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444 results for “Citizen Science”
FIGURE 1 in A shallow-living benthic Rhodaliid siphonophore: citizen science discovery from Papua New Guinea
FIGURE 1. Enhanced photograph of the Papua New Guinea rhodaliid (insert) and a vector drawing of the specimen. Scale bars 10 mm. Original version of the photograph (by Andrey Ryanskiy) is accessible under https://dx.doi.org/10.6084/m9.figshare.5411035.
FIGURE 2 in A shallow-living benthic Rhodaliid siphonophore: citizen science discovery from Papua New Guinea
FIGURE 2. Distribution map of all extant records of Archangelopsis typica (circles) and of the PNG rhodaliid (star) with plotted currents systems showing main surface currents (during the southwest monsoon season). Dashed line marks extremely low water flow (Wolanski et al. 2013).
Supplementary material 1 from: Croce A, Nazzaro R (2017) An atlas of orchids distribution in the Campania region (Italy), a citizen science project for the most charming plant family. Italian Botanist 4: 15-32. https://doi.org/10.3897/ib.4.14916
Relations between the 4 tables of the MS Access database used to store the data of the project and respective fields : Data type: Image
Supplementary material 3 from: Croce A, Nazzaro R (2017) An atlas of orchids distribution in the Campania region (Italy), a citizen science project for the most charming plant family. Italian Botanist 4: 15-32. https://doi.org/10.3897/ib.4.14916
References considered for the bibliographic records : Data type: (measurement/occurence/multimedia/etc.)
Assessing and Correcting Neighborhood Socioeconomic Spatial Sampling Biases in Citizen Science Mosquito Data Collection
<p>Reporting data from the Mosquito Alert citizen science system, active catch basin surveillance, and mosquito trap surveillance used in "Assessing and Correcting Neighborhood Socioeconomic Spatial Sampling Biases in Citizen Science Mosquito Data Collection."</p> <p>The file named mosquito_alert_adult_bite_reports_Barcelona_2014_2023.Rds includes all adult mosquito and mosquito bite reports received from Barcelona Municipality from the start of the Mosqiuto Alert project in 2014 through the end of 2023. The file named mosquito_alert_validated_albopictus_reports_Barcelona_2014_23.Rds includes all expert-validated <em>Ae. albopictus </em>reports received from Barcelona Municipality during the same time period. The data is stored as RDS files and contain the following fields:</p> <ul> <li><strong>year </strong>- the year in which the report was made. Class = dbl.</li> <li><strong>date </strong>- the date om which the report was made. Class = date.</li> <li><strong>type </strong>- the report type, either adult mosquito ("adult") or mosquito breeding site ("site"). Class = chr.</li> <li><strong>lon</strong> - the longitude of the report location. Class = dbl.</li> <li><strong>lat</strong> - the latitude of the report location. Class = dbl.</li> <li><strong>validation_score</strong> - Entolab validation score. Either 1 (possible <em>Ae. albopictus</em>) or 2 (probable <em>Ae. albopictus</em>). This field is present only in the validated reports data. </li> </ul> <p>The file named active_catch_basin_drain_data.Rds includes information about all catch basin drains in Barcelona Municipality in which the Barcelona Public Health Agency (ASPB) detected mosquito activity as part of its continuous monitoring and control of mosquitoes from 2019 through 2023. The data is stored in an RDS file with the following fields:</p> <ul> <li><strong>any_reports </strong>- dummy variable indicating whether any Mosquito Alert adult mosquito or mosquito bite reports were sent through Mosquito Alert from within 200 m of the catch basin drain during the year in which the ASPB detected mosquito activity in hte catch basin drain. Class = lgl.</li> <li><strong>se_expected</strong> - sampling effort for the 0.025 degree lon/lat sampling cell in which the catch basin drain lies during the year in which the ASPB detected mosquito activity in the drain. This value is taken from the SE_expected variable in the sampling_effort_daily_cellres_025.csv.gz file available at https://zenodo.org/records/12602985. Sampling effort is estimated as the expected number of participants sending at least one report from the cell during the day in question given the the number of participants recorded in the cell that day and the amount of time elapsed since each one began participating in the project. Class = dbl.</li> <li><strong>p_singlehh</strong> - proportion of single-member households in the population of the census tract in which the catch basin drain is located. Class = dbl.</li> <li><strong>mean_age </strong>- mean age of the population of the census tract in which the catch basin drain is located. Class = dbl.</li> <li><strong>mean_rent_consumption_unit</strong> - mean income per consumption unit in the census tract in which the catch basin drain is located. Class = dbl.</li> <li><strong>popd</strong> - population density of the census tract in which the catch basin drain is located. Class = dbl.</li> <li><strong>id_item </strong>- unique identifier given to the catch basin drain. Drain itentifiers appear multiple times in the data when the ASPB detected activity in the drain in multiple years. Class = dbl.</li> </ul> <p>The file named trap_data.Rds includes information on the adult mosquito trap surveillance analyzed in this article. The data is stored in an RDS file with the following fields:</p> <ul> <li><strong>females </strong>- number of Ae. albopictus females found in the trap. Class = dbl.</li> <li><strong>trap_name</strong> - unique identifier for the trap. Class = chr.</li> <li><strong>trapping_effort</strong> - number of days from when the trap was set to when it was checked. Class = dbl.</li> <li><strong>date</strong> - date on which the trap was checked. Class = date.</li> <li><strong>mean_tm30</strong> - mean temperature for the 30 days leading up to the date on which the trap was checked. Class = dbl.</li> <li><strong>mean_rent_consumption_unit </strong>- mean income per consumption unit for the census tract in which the trap was located. Class = dbl.</li> </ul>
Involving Volunteers in Citizen Science: tips for Researchers
<div> <div> <div> <div> </div> </div> </div> </div> <div>The video introduces researchers to the possibilities of recruiting volunteers for citizen science projects.</div>
Data from: Using artificial neural networks and citizen science data to assess jellyfish presence along coastal areas
<p><strong><span>General Information</span></strong></p> <p><span>This dataset was used in the study titled "Using artificial neural networks and citizen science data to assess jellyfish presence along coastal areas". The study employs citizen science data collected from the Infomedusa application to assess the presence of jellyfish on beaches along the Andalusian coast, along with environmental data to analyze the factors influencing jellyfish distribution. The study aims to employ machine learning techniques, specifically a Multi-Layer Perceptron (MLP) neural network, to classify user comments on the presence or absence of jellyfish and analyze how environmental factors such as sea surface temperature, wind direction, and wind speed influence jellyfish distribution.</span></p> <p><strong><span>Dataset Columns</span></strong></p> <ul> <li><strong><span>Fecha</span></strong><span>: Timestamp of the comment made by the user in the Infomedusa application.</span></li> <li><strong><span>Municipio</span></strong><span>: Name of the municipality where the beach mentioned in the comment is located.</span></li> <li><strong><span>Jellyfish</span></strong><span>: Binary variable indicating the presence (1) or absence (0) of jellyfish according to the user’s comment.</span></li> <li><strong><span>Comunidad</span></strong><span>: Autonomous community to which the municipality belongs.</span></li> <li><strong><span>Provincia</span></strong><span>: Province to which the municipality belongs.</span></li> <li><strong><span>Latitud</span></strong><span>: Geographical latitude of the municipality where the comment was made.</span></li> <li><strong><span>Longitud</span></strong><span>: Geographical longitude of the municipality where the comment was made.</span></li> <li><strong><span>Set</span></strong><span>: Set of grouped beaches for geographical analysis. Each set includes beaches close to each other and the nearest weather station.</span></li> <li><strong><span>Month</span></strong><span>: Month when the comment was made.</span></li> <li><strong><span>Longitud_sea</span></strong><span>: Longitude of the nearest point in the sea for which environmental data was available.</span></li> <li><strong><span>Latitud_sea</span></strong><span>: Latitude of the nearest point in the sea for which environmental data was available.</span></li> <li><strong><span>SST</span></strong><span>: Sea Surface Temperature at the nearest point in the sea to the municipality, obtained from the Copernicus Marine Environment Monitoring Service.</span></li> <li><strong><span>Wind_dir</span></strong><span>: Wind direction measured at the weather station closest to the municipality, provided by the Spanish Meteorological Agency (AEMET).</span></li> <li><strong><span>Wind_speed</span></strong><span>: Wind speed measured at the weather station closest to the municipality, provided by the AEMET.</span></li> </ul> <p><strong><span>Data Sources</span></strong></p> <ul> <li><strong><span>Infomedusa APP</span></strong><span>: Application developed by the Provincial Council of Malaga and Aula del Mar of Malaga to monitor the presence of jellyfish through citizen participation.</span></li> <li><strong><span>Copernicus Marine Environment Monitoring Service (CMEMS)</span></strong><span>: Provides data on sea surface temperature with an hourly temporal resolution and a spatial resolution of 0.0625° x 0.0625°.</span></li> <li><strong><span>Agencia Estatal de Meteorología (AEMET)</span></strong><span>: Provides daily data on wind direction and speed.</span></li> </ul>
Citizen Science for Librarians: Introduction to the self-paced course
<p>LibOCS project partner KTU Library has made a brief introductory video about the self-paced online course for librarians.</p>
Supplementary material 1 from: Croce A, Nazzaro R (2017) An atlas of orchids distribution in the Campania region (Italy), a citizen science project for the most charming plant family. Italian Botanist 4: 15-32. https://doi.org/10.3897/italianbotanist.4.14916
Relations between the 4 tables of the MS Access database used to store the data of the project and respective fields : Data type: Image
Supplementary material 3 from: Croce A, Nazzaro R (2017) An atlas of orchids distribution in the Campania region (Italy), a citizen science project for the most charming plant family. Italian Botanist 4: 15-32. https://doi.org/10.3897/italianbotanist.4.14916
References considered for the bibliographic records : Data type: (measurement/occurence/multimedia/etc.)
Figure 2 in Remote sensing and citizen science to characterize the ecological niche of an endemic and endangered Costa Rican poison frog
Figure 2. Land cover classification of the study area in the South Pacific of Costa Rica for 2019 using Sentinel-2 spatial data.
Figure 1 in Remote sensing and citizen science to characterize the ecological niche of an endemic and endangered Costa Rican poison frog
Figure 1. Map of the study area in the South Pacific of Costa Rica (1:1,250,000 scale and Coordinate Reference System (CRS) WGS84) for the analysis of the ecological niche of P. vittatus using data generated during 2020. Main towns are shown. Large protected areas are represented by their management category: 1: Corcovado National Park; 2: Piedras Blancas National Park; 3: Golfo Dulce Forest Reserve; 4: Paso de la Danta Biological Corridor. Sources: Costa Rica Atlas 2014, Costa Rica Conservation Areas National System (SINAC) 2016 and 2020. Photograph of Phyllobates vittatus by Marina Garrido-Priego.
Figure 3 in Remote sensing and citizen science to characterize the ecological niche of an endemic and endangered Costa Rican poison frog
Figure 3. Relative niche suitability for Phyllobates vittatus across its distribution in the South Pacific of Costa Rica. The suitability map was generated during 2020 through the combination of eleven different environmental predictors, with elevation, forest percentage, distance to lakes and distance to ASADAS explaining the greatest proportion of the variance. The legend shows niche suitability ranging from low suitability (0; white) to high suitability (1; dark green). We represent in white high-altitude areas (>1500 m) and large plantations identified during the classification of the land cover, which were not included in the model. This prediction was made with a model built using a 5 km buffer area around the known occurrence points. Large protected areas are represented by their management category: 1: Corcovado National Park; 2: Piedras Blancas National Park; 3: Golfo Dulce Forest Reserve; 4: Paso de la Danta Biological Corridor. Sources: Costa Rica Atlas 2014, Costa Rica Conservation Areas National System (SINAC) 2016 and 2020. This map is at a 1:1,150,000 scale and CRS WGS84.
Fig. 2. A–G, Aphaena discolor. A–B in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 2. A–G, Aphaena discolor. A–B, specimen on his host-tree, Kirirom, 9.V.2015 (J. Constant). C, habitat in Kirirom, 9.V.2015 (J. Constant). D, Pursat, Cardamom, 2.I.2009 (J. Holden). E, Koh Kong, Tatai, 5.III.2012 (G. Chartier). F–G, Dichoptera sp. Chambok, 5.V.2015 (J. Constant). H, Kalidasa nigromaculata, Siem Reap, Angkor, 10.VIII.2014 (S. De Greef). I, Penthicodes atomaria tended by a cockroach, Cardamom Mts, 4.VIII.2013 (A. Anker). J, P. pulchella, Siem Reap, 18.IX.2013 (S. De Greef). K, P. variegata, Mondulkiri, O Reang District, 19.V.2015 (B. Barca). L–M, Polydictya tricolor, Siem Reap, Angkor, 1.VIII.2013 (S. De Greef). N–O, Polydictya sp., 8 km NNW Angkor, 6.XI.2013 (E. Smith).
Fig. 1 in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 1. Call to collaboration to the study of Fulgoridae of Cambodia posted on Facebook on May 18th, 2015.
Fig. 3. A–B, Pyrops candelaria. A in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 3. A–B, Pyrops candelaria. A, Chambok, 1.IX.2014 (S. Phauk). B, Kampong Tralach, 21.IX.2013 (O. Rodriguez). C–D, P. coelestinus, Chambok, 1.IX.2014 (S. Phauk). E, P. condorinus, Koh Kong, Tatai, 24.V.2015 (G. Chartier). F, P. ducalis, Mondulkiri, Seima Forest, 13.V.2015 (B. Barca). G–I, P. peguensis. G, Tumpor, Cardamom, 12.VIII.2009 (J. Holden). H, Chambok, 5.V.2015 (J. Constant). I, idem, biotope. J, P. spinolae, Ratanakiri, Veun Sai Siem Pang, 23.II.2015 (Marduk). K, P. viridirostris, Chambok, 7.V.2015 (J. Constant). L–N, Saiva gemmata. L, nymph, Chambok, 7 May 2015 (J. Constant). M, adult tended by a cockroach, Chambok, 7.V.2015 (J. Constant). N, Mondulkiri, Seima Forest, 13.V.2015 (B. Barca). O–Q, Zanna sp. O–P, Koh Kong, Tatai, 1.XI.2012 (G. Chartier). Q, Kampot, 21.XII.2013 (K.W. Meier-Doernberg).
Data from: A mobile application–based citizen science product to compile bird observations
<p>This repository contains the data and codes to reproduce the analysis of manuscript: "A mobile application–based citizen science product to compile bird observations":</p> <p>Nokelainen, O, Lauha, P, Andrejeff, S, Hänninen, J, Inkinen, J, Kallio, A, Lehto, HJ, Mutanen, M, Paavola, R, Schiestl-Aalto, P, Somervuo, P, Sundell, J, Talaskivi, J, Vallinmäki, M, Vancraeyenest, A, Lehtiö, A and Ovaskainen, O. 2024. A Mobile Application– Based Citizen Science Product to Compile Bird Observations. <em>Citizen Science: Theory and Practice, </em>9(1): 24, pp. 1–14. DOI: <a href="https://doi.org/10.5334/cstp.710" target="_blank" rel="noopener noreferrer">https://doi.org/10.5334/cstp.710</a></p>
Online Appendix for the HICSS2025 Paper: Bridging Citizens and Public Sector Employees through an Open Employee-driven Innovation Process - A Design Science Research Study
<p>Online Appendix for the HICSS2025 Paper: Bridging Citizens and Public Sector Employees through an Open Employee-driven Innovation Process - A Design Science Research Study</p>
Citizen_science_in_lockdown_data_v4.0
<p>The dataset is part of the manuscript "Global impact of the COVID-19 lockdown on biodiversity data collection" by Stephanie Roilo, Ruben Remelgado, Jan O. Engler, and Anna F. Cord, which is currently under revision. Please acknowledge this publication when using the data.<br> </p> <p>### DATA DESCRIPTION ####</p> <p>This zipped folder contains:<br>- the file "Data_249_countries_20230321.csv", which, for each day between January 1st 2019 and October 15th 2022, collates the following data:<br> -- the number of human observations (n_HumObs) per day collected in the Global Biodiversity Information Facility (GBIF) as of March 21st 2023, <br> -- the number of eBird records per day in GBIF (n_CLO) as of March 21st 2023, <br> -- the stringency index from the Oxford COVID-19 Government Response Tracker, <br> -- the change in park visitors and the change in time spent at home from the Google Community Mobility Reports (https://support.google.com/covid19-mobility), <br> -- information on the week day (weekday), week number (weeknr), and year (year),<br> -- the country, or dependent territory, name (Country) and its two-lettered code (country_iso2)<br> for 249 countries or dependent territories according to the ISO 3166 country code list;<br>- 40 files (one per country) named "CLO_XX_March15_May1_2019_2020.csv", which summarise, for each day between March 15th 2019 and May 1st 2019:<br> -- the number of eBird records (n_CLO) collected and stored in the Global Biodiversity Information Facility (GBIF), <br> -- the number of unique observers (n_obsr), <br> -- and the observers' IDs (ID_obsr), <br> for each country or dependent territory separately. These data were downloaded between the 9th and the 30th of January 2024.<br>- the file "Linear_regression_full_dataset_20240904.xlsx", which contains the dataset used in the linear regression explaining the change in GBIF records relative to the stringency index, human mobility variables, and countries' economic class and population size.</p>
D4.6- Validation of citizen science data
<p>This document is a deliverable of the SCORE project, funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101003534. The aim of this document is to outline the dual objectives essential to the success of the SCORE project: first, providing an overview of the methods and practices for validating citizen science data, and second, integrating these validated datawith institutional monitoring systems. Validating citizen science data is crucial to ensuring the accuracy and reliability of information collected by non-professional volunteers. Once validated, this data can be integrated into institutional systems to enhance environmental monitoring, particularly in urban coastal areas where detailed and localised data can significantly improve monitoring capabilities and decision-making.</p>
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