Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
210
datasets available to search
ShareScore release 0.9.0
Dataset results
210 results for “citizen data”
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>
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>
Bird strikes at commercial airports explained by citizen science and weather radar data
<p>1. Aircraft collisions with birds span the entire history of human aviation, including fatal collisions during some of the first powered human flights. Much effort has been expended to reduce such collisions, but increased knowledge about bird movements and species occurrence could dramatically improve decision support and proactive measures to reduce them. Migratory movements of birds pose a unique, often overlooked, threat to aviation that is particularly difficult for individual airports to monitor and predict: the occurrence of birds vary extensively in space and time at the local scales of airport responses.</p> <p>2. We use two publicly available datasets, radar data from the US NEXRAD network characterizing migration movements and eBird data collected by citizen scientists to map bird movements and species composition with low human effort expenditures but high temporal and spatial resolution relative to other large scale bird survey methods. As a test case we compare these results from weather radar distributions and eBird species composition with detailed bird strike records from three major New York airports.</p> <p>3. We show that weather radar based estimates of migration intensity can accurately predict probability of bird strikes, with 80% of the variation in bird strikes across the year explained by the average amount of migratory movements captured on weather radar. We also show that eBird based estimates of species occurrence can, using species' body mass and flocking propensity, accurately predict when most damaging strikes occur.</p> <p>4. Synthesis and applications: Our results highlight the power of federating datasets with movement and distribution data for developing better and more taxonomically and ecologically tuned models of likelihood of strikes occurring and severity of strikes. By better understanding when, and where, different species occur, airports across the world can predict seasonal periods of collision risks with greater temporal and spatial resolution; such predictions include potential to predict when the most severe and damaging strikes may occur.</p>
Data from: Tradeoffs in moving citizen-based anuran call surveys online during the SARS-CoV-2 pandemic: lessons from rural Appalachia, USA
<p>Citizen science approaches provide adaptable methodologies for enhancing the natural history knowledge of understudied taxa and engaging underserved populations with biodiversity. However, transitions to remote, virtual training and participant recruitment in response to public health crises like the SARS-CoV-2 pandemic have the potential to disrupt citizen science projects. We present a comparison of outputs from a citizen science initiative built around call surveys for the Mountain Chorus Frog (<i>Pseudacris brachyphona</i>), an understudied anuran, in Appalachian Virginia, USA prior to and during the SARS-CoV-2 pandemic. A transition to virtual training in this initiative did not lead to a decrease in scientific output and led to unexpected natural history insight about our focal taxon; however, a reliance on virtual instruction did decrease overall participation by local residents, particularly for rural K-12 students. We discuss the tradeoffs exhibited by the adaptation of our initiative to a virtual format and provide recommendations for other citizen science initiatives facing similar restrictions in the face of current and future public health crises.</p>
Cannot see the diversity for all the species: evaluating inclusion criteria for local species lists when using abundant citizen science data
Abundant citizen science data on species occurrences is becoming increasingly available and enables identifying composition of communities occurring at multiple sites with high temporal resolution. However, for species displaying temporary patterns of local occurrences, i.e. that are transient to some sites, biodiversity measures are clearly dependent on the criteria used to include species into local species lists. Using abundant opportunistic citizen science data from frequently visited wetlands we investigated the sensitivity of α- and β-diversity estimates to the use raw vs. detection-corrected data and to the use of inclusion criteria for species presence reflecting alternative site use. We tested 7 inclusion criteria (with varying number of days required to be present) on time series of daily occurrence status during a breeding season of 90 days for 77 wetland bird species. We show that even when opportunistic presence-only observation data is abundant, raw data may not produce reliable local species richness estimates and rank sites very differently in terms of species richness. Furthermore, occupancy model based - and - diversity estimates were sensitive to the inclusion criteria used. Total species lists (all species observed at least once during a season) may therefore mask diversity differences among sites in local communities of species, by e.g. including vagrant species on potentially breeding communities and change the relative rank order of sites in terms of species richness. Very high sampling effort does not necessarily free opportunistic data from its inherent bias and can produce a pattern in which many species are observed at least once almost everywhere, thus leading to a possible paradox: the large amount of biological information may hinder its usefulness. Therefore, when prioritizing among sites to manage or preserve species diversity estimates need to be carefully related to relevant inclusion criteria depending on the diversity estimate in focus.
Adopt a Pixel 3 km: A Multiscale Data Set Linking Remotely Sensed Land Cover Imagery with Field Based Citizen Science Observation
<p>These datasets were used in an article submitted to the journal Frontiers in Climate in 2021: <a href="https://www.frontiersin.org/articles/10.3389/fclim.2021.658063/full">https://www.frontiersin.org/articles/10.3389/fclim.2021.658063/full</a></p> <p>Further supplemental links (including general information about GLOBE data) can be accessed at <a href="https://observer.globe.gov/get-data/mosquito-habitat-data">https://observer.globe.gov/get-data/mosquito-habitat-data</a>.</p>
FIG. 2 in The Utility of Acoustic Citizen Science Data in Understanding Geographic Distributions of Morphologically Conserved Species: Frogs in the Litoria phyllochroa Species Group
FIG. 2. Map of eastern Australia including the states of Queensland (QLD), New South Wales (NSW), Australian Capital Territory (ACT), and Victoria (VIC) showing the mapped ranges of known extant species in the Litoria phyllochroa group as informed by data from FrogID and the Atlas of Living Australia, including areas where species' ranges may overlap.
FIG. 1 in The Utility of Acoustic Citizen Science Data in Understanding Geographic Distributions of Morphologically Conserved Species: Frogs in the Litoria phyllochroa Species Group
FIG. 1. Map of eastern Australia including the states of Queensland (QLD), New South Wales (NSW), Australian Capital Territory (ACT), and Victoria (VIC) showing records of known extant species of the Litoria phyllochroa group from the Atlas of Living Australia (ALA; A), FrogID (B), and a combined dataset containing both FrogID records and those deemed spatially and taxonomically reliable from ALA (C).
Treating the End of the Data Life Cycle as a First-Class Citizen in Data Engineering - Datasets
<p>Additional data for paper "Treating the End of the Data Life Cycle as a First-Class Citizen in Data Engineering".</p>
Cannot see the diversity for all the species: evaluating inclusion criteria for local species lists when using abundant citizen science data
Open the record for dataset details and reuse information.
Data from: Estimates of observer expertise improve species distributions from citizen science data
Open the record for dataset details and reuse information.
Data from: DIY meteorology: use of citizen science to monitor snow dynamics in a data-sparse city
Open the record for dataset details and reuse information.
Data from: Can opportunistically-collected Citizen Science data fill a data gap for habitat suitability models of less common species?
Open the record for dataset details and reuse information.
Data supporting: Assessment of the performance of nonfouling polymer hydrogels utilizing citizen scientists
Open the record for dataset details and reuse information.
Data from: Identifying conservation priorities for gorgonian forests in Italian coastal waters with multiple methods including citizen science and social media content analysis
Open the record for dataset details and reuse information.
Data from: Tradeoffs in moving citizen-based anuran call surveys online during the SARS-CoV-2 pandemic: lessons from rural Appalachia, USA
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
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.