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210 results for “citizen data”
Assessing cooper’s hawk (<em>Astur cooperii</em>) and sharp-shinned hawk (<em>Accipiter striatus</em>) prey size and species composition using citizen science data
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Data from: Continent‐scale phenotype mapping using citizen scientists’ photographs
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Data coverage, biases, and trends in a global citizen-science resource for monitoring avian diversity
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Data from: A pan-European citizen science study shows population size, climate and land use are related to biased morph ratios in the heterostylous plant Primula veris
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Citizen science data on the presence of invasive mosquitoes in Hungary
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Data from: Co-design of a citizen science study: Unlocking the potential of eDNA for volunteer freshwater monitoring
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Data from: Facebook groups as citizen science tools for plant species monitoring
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Data from: Citizen science and color pattern analysis indicate unreported Batesian mimicry between Neotropical snakes
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Data from: Bird predation on Roseau cane scale as revealed by a web image search and querying a citizen monitoring database
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Citizen science data reveal altitudinal movement and seasonal ecosystem use by hummingbirds in the Andes Mountains
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Improving citizen science data for long-term monitoring of plant species
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Louse flies (Diptera: Hippoboscidae): United Kingdom, Republic of Ireland and Isle of Man: Citizen science data: Part 1, mapping
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Data from: Citizen science can complement professional invasive plant surveys and improve estimates of suitable habitat
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iSCAPE Citizen Science Workshops Data
<p><strong>Dataset Description</strong></p> <p>This dataset contains all the sensor data recorded during the Citizen Science Workshops during the iSCAPE project . Several workshops were held as part of the Citizen Science activities during the project in the cities of Vantaa, Dublin, Bologna, Bottrop, Hasselt and Guildford. Each csv file contains time series data of each experiment, and the yaml files contain the lists of devices used in each site.</p> <p><strong>Sensors</strong></p> <p>The sensors used are herein referred as Citizen Kits or Smart Citizen Kits, and are a set of modular hardware components that feature a selection of low cost sensors for environmental monitoring listed below. The hardware is licensed under <a href="https://www.ohwr.org/licenses/cern-ohl/license_versions/v1.2">CERN Open Hardware License V1.2</a> and is fully described in the HardwareX Open Access publication: <a href="https://doi.org/10.1016/j.ohx.2019.e00070">https://doi.org/10.1016/j.ohx.2019.e00070</a>. The sensor documentation can be found at <a href="https://docs.smartcitizen.me">https://docs.smartcitizen.me</a> and with this DOI at Zenodo: <a href="https://doi.org/10.5281/zenodo.2555029">https://doi.org/10.5281/zenodo.2555029</a>.</p> <p>In the list below, the different sensors for the Citizen Kits are detailed, and their [CHANNELS] in the csv files above linked.</p> <p> </p> <ul> <li>Air temperature (ºC): Sensirion SHT-31 [TEMP]</li> <li>Relative Humidity (%rh): Sensirion SHT-31 [HUM]</li> <li>Noise level (dBA): Invensense ICS-434342 [NOISE_A]</li> <li>Ambient light (lux): Rohm BH1721FVC [LIGHT]</li> <li>Barometric pressure (kPa): NXP MPL3115A26 [PRESS]</li> <li>Particulate Matter PM 1 / 2.5 / 10 (µg/m3) Planttower PMS 5003 [EXT_PM_1,EXT_PM_25,EXT_PM_10]</li> </ul> <p><strong>How to find the data</strong></p> <p>Each yaml file contains the description of a test. Each test is comprised of recordings of several devices in the same location and during the same period. Each yaml file is comprised of the following fields:</p> <ul> <li>author: who has been in charge of performing the test (internal reference - not relevant)</li> <li>comment: describing in general terms what was done in the test, and with what purpose</li> <li>commit: the firmware commit (in the case of Smart Citizen devices) with which the test was performed, for development purposes only</li> <li>devices: a descriptor containing different fields for traceability (below)</li> <li>id: the test name</li> <li>project: within the test was performed, in this case it is always iscape</li> <li>report: if there is any report analysing the test</li> <li>type_test: indoor, oudoor test or other.</li> </ul> <p><strong>Description of devices entry</strong></p> <p>For each device that was used in the test, two generic types are used:</p> <ul> <li>low cost sensors (type: STATION or KIT)</li> <li>high end sensors (type: REFERENCE)</li> </ul> <p>For <strong>low cost Smart Citizen sensors</strong>, the fields are:</p> <ul> <li>alphasense: electrochemical sensors device ids, by pollutant (for manufacturer calibration) and slots in which they were placed</li> <li>device_id: device id in Smartcitizen API</li> <li>fileNameInfo: not used</li> <li>fileNameProc: (only if source = csv is specified) 2019-03_EXT_UCD_URBAN_BACKGROUND_API_CITY_COUNCIL_REF.csv</li> <li>fileNameRaw: (only if source = csv is used) raw file name</li> <li>frequency: original recording frequency</li> <li>location: for timezone correction only, not accurate</li> <li>max_date: last recording date</li> <li>min_date: first recording date</li> <li>name: self-explanatory</li> <li>pm_sensor: if there was a pm sensor connected (all of them are PMS5003 if no sensor is specified)</li> <li>source: api or csv</li> <li>type: STATION (KIT + Alphasense + PM board with two PMS5003) or KIT</li> <li>version: smartcitizen hardware version</li> </ul> <p>For <strong>high end</strong> sensors, the fields are:</p> <ul> <li>channels: which channels the device was recording for internal convertion <ul> <li>names: which are the columns in the csv file</li> <li>pollutants: which pollutants do they respectively refer to</li> <li>units: the units of these pollutants</li> </ul> </li> <li>equipment: the brand of the analyser</li> <li>fileNameProc: same as above</li> <li>fileNameRaw: same as above</li> <li>index: format in which the timeindex is done, for parsing purposes <ul> <li>format: (example '%Y-%m-%d %H:%M:%S')</li> <li>frequency: frequency at which the device was recorded</li> <li>name: column name</li> </ul> </li> <li>location: same as above</li> <li>name: name of the device</li> <li>type: REFERENCE (always for these devices)</li> <li>source: csv</li> </ul> <p><strong>iSCAPE Dataset Reference Numbers</strong></p> <p>The datasets here presented are related to the following iSCAPE dataset reference numbers:</p> <ul> <li>DS_TS_093</li> <li>DS_TS_094</li> <li>DS_TS_095</li> <li>DS_TS_096</li> <li>DS_TS_097</li> <li>DS_TS_098</li> </ul>
Connecting digital citizen science data quality issue to solution mechanism table
<p>A table explaining how to solve data quality issues in digital citizen science. A total of 35 issues and 64 mechanisms to solve them are proposed</p>
"What Influences Citizens' Expectations towards Digital Government?" - Research Data
<p>This excel sheet contains the data from the survey entitled "What Influences Citizens' Expectations towards Digital Government?".</p> <p> </p>
FIGURE 1 in Ontogeny of an arlequin: morphological and colour pattern changes from juvenile to adult in Gnathophyllum elegans (Risso, 1816) (Decapoda: Palaemonidae), traced through citizen science and social media data mining
FIGURE 1. Morphological and colour pattern changes from juvenile to adult in Gnathophyllum elegans (Risso, 1816). A–C. Specimens from Capo Noli (Italy, Mediterranean Sea) (~44.199232N, 8.420455E), 15–16 m, on anthropogenic debris laying on a detritic bottom, 5–13.IX.2020. A. Photo by Walter Bassi. B–C. Photos by Alessandro Raho. D. Specimen from La Laja beach, Gran Canaria (Spain, Atlantic Ocean) (~28.060335N, -15.418428E), 1 m, amidst algae in a tide pool, 29.VIII.2017. Photo by Alberto Navarro. E. Specimen from Bat Galim reef, Haifa (Israel, Mediterranean Sea) (~32.833317N, 34.97431E), 2 m, under a rock on a rocky bottom, ~2017. Photo by Sarah Ohayon. F. Specimen from Capo Caccia, Sardinia (Italy, Mediterranean Sea) (~40.565506N, 8.165579E), 5 m, detritic bottom with rocks, 28.VIII.2015. Photo by Marco Colombo.
Dispatches from the neighborhood watch: using citizen science and field survey data to document color morph frequency in space and time
<p>Heritable color polymorphisms have a long history of study in evolutionary biology, though they are less frequently examined today than in the past. These systems, where multiple discrete, visually identifiable color phenotypes co-occur in the same population, are valuable for tracking evolutionary change and ascertaining the relative importance of different evolutionary mechanisms. Here, we use a combination of citizen science data and field surveys in the Great Lakes region of North America to identify patterns of color morph frequencies in the eastern gray squirrel (<i>Sciurus carolinensis</i>). Using over 68,000 individual squirrel records from both large and small spatial scales, we identify the following patterns: (1) the melanistic (black) phenotype is often localized but nonetheless widespread throughout the Great Lakes region, occurring in all states and provinces sampled. (2) In Ohio, where intensive surveys were performed, there is a weak but significantly positive association between color morph frequency and geographic proximity of populations. Nonetheless, even nearby populations often had radically different frequencies of the melanistic morph, which ranged from 0 to 96%. These patterns were mosaic rather than clinal. (3) In the Wooster, Ohio population, which had over eight years of continuous data on color morph frequency representing nearly 40,000 records, we found that the frequency of the melanistic morph increased gradually over time on some survey routes but decreased or did not change over time on others. These differences were statistically significant and occurred at very small spatial scales (on the order of hundreds of meters). Together, these patterns are suggestive of genetic drift as an important mechanism of evolutionary change in this system. We argue that studies of color polymorphism are still quite valuable in advancing our understanding of fundamental evolutionary processes, especially when coupled with the growing availability of data from citizen science efforts.</p>
Data from: Historical citizen science to understand and predict climate-driven trout decline
Historical species records offer an excellent opportunity to test the predictive ability of range forecasts under climate change, but researchers often consider that historical records are scarce and unreliable, besides the datasets collected by renowned naturalists. Here, we demonstrate the relevance of biodiversity records developed through citizen-science initiatives generated outside the natural sciences academia. We used a Spanish geographical dictionary from the mid-nineteenth century to compile over 10 000 freshwater fish records, including almost 4 000 brown trout (Salmo trutta) citations, and constructed a historical presence–absence dataset covering over 2 000 10 × 10 km cells, which is comparable to present-day data. There has been a clear reduction in trout range in the past 150 years, coinciding with a generalized warming. We show that current trout distribution can be accurately predicted based on historical records and past and present values of three air temperature variables. The models indicate a consistent decline of average suitability of around 25% between 1850s and 2000s, which is expected to surpass 40% by the 2050s. We stress the largely unexplored potential of historical species records from non-academic sources to open new pathways for long-term global change science.
Data from: Non-invasive genetic monitoring involving citizen science enables reconstruction of current pack dynamics in a re-establishing wolf population
Background: Carnivores are re-establishing in many human-populated areas, where their presence is often contentious. Reaching consensus over management decisions is often hampered by a dispute about the size of the local carnivore population. Understanding the reproductive dynamics and individual movements of the carnivores can provide support for management decisions, but individual-level information can be difficult to obtain from elusive, wide-ranging species. Non-invasive genetic sampling can yield such information, but makes subsequent reconstruction of population history challenging due to incomplete population coverage and error-prone data. Here, we combine a collaborative, volunteer-based sampling scheme with Bayesian pedigree reconstruction to describe the pack dynamics of an establishing grey wolf (Canis lupus) population in south-west Finland, where wolf breeding was recorded in 2006 for the first time in over a century. Results: Using DNA extracted mainly from faeces collected since 2008, we identified 81 individual wolves and assigned credible full parentages to 70 of these and partial parentages to a further 9, revealing 7 breeding pairs. Individuals used a range of strategies to obtain breeding opportunities, including dispersal to established or new packs, long-distance migration and inheriting breeding roles. Gene flow occurred between all packs but inbreeding events were rare. Conclusions: These findings demonstrate that characterizing ongoing pack dynamics can provide detailed, locally-relevant insight into the ecology of contentious species such as the wolf. Involving various stakeholders in data collection makes these results more likely to be accepted as unbiased and hence reliable grounds for management decisions.
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.