assoziationen-Datensammlung_12-03-24.xlsx). Die Word Cloud-Visualisierung darf beliebig unter folgender Angabe verwendet werden: "Tim Kiessling (CC BY NC 4.0), https://doi.org/10.5281/zenodo.10810212". Die Vektor-Grafikdatei ist verfügbar um Änderungen durchzuführen (Citizen Science Assoziationen_12-03-24.svg).

opencc-by-4.0Apr 2024View details →

Data for "Citizen Science for Health: an international survey on its characteristics and enabling factors"

Data and data analysis code for manuscript "Citizen Science for Health: an international survey on its characteristics and enabling factors"

openOct 2024View details →

Thermo-staat project citizen science indoor temperature and humidity measurements for researching heat stress in the Netherlands

These datasets contains raw data collected within the Thermo-staat citizen science project per the full year. The aim of the project was to get insight in heat stres and if this problem is subject to social inequality. Measurements contain indoor temperature and humidity data. Sensors in the dataset are bound to different rooms in a home and have different periods of activity. Each sensor has metadata about the room/situation attached. The aim was to get most sensors active in the summer. Measurements where not done at a constant frequency, depending on the connectivity sensors did send up to once every 20 seconds. More information on the project on the website of the <a href="https://thermo-staat.nl/">Thermo-staat project</a> More infromation on the <a href="https://thermo-staat.nl/download">data</a>  Live data collection <a href="https://thermo-staat.waag.org/api/status_all">status</a>

opencc-zeroOct 2021View details →

The island biogeography of the eBird citizen-science program

Aim: Island biotas face an array of unique challenges under global change. Monitoring and research efforts, however, have been hindered by the large number of islands, their broad distribution and geographic isolation. Global citizen-science initiatives have the potential to address these deficiencies. Here, we determine how the eBird citizen-science program is currently sampling island bird assemblages annually and how these patterns are developing over time. Location: Global. Taxa: Birds. Methods: We compiled occurrence information of non-marine bird species across the world's islands (n = 21,813) over an 18-year period (2002-2019) from eBird. We estimated annual survey completeness and species richness across islands, which we examined in relation to six geographical and four climatic features. Results: eBird contained bird occurrence information for ca. 20% of the world's islands (n = 4,205) with ca. 8% classified as well surveyed annually (n =1,644). eBird participants tended to survey larger islands that were more distant from the mainland. These islands had lower proximity to other islands and contained a broader range of elevations. Temperature, precipitation, and temperature seasonality were at intermediate levels. Precipitation seasonality was at low and intermediate levels. Islands located between 10-60° N latitude and 30-40° S latitude were overrepresented, and islands located between 60-130° W longitude were underrepresented. From 2002 to 2019, the number of islands surveyed annually increased by ca. 96.3 islands/year. During this period, island size decreased, distance from mainland did not change, proximity to other islands increased, and elevation range decreased. Main conclusions: The eBird program tends to survey larger islands containing intermediate climates that are more isolated from the mainland and other islands. These findings provide a framework to support the rigorous application of eBird data in avian island biogeography. Our findings also emphasize citizen science as a resource to support ecological research, conservation, and monitoring efforts across remote regions of the globe.

opencc-zeroDec 2021View details →

Chimpanzee identification and social Network construction through an online citizen science platform

Citizen science has grown rapidly in popularity in recent years due to its potential to educate and engage the public while providing a means to address a myriad of scientific questions. However, the rise in popularity of citizen science has also been accompanied by concerns about the quality of data emerging from citizen science research projects. We assessed data quality in the online citizen scientist platform Chimp&See, which hosts camera trap videos of chimpanzees (Pan troglodytes) and other species across Equatorial Africa. In particular, we compared detection and identification of individual chimpanzees by citizen scientists to that of experts with years of experience studying those chimpanzees. We found that citizen scientists typically detected the same number of individual chimpanzees as experts, but assigned far fewer identifications (IDs) to those individuals. Those IDs assigned, however, were nearly always in agreement with the IDs provided by experts. We applied the data sets of citizen scientists and experts by constructing social networks from each. We found that both social networks were relatively robust and shared a similar structure, as well as having positively correlated individual network positions. Our findings demonstrate that, although citizen scientists produced a smaller data set based on fewer confirmed IDs, the data strongly reflect expert classifications and can be used for meaningful assessments of group structure and dynamics. This approach expands opportunities for social research and conservation monitoring in great apes and many other individually identifiable species.

opencc-zeroFeb 2022View details →

A national scale BioBlitz using citizen science and eDNA metabarcoding for monitoring coastal marine fish

Marine biodiversity is threatened by human activities. To understand the changes happening in aquatic ecosystems and to inform management, detailed, synoptic monitoring of biodiversity across large spatial extents is needed. Such monitoring is challenging due to the time, cost, and specialized skills that this typically requires. In an unprecedented study, we here combined citizen science with eDNA metabarcoding to map coastal fish biodiversity at a national scale. We engaged 360 citizen scientists to collect filtered sea water samples from 100 sites across Denmark over two seasons (1 pm on September 29th 2019 and May 10th 2020), and by sampling at nearly the exact same time across all 100 sites, we obtained an overview of fish biodiversity largely unaffected by temporal variation. This would have been logistically impossible for the involved scientists without the help of volunteer citizens. We obtained a high return rate of 94% of the samples, and a total richness of 52 fish species, representing approximately 80% of coastal Danish fish species and approximately 25% of all Danish marine fish species. We retrieved distribution patterns matching known occurrence for both invasive, endangered, and cryptic species, and detected seasonal variation in accordance with known phenology. Dissimilarity of eDNA community compositions increased with distance between sites. Importantly, comparing our eDNA data with National Fish Atlas data (the latter compiled from a century of observations) we found positive correlation between species richness values and a congruent patterns of community compositions. These findings support the use of eDNA-based citizen science to detect patterns in biodiversity, and our approach is readily scalable to other countries, or even regional and global scales. We argue that future large-scale biomonitoring will benefit from using citizen science combined with emerging eDNA technology, and that such an approach will be important for data-driven biodiversity management and conservation.

opencc-by-4.0Feb 2022View details →

Data for: Impacts of urbanization on chloride and stream invertebrates: a 10-year citizen science field study of road salt in stormwater runoff

Abstract: The use of deicing agents during the winter months is one of many stressors that impact stream ecosystems in urban and urbanizing watersheds. In this study, a long-term dataset collected by citizen scientists with the Missouri Stream Team was used to evaluate the relationships between watershed urbanization metrics and chloride metrics. Further, these data were used to explore effects of elevated chloride concentrations on stream invertebrate communities using quantile regression. While the amount of road surface in a watershed was a dominant factor in predicting the maximum chloride measurement, the median chloride concentration was also strongly related to the amount of medium-to-high density development in the watershed, suggesting that non-municipal salt use is an important contributor to increases in baseflow chloride concentrations. Additionally, chloride concentration appears to be one of the many factors that impact invertebrate density and diversity measurements, with decreases in invertebrate diversity corresponding with the U.S. EPA water quality criteria. Our findings suggest that the use of chloride-based road salt on municipal roads as well as in non-municipal settings is contributing to a loss of diversity and density of aquatic invertebrate communities in urban regions.

opencc-zeroMay 2022View details →

Spatial clustering of trumpetfish shadowing behaviour in the Caribbean Sea revealed by citizen science

The West Atlantic trumpetfish (Aulostomus maculatus) performs an unusual hunting strategy, termed shadowing, whereby a trumpetfish swims closely behind or next to another 'host' species to facilitate the capture of prey. Despite trumpetfish being observed throughout the Caribbean, observations of this behaviour appear to be concentrated to a handful of localities. Here we assess the degree of geographical clustering of shadowing behaviour throughout the Caribbean Sea, and identify ecological features associated with the likelihood of its occurrence. To do this, we used a citizen science approach by creating and distributing an online survey to target frequent divers across this region. While the vast majority of participants observed trumpetfish on nearly every dive across the Caribbean, using random labelling spatial analyses, we found the frequency of shadowing behaviour was geographically clustered; participants that were within ~ 120 km of each other reported observations of shadowing that were more similar than would be expected by chance. Our survey also highlighted that trumpetfish were more likely to be observed shadowing than observed alone in a particular habitat type, and with particular host species, suggesting potential ecological factors that could drive the uneven distribution of this behaviour. Our results demonstrate that this behavioural hunting strategy is spatially clustered and, more generally, highlight the power of using citizen science to investigate variation in animal behaviour over thousands of square kilometres.

opencc-zeroMay 2022View details →

Data from: Identifying conservation priorities for gorgonian forests in Italian coastal waters with multiple methods including citizen science and social media content analysis

<div> <div> <div> <div> Gorgonian forests are among the most complex of subtidal habitats in the Mediterranean Sea, supporting high biodiversity and providing diverse ecosystem services. Despite their iconic status, the geographical distribution and condition of gorgonian species is poorly known. Using multiple online data sources, our primary aims were to compile, map and analyse observations of gorgonian forests in Italian coastal waters to assess the biological complexity of gorgonian forests; evaluate impacts and vulnerable species, and identify areas of special interest inside and outside of existing MPAs to help prioritise conservation strategies and actions. </div> </div> </div> </div>

[Suplemental Materials] Accuracy of bird identifications in citizen science data: a quantification of errors using photographic records

Supporting Information Appendix S1. The full dataset used in this study with a reproducible R code to perform data quality and network analyses.  Appendix S2. All species ranking of identification accuracy of photo reports submitted to eBird in Argentina. The ranking is first ordered by the minimum value found for either precision and recall scores, and second by the number of samples analyzed for each species. Species that were tagged as difficult to identify are indicated as ‘TRUE’ in column D named ‘hard_to_id’. Appendix S3. High-resolution network (Html file).

Accuracy of bird identifications in citizen science data: a quantification of errors using photographic records [R code]

Appendix S1. The full dataset used in this study with a reproducible R code to perform data quality and network analyses. R code archived to Zenodo for publication.

Analytics of collaborations and performance of citizen science teams during the GEAR cycle 2 of the Crowd4SDG project.

This data frame is part of the deliverable 4.2 of the Crowd4SDG project. It contains the measured analytics of citizen collaborations using new metrics/descriptors developed during the first year of the Crowd4SDG project.  <pre>The goal of the Crowd4SDG project is to research the extent to which Citizen Science (CS) can provide an essential source of non-traditional data for tracking progress towards the SDGs, as well as the ability of CS to generate social innovations that enable such progress. In the Crowd4SDG project, the Work Package 4 aims to develop and monitor new metrics and develop statistical models of team engagement and collaboration that contribute to the many-faceted outcomes of the citizen science projects developed within the Crowd4SDG consortium over the 3-years course of the project. Here, we share a dataframe of features collected during the GEAR cycle 2 pertaining to team composition, activity, performance and interaction dynamics. In particular, we leveraged the CoSo platform for collecting self-reported data on collaborations and task allocation structure of participating teams, as well as Slack data for measuring communication networks. The related findings are presented in the deliverable 4.4 of the Crowd4SDG project and serve as a basis for i) exhibiting the potential of using digital traces to derive measures related to team process, ii) highlighting perspectives for monitoring metrics in the next GEAR cycle.</pre>

Smell Pittsburgh: Engaging Community Citizen Science for Air Quality

Link to the files and description of the Smell Pittsburgh Dataset – <a href="https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2FCMU-CREATE-Lab%2Fsmell-pittsburgh-prediction%2Ftree%2Fmaster%2Fdataset%2Fv2&data=05%7C01%7Cy.c.hsu%40uva.nl%7C89562067341d40d0bad308da2c475652%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C637870982141190827%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=gWn0nGRUl5EAHvDfJwTlTNJN%2BWFH62NX6Mw%2B2web6XE%3D&reserved=0">https://github.com/CMU-CREATE-Lab/smell-pittsburgh-prediction/tree/master/dataset/v2</a> Smell Pittsburgh (<a href="https://smellpgh.org">https://smellpgh.org</a>) is a mobile application for crowdsourcing reports of bad odors, such as those generated from air pollution. The data is used to train a machine learning model to predict the presence of bad smell and create push notifications to inform citizens about the bad smell. The motivation, background, and design of the Smell Pittsburgh application is described in the following paper. <ul> <li>Yen-Chia Hsu, Jennifer Cross, Paul Dille, Michael Tasota, Beatrice Dias, Randy Sargent, Ting-Hao (Kenneth) Huang, and Illah Nourbakhsh. 2020. Smell Pittsburgh: Engaging Community Citizen Science for Air Quality. ACM Transactions on Interactive Intelligent Systems. 10, 4, Article 32. DOI:<a href="https://doi.org/10.1145/3369397">https://doi.org/10.1145/3369397</a>. Preprint:<a href="https://arxiv.org/pdf/1912.11936.pdf">https://arxiv.org/pdf/1912.11936.pdf</a>.</li> </ul>

opencc-zeroDec 2015View details →

Data from: Combining citizen science species distribution models and stable isotopes reveals migratory connectivity in the secretive Virginia rail

Stable hydrogen isotope (δD) methods for tracking animal movement are widely used yet often produce low resolution assignments. Incorporating prior knowledge of abundance, distribution or movement patterns can ameliorate this limitation, but data are lacking for most species. We demonstrate how observations reported by citizen scientists can be used to develop robust estimates of species distributions and to constrain δD assignments. We developed a Bayesian framework to refine isotopic estimates of migrant animal origins conditional on species distribution models constructed from citizen scientist observations. To illustrate this approach, we analysed the migratory connectivity of the Virginia rail Rallus limicola, a secretive and declining migratory game bird in North America. Citizen science observations enabled both estimation of sampling bias and construction of bias-corrected species distribution models. Conditioning δD assignments on these species distribution models yielded comparably high-resolution assignments. Most Virginia rails wintering across five Gulf Coast sites spent the previous summer near the Great Lakes, although a considerable minority originated from the Chesapeake Bay watershed or Prairie Pothole region of North Dakota. Conversely, the majority of migrating Virginia rails from a site in the Great Lakes most likely spent the previous winter on the Gulf Coast between Texas and Louisiana. Synthesis and applications. In this analysis, Virginia rail migratory connectivity does not fully correspond to the administrative flyways used to manage migratory birds. This example demonstrates that with the increasing availability of citizen science data to create species distribution models, our framework can produce high-resolution estimates of migratory connectivity for many animals, including cryptic species. Empirical evidence of links between seasonal habitats will help enable effective habitat management, hunting quotas and population monitoring and also highlight critical knowledge gaps.

opencc-zeroDec 2016View details →

Supplementary material 2 from: Runnel V, Wetzel F, Groom Q, Koch W, Pe'er I, Valland N, Panteri E, Kõljalg U (2016) Summary report and strategy recommendations for EU citizen science gateway for biodiversity data. Research Ideas and Outcomes 2: e11563. https://doi.org/10.3897/rio.2.e11563

EU BON conducted a survey to assess how willing are researchers to recruit volunteers in their work, what are the main effects, motivators and hindrances.