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444 results for “Citizen Science”
Antipredator behaviors in urban settings: Ecological experimentation powered by citizen science
<p><span>1. Animal behaviors are often modified in urban settings due to changes in species assemblages and interactions. The ability of prey to respond to a predator is a critical behavior, but </span><span>urban populations may experience altered predation pressure, food supplementation, and other human-mediated disturbances that modify their responsiveness to predation risk and promote habituation.</span></p> <p><span>2. Citizen-science programs generally focus on the collection and analysis of observational data (e.g., bird checklists), but there has been increasing interest in the engagement of citizen scientists for ecological experimentation.</span></p> <p><span>3. Our goal was to implement a behavioral experiment in which citizen scientists recorded antipredator behaviors in wild birds occupying urban areas. In North America, increasing populations of Accipiter hawks have colonized suburban and urban areas and regularly prey upon birds that frequent backyard bird feeders. This scenario, of an increasingly common avian predator hunting birds near human dwellings, offers a unique opportunity to characterize antipredator behaviors within urban passerines. </span></p> <p><span>4. For two winters, we engaged citizen scientists in Chicago, IL, USA to deploy a playback experiment and record antipredator behaviors in backyard birds. If backyard birds maintained their antipredator behaviors, we hypothesized that birds would decrease foraging behaviors and increase vigilance in response to a predator cue (hawk playback) but that these responses would be mediated by flock size, presence of sentinel species, body size, tree cover, and amount of surrounding urban area. </span></p> <p><span>5. Using a randomized control–treatment design, citizen scientists at 15 sites recorded behaviors from 3,891 individual birds representing 22 species. Birds were more vigilant and foraged less during the playback of a hawk call, and these responses were strongest for individuals within larger flocks and weakest in larger-bodied birds. We did not find effects of sentinel species, tree cover, or urbanization. </span></p> <p><span>6. By deploying a behavioral experiment, we found that backyard birds inhabiting urban landscapes largely maintained antipredator behaviors of increased vigilance and decreased foraging in response to predator cues. Experimentation in citizen science poses challenges (e.g., observation bias, sample size limitations, reduced complexity in protocol design), but unlike programs focused solely on observational data, experimentation allows researchers to disentangle the complex factors underlying animal behavior and species interactions. </span></p>
Citizen Science Initiatives in Belgium
<p>This dataset contains original mapping data developed in the process of the Belgiuam Citizen Science landscape review. In total, 31 German Citizen Science projects were analysed according to a common framework. During the mapping process, we weren’t always able to find information on things that interested us e.g. project impact, stakeholder engagement, the size of the volunteering force. But just because we couldn’t find something does not mean the results or activities did not happen. It goes without saying that absence of evidence is not evidence of absence. Failure to find some information on our part can be explained by the fact that we worked primarily with internet sources, so we had to make do with whatever publicly available information we could find within reasonable time.</p>
Dataset for Scrollytelling as as Strategy for Socio-Environmental Enagement: A Digital Citizen Science Narrative Approach
<p>Resulting Dataset on the evaluation of Scrollytelling deliverables developed by higher education students based on citizen science projects.</p>
ScintiPi3 data sets for "First observations of severe scintillation over low-to-mid latitudes driven by quiet-time extreme equatorial plasma bubbles: conjugate measurements enabled by citizen science initiatives"
<p>ScintPi 3.0 data sets for "First observations of severe scintillation over low-to-mid latitudes driven by quiet-time extreme equatorial plasma bubbles: conjugate measurements enabled by citizen science initiatives" by Sousasantos et al. (2024).</p>
Data coverage, biases, and trends in a global citizen-science resource for monitoring avian diversity
<p><strong>Aim:</strong> Understanding and addressing the global biodiversity crisis requires ecological information compiled continuously from across the globe. Data from citizen science initiatives are useful for quantifying species' ecological niches and geographical distributions but can be difficult to apply towards biodiversity monitoring. The presence of fixed geographical locations reduces the opportunistic nature of citizen science data, allowing for more reliable and nuanced trend estimation. The eBird citizen-science programs contains predefined locations whose bird assemblages are sampled across years ('hotspots'). For hotspots to function as a biodiversity monitoring resource, issues related to data coverage, biases, and trends need to be addressed.</p> <p><strong>Location:</strong> Global.</p> <p><strong>Methods:</strong> We estimated the survey completeness of species richness at 300,500 eBird hotspots during the years 2002 to 2022. We documented sampling biases at eBird hotspot and non-hotspot locations during 2022 based on protection status, temperature, precipitation, and landcover.</p> <p><strong>Results:</strong> A total of 10,410 bird species (<em>ca</em>. 96.9% of total) were recorded at hotspots. The number hotspots and the quantity of data and unique participants and quality of species richness estimates has increased worldwide with the Nearctic containing the strongest and most consistent trends. Compared to non-hotspots, hotspots over sampled areas with higher protection status. Hotspots and non-hotspots over sampled warmer and wetter locations in the Antarctic, Nearctic, and Palearctic, and cooler locations in the Afrotropics, Australasia, and the Neotropics. Hotspots and especially non-hotspots over sampled urban areas. Hotspots and non-hotspots under sampled shrublands in Australasia. Hotspots and especially non-hotspots under sampled forests in the Afrotropics, Indomalaya, Neotropics, and Oceania.</p> <p><strong>Main conclusions:</strong> Hotspots have captured a large component of the world's avian diversity but have done so inconsistently across space and time. Data quantity and quality are increasing in many regions, but the presence of sampling biases and spatial uncertainty needs to be addressed when applying the data.</p>
Data for "Citizen science as a valuable tool for environmental review" - Frontiers in Ecology and the Environment - Callaghan et al.
<p>This dataset is the dataset used in Callaghan et al. Citizen science as a valuable tool for environmental review. Frontiers in Ecology and the Environment. The data are Environmental Impact Statement titles, and our coding of those documents. See paper for details.</p>
FIGURE 10 in An analysis of fossil identification guides to improve data reporting in citizen science programs
FIGURE 10. An example of a †Cosmopolitodus hastalis photo enhanced by illustration.
Citizen Science Reports on Aurora Sighting and Technological Disruptions during the 10 May 2024 Geomagnetic Storm – ARCTICS Survey
<div> <div> <div> <div> <p>The geomagnetic storm that began on 10 May 2024 provided stunning auroral displays observed worldwide. This dataset contains data collected via an online survey designed and distributed by the "Auroral Research Coordination – Towards Internationalised Citizen Science" (ARCTICS) collaboration sponsored by the International Space Science Institute (ISSI) in Bern, Switzerland (see https://collab.issibern.ch/arctics/). A total of 696 observers from over 30 countries filled in the survey and reported on aurora sightings and experienced disruptions in technological systems during the superstorm.</p> <p>The dataset consists of two data files in the CSV format and a text file providing a detailed description of the data. The collected data have been anonymised and pre-processed to obtain a homogeneous data set.</p> <p>Dataset associated with the <span>egusphere-2024-2174 preprint by Grandin et al. ("<span>The geomagnetic superstorm of 10 May 2024: Citizen science observations</span>"), submitted to Geoscience Communication.</span></p> </div> </div> </div> </div>
Fig. 1 in Fig. 5 in Fig. 2 in An Updated Checklist of Sea Slugs (Gastropoda, Heterobranchia) from Hong Kong Supported by Citizen Science.
Fig. 1. Austruca albimana (Kossmann, 1877), dorsal view of the berried female.
Illustrative Darwin core archive to output data from a citizen science platform to a collection management system
<p>Illustrative DwC archive to send data back to a collection management system from a citizen sciences platform. This illustrative archive displays the specimens used for the trans-institutional and trans-platform pilot project held in the frame of ICEDIG.</p> <p>Further description of its content in the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Enhancing the health and wellbeing benefits of biodiversity citizen science
Open the record for dataset details and reuse information.
Detecting the effect of intensive agriculture on Odonata diversity using citizen science data
<p>These datasets are used in the following article :</p> <p><strong><span>Detecting the effect of intensive agriculture on Odonata diversity using citizen science data</span></strong></p> <p><strong><span><span>By </span></span></strong><span>Renaud Baeta<sup>1</sup>, Justine Léauté<sup>1,2</sup>, Éric Sansault<sup>1</sup> and Sylvain Pincebourde<sup>2*</sup></span></p> <p><span><strong>In </strong></span><em><span>Ecological Applications </span></em><span>(Research article)</span></p> <p><strong><span>Abstract</span></strong></p> <p><span><span><span>Agricultural areas represent one of the major ecosystems of the world. Intensification of agricultural practices produced openfields characterized by low biological diversity. Nevertheless, the distance up to which intensive agricultural fields alter surrounding natural systems is rarely quantified. We determined the spatial scale at which agricultural landscapes alter the diversity of Odonates, a key taxon in wetland ponds, and we tested to what extent citizen-science data can be used reliably for this purpose. We compiled 7,731 observations made in a portion of the region Centre-Val-de-Loire (France) over 10 years by naturalists on 729 water bodies to analyze the effect of agricultural landscapes (mainly wheat, rapeseed, sunflower) on the species richness of both damselflies and dragonflies in lentic systems. Sixty species were reported over the 10-years period. For dragonflies, intensive agricultural landscapes best explained their richness at the scales of 800m and 1,600m for overall and autochthonous species, respectively, when using the full dataset. The spatial scale was smaller for damselflies, at 200m for both overall and autochthonous species. These distances were not severely impacted when constraining the data to consider several biases. Multi-model averaging showed that the proportion of intensive agriculture decreased species richness, despite the potential biases inherent to an imperfect database acquired by citizens. This imperfect citizen dataset allows to infer the lowest effect size of agriculture on species richness. Quantitatively, this effect was more important for autochthonous species. Interestingly, both relatively rare taxa and common or generalist species can be under threat in intensive agricultural landscapes, calling for more ecotoxicological studies. The influence of agricultural practices from a distance implies that conservation and management plans of wetland ponds should consider the landscape ecological characteristics and not only the pond features. Conservation efforts focusing too locally on a site may be undermined because intensive agriculture from a distance limits the potential for the site to recover highly diverse communities. These distant effects should be integrated by policy-makers when deciding which wetland pond should benefit from a conservation plan or which conservation action may be planned, implementing for instance buffer zones and/or ecological corridors composed of natural vegetation. </span></span></span></p>
Citizen Science Scan 2023 Belgium - dataset
Open the record for dataset details and reuse information.
Data from: Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics
Time-lapse cameras facilitate remote and high-resolution monitoring of wild animal and plant communities, but the image data produced require further processing to be useful. Here we publish pipelines to process raw time-lapse imagery, resulting in count data (number of penguins per image) and 'nearest neighbour distance' measurements. The latter provide useful summaries of colony spatial structure (which can indicate phenological stage) and can be used to detect movement – metrics which could be valuable for a number of different monitoring scenarios, including image capture during aerial surveys. We present two alternative pathways for producing counts: 1) via the Zooniverse citizen science project Penguin Watch and 2) via a computer vision algorithm (Pengbot), and share a comparison of citizen science-, machine learning-, and expert- derived counts. We provide example files for 14 Penguin Watch cameras, generated from 63,070 raw images annotated by 50,445 volunteers. We encourage the use of this large open-source dataset, and the associated processing methodologies, for both ecological studies and continued machine learning and computer vision development.
GLOBE Mosquito Habitat Mapper Citizen Science Data 2017-2020
<p>Three Cases: Metadata and Procedures</p> <p>The data sets described here were used in an article submitted to the journal GeoHealth in 2021. The data files and further supplemental links (including general information about GLOBE data) can be accessed at https://observer.globe.gov/get-data/mosquito-habitat-data.</p> <p>Case 1: Removal of records with suspect geolocation data. A Python script was applied to remove records where the measured position (in decimal degrees) was identical to the GLOBE MGRS site position. GPS-obtained latitude and longitude coordinates are reported in decimal degrees, so records identified by whole numbers were also removed. This procedure removed 5704 (23%) of the 24983 records in the Mosquito Habitat Mapper database, with 19,279 records remaining. The secondary data sets cleaned only for geolocation anomalies were labeled Case 1.</p> <p>Case 2: Identifying suspected training events. For this test, we sought to identify groups of data that exceeded 10 records sharing these characteristics. Another Python script was employed to extract the photos for ease of visual inspection. Because we needed to manually review the photo records, we set the threshold for groups at >10, so that the analysis could be completed in the time allotted. Groups identified thought this procedure were outputted as case 2: groups. The resulting data set cleaned of groups >10 was labeled Case 2. The resulting data set included 20,006 records and identified 2,447 records found in clusters we postulated were training events.</p> <p>Case 3: The Case 3 secondary dataset result from applying the Python scripts used to create Cases 1 and 2. We used the Case 3 data sets, with improved geolocation and large groups eliminated, in the following analysis.</p> <p>Acknowledgments: These data were obtained from NASA and the GLOBE Program and are freely available for use in research, publications and commercial applications. When data from GLOBE are used in a publication, we request this acknowledgment be included: "These data were obtained from the GLOBE Program." Please include such statements, either where the use of the data or other resource is described, or within the acknowledgments section of the publication.</p>
Data from: Assessing the usefulness of Citizen Science Data for habitat suitability modelling: opportunistic reporting versus sampling based on a systematic protocol
<p><strong>Aim:</strong> To evaluate the potential of models based on opportunistic reporting (OR) compared to models based on data from a systematic protocol (SP) for modelling species distributions. We compared model performance for eight forest bird species with contrasting spatial distributions, habitat requirements, and rarity. Differences in the reporting of species were also assessed. Finally, we tested potential improvement of models when inferring high quality absences from OR based on questionnaires sent to observers.</p> <p><strong>Location:</strong> Both datasets cover the same large area (Sweden) and time period (2000 -2013).</p> <p><strong>Methods:</strong> Species distributions were modelled using logistic regression. Predictive performance of OR models to predict SP data were assessed based on AUC. We quantified the congruence in spatial predictions using Spearman's rank correlation coefficient. We related these results to species characteristics and reporting behaviour of observers. We also assessed the gain in predictive performance of OR models by adding inferred absences. Finally, we investigated the potential impact of sampling bias in OR.</p> <p><strong>Results:</strong> For all species, and despite the sampling biases, results from OR overall agreed well with those of SP, for the nationwide spatial congruence of habitat suitability maps and the selection and directions of species-environment relationships. The OR models also performed well in predicting the SP data. The predictive performance of the OR models increased with species rarity and even outperformed the SP model for the rarest species. No significant impact of observer behaviour was found.</p> <p><strong>Main Conclusions:</strong> Relatively simple analyses with inferred absences could produce reliable spatial predictions of habitat suitability. This was especially true for rare species. OR data should be seen as a complement to SP, as the weakness of one is the strength of the other, and OR may be especially useful at large spatial scales or where no systematic data collection protocols exist.</p>
Supporting Information 1 to the paper "Human-machine-learning integration and task allocation in citizen science".
<p>This appendix - Supporting Information 1 - is a dataset excel file directly related to the following paper:</p> <p>Ponti, M., Seredko, A. <a href="http://doi.org/10.1057/s41599-022-01049-z">Human-machine-learning integration and task allocation in citizen science.</a> <em>Humanit Soc Sci Commun</em> <strong>9, </strong>48 (2022). https://doi.org/10.1057/s41599-022-01049-z</p> <p>The dataset in this excel file is a detailed result of the integrative literature review conducted for the manuscript.</p> <p> </p> <p> </p>
Database of the educational role of Citizen Science in the framework of Open Science from the paradigm of complex thinking
<p>Database for the analysis of the educational role of Citizen Science projects in the framework of Open Science from the paradigm of complex thinking</p>
Data from: Hazard and catch composition of ghost fishing gear revealed by a citizen science clean-up initiative
<p><span>Ghost fishing, the continued catch of fishes and invertebrates by lost fishing gear, represents an animal welfare issue as well as a waste of both potential food and ecosystem resources. Fishing gear is lost by both commercial and recreational fishers, and management authorities often lack an overview of gear loss and subsequently potential impact on coastal populations. </span><span>To investigate the hazard and catch composition of lost fishing gear along the Norwegian coast</span><span>, recreational divers in collaboration with scientists conducted systematic reporting of retrieved lost fishing gear. </span><span>Through this citizen science project,</span><span> a total of 12,101 gear items were retrieved and reported, including traps, gillnets and fyke nets. Combining both data on the catch ratio of the gear and its relative quantity, we identified the five most hazardous gear types to be parlor traps, gillnets, fyke nets, wrasse traps and square collapsible traps. The parlour trap was the most hazardous trap, due to high catchability and quantity. The correct classification of gear type could not be confirmed in 2.8 – 6.1 % of the pictures taken by divers, depending on reporting format, and divers reported the wrong gear type in 1.4 % of the reports. Brown crab (<em>Cancer</em> <em>pagurus</em>) was the species most often found in retrieved gear. Furthermore, the vulnerable species European lobster (<em>Homarus</em> <em>gammarus</em>) and Atlantic cod (<em>Gadus</em> <em>morhua</em>) were also common. These results can inform future clean up-initiatives and management responses to ghost fishing, including preventive measures against gear loss and gear restrictions and customization. </span></p>
Sustaining Cities, Naturally Webinar: Education Session - Bioblitz - Citizen science
<p>Poorly planned urbanisation can lead to societal challenges as social deprivation, climate change, deteriorating health and increasing pressure on urban nature. Urban ecosystem restoration can contribute to lessen these challenges, e.g. through implementing nature-based solutions (NBS). </p> <p>This pitch was made as part of the online webinar ‘Sustaining cities, Naturally: Urban ecosystem restoration in Europe, China and Latin America’, which took place as an official side-event of the European Week of Regions and Cities 2022 on 13th and 14th October 2023. The webinar was jointly organised by the projects: INTERLACE, CONEXUS, Regreen and CLEARING HOUSE. </p> <p>The webinar illustrated how Horizon 2020 projects support international cooperation in knowledge creation and knowledge exchange between local authorities and researchers to promote urban ecosystem restoration in Europe, China and Latin America and brought together cities, regions and local authorities, city network representatives, policy makers, researchers, civil society and experts on nature-based solutions and urban ecosystem restoration from Europe, China and Latin America. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.