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317 results for “Experts”
Data from: Assessing cumulative impacts of forest development on the distribution of furbearers using expert-based habitat modeling
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Bird species classifications from Munich-Laim: Expert vs. BirdNET (multi-parameter) results
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Expert-based assessment of rewilding indicates progress at site-level, yet challenges for upscaling
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Data from: Use of opportunistic sightings and expert knowledge to predict and compare Whooping Crane stopover habitat
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Data from: Group elicitations yield more consistent, yet more uncertain experts in understanding risks to ecosystem services in New Zealand bays
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Supplementary material 1 from: Rowley JJL, Callaghan CT (2020) The FrogID dataset: expert-validated occurrence records of Australia's frogs collected by citizen scientists. ZooKeys 912: 139-151. https://doi.org/10.3897/zookeys.912.38253
: Data type: Species data
Figure 2 from: Rowley JJL, Callaghan CT (2020) The FrogID dataset: expert-validated occurrence records of Australia's frogs collected by citizen scientists. ZooKeys 912: 139-151. https://doi.org/10.3897/zookeys.912.38253
Figure 2 Frequency histogram for the 172 species published in our openly accessible dataset, showing the number of records (on a log-scale) and how many species have that associated number of records.
Figure 1 from: Rowley JJL, Callaghan CT (2020) The FrogID dataset: expert-validated occurrence records of Australia's frogs collected by citizen scientists. ZooKeys 912: 139-151. https://doi.org/10.3897/zookeys.912.38253
Figure 1 Photographs of the top six species recorded in the first year FrogID. 1Crinia signifera2Limnodynastes peronii3Litoria peronii4Litoria fallax5Limnodynastes tasmaniensis6Litoria ewingii.
Expert VIsion Chat - Social Media Issues, Challenges and Requirements
<p>Expert VIsion Chat - Social Media Issues, Challenges and Requirements. We took questions from end users across Europe, US and India</p>
Data from: Control at stability's edge minimizes energetic costs: expert stick balancing
Stick balancing on the fingertip is a complex voluntary motor task that requires the stabilization of an unstable system. For seated expert stick balancers, the time delay is 0.23 s, the shortest stick that can be balanced for 240 s is 0.32 m and there is a Embedded Image° dead zone for the estimation of the vertical displacement angle in the saggital plane. These observations motivate a switching-type, pendulum–cart model for balance control which uses an internal model to compensate for the time delay by predicting the sensory consequences of the stick's movements. Numerical simulations using the semi-discretization method suggest that the feedback gains are tuned near the edge of stability. For these choices of the feedback gains, the cost function which takes into account the position of the fingertip and the corrective forces is minimized. Thus, expert stick balancers optimize control with a combination of quick manoeuvrability and minimum energy expenditures.
Data from: The role of deliberate practice in expert performance: revisiting Ericsson, Krampe, & Tesch-Römer
We sought to replicate Ericsson, Krampe, and Tesch-Römer's (1993) seminal study on deliberate practice. Ericsson et al. (1993) found that differences in retrospective estimates of accumulated amounts of deliberate practice corresponded to each skill level of student violinists. They concluded, "individual differences in ultimate performance can largely be accounted for by differential amounts of past and current levels of practice" (p. 392). We reproduced the methodology with notable exceptions, namely 1) employing a double-blind procedure, 2) conducting analyses better suited to the study design, and 3) testing previously-unanswered questions about teacher-designed practice—that is, we examined the way Ericsson et al. (1993) operationalized deliberate practice (practice alone), and their theoretical but previously unmeasured definition of deliberate practice (teacher-designed practice), and compared them. We did not replicate the core finding, that accumulated amounts of deliberate practice corresponded to each skill level. Overall, the size of the effect was substantial, but considerably smaller than the original study's effect size. Teacher-designed practice was perceived as less relevant to improving performance on the violin than practice alone. Further, amount of teacher-designed practice did not account for more variance in performance than amount of practice alone. Implications for the deliberate practice theory are discussed.
Data from: Evidence-based tool surpasses expert opinion in predicting probability of eradication of aquatic nonindigenous species
The main objective of evidence-based management is to promote use of scientific data in the decision-making process of managers, with data either complementing or replacing expert knowledge. It is expected that this will increase the efficiency of environmental interventions. However, the relative accuracy and precision of evidence-based tools and expert knowledge has seldom been evaluated. It is therefore essential to verify whether such tools provide better decision support before advocating their use. We conducted an elicitation survey in which experts were asked to (1) evaluate the influence of various factors on the success of eradication programs for aquatic nonindigenous species and (2) provide probabilities of success for real case studies for which we knew the outcome. The responses of experts were compared with the results and predictions of a newly developed evidence-based tool: a statistical model calibrated with a meta-analysis of case studies designed to evaluate probability of eradication. Experts and the model generally identified the same factors as influencing the probability of success. However, the model provided much more accurate estimates for the probability of eradication than expert opinion, strongly suggesting that an evidence-based approach is superior to expert knowledge in this case. Uncertainty surrounding the predictions of the evidence-based tool was similar to among-expert variability. Finally, a model based on ≥30 case studies returned more accurate predictions than expert opinion. We conclude that decision-making processes based on expert judgment would greatly benefit from incorporating evidence-based tools.
Data from: Measuring agreement among experts in classifying camera images of similar species
Camera trapping and solicitation of wildlife images through citizen science have become common tools in ecological research. Such studies collect many wildlife images for which correct species classification is crucial; even low misclassification rates can result in erroneous estimation of the geographic range or habitat use of a species, potentially hindering conservation or management efforts. However, some species are difficult to tell apart, making species classification challenging - but the literature on classification agreement rates among experts remains sparse. Here, we measure agreement among experts in distinguishing between images of two similar congeneric species, bobcats (Lynx rufus) and Canada lynx (L. canadensis). We asked experts to classify the species in selected images to test whether the season, background habitat, time of day, and the visible features of each animal (e.g., face, legs, tail) affected agreement among experts about the species in each image. Overall, experts had moderate agreement (Fleiss' kappa = 0.64), but experts had varying levels of agreement depending on these image characteristics. Most images (71%) had ≥1 expert classification of 'unknown', and many images (39%) had some experts classify the image as 'bobcat' while others classified it as 'lynx'. Further, experts were inconsistent even with themselves, changing their classifications of numerous images when they were asked to reclassify the same images months later. These results suggest that classification of images by a single expert is unreliable for similar-looking species. Most of the images did obtain a clear majority classification from the experts, although we emphasize that even majority classifications may be incorrect. We recommend that researchers using wildlife images consult multiple species experts to increase confidence in their image classifications of similar sympatric species. Still, when the presence of a species with similar sympatrics must be conclusive, physical or genetic evidence should be required.
Data from Choice Architecture experiment with Experts
<p>we present the evaluation of the interventions we designed using the Choice-Architecture-based Interventions Design procedure for SDR. The evaluation is of functional character, we seek to test whether the procedure can produce interventions that actually persuade scientists (i.e., users) to change their data-sharing behaviour. To perform the evaluation, we ran a pseudo-randomised control crossover trial experiment. In achieving so, we used the implementations of the procedure that were presented in my thesis as treatments.</p> <p>We found that Emphasis Framing can affect scientists’ data-sharing behaviour, when having to decide to share a dataset upon publication, and their satisfaction when performing the activity. On the other hand, we ran into difficulties to recruit participants to test all the interventions defined here.</p> <p> </p> <p>This data support my thesis work.</p>
Supplementary material 1 from: Dehnen-Schmutz K, Pescott OL, Booy O, Walker KJ (2022) Integrating expert knowledge at regional and national scales improves impact assessments of non-native species. NeoBiota 77: 79-100. https://doi.org/10.3897/neobiota.77.89448
Survey and Tables S1–S3
Supplementary material 3 from: Dehnen-Schmutz K, Pescott OL, Booy O, Walker KJ (2022) Integrating expert knowledge at regional and national scales improves impact assessments of non-native species. NeoBiota 77: 79-100. https://doi.org/10.3897/neobiota.77.89448
Figure S1
Supplementary material 2 from: Dehnen-Schmutz K, Pescott OL, Booy O, Walker KJ (2022) Integrating expert knowledge at regional and national scales improves impact assessments of non-native species. NeoBiota 77: 79-100. https://doi.org/10.3897/neobiota.77.89448
Table S2
Expert opinion and model of natural pest control in agricultural landscapes
<table> <tbody> <tr> <td> <div>The survey asks expert How they would estimate the capacity different land use (herbaceous semi-natural habitat, forest edge, forest core) to support the abundance of the following insect groups in the landscape: ‘complete generalists’ ‘specialized predators’, ‘parasitoids’. The score is provided on a scale from 0 (no relevant capacity) to 10 (very high relevance). For each opinion, experts provided a level of confidence: 1 'I don’t feel confident with my score', 2 'I feel fairly confident with my score” and 3: 'I feel confident with my score'. In the same way experts were asked to rate a baseline scenario of agricultural fields defined as as a conventionally managed average field (with an average field size of 3-7 ha, fertilization and pesticide application compared to the region of interest) of medium crop diversity with three functional groups over 4 years (e.g., cereal, oilseed crop, root crop). Then experts were asked how much a single change from one practices to an alternative one (e.g., conventional to organic) would affect the score they provided - 50 to - 100% = considerably worse -20 to -50%= notably better -1 to -20% = slightly worse 0 = no change +1 to 20% = slightly better +20 to 50%= notably better + 50 to 100%= considerably better +100 to 200% = extremely better/</div> <div> </div> <div>Finally experts were asked about the distance at which landscape change affect the abundance of the three group of natural enemies.</div> <div> </div> <div>The survey was conducted from April to June 2021.</div> <div> </div> <div>The scores for each practices are derived by mixed effect model and provided in the file Code_Habitats.csv. This file is used in the R model provided here to calculate natural pest control using the weighted moving window describe in Riggi et al., 2024 Ecological Indicators. NPC_Model_Riggi.R is the code of the model Fields_AOI.shp represent an example of fields with agricultural land use information CadasterEnv_AOI.tif is the land use map CADASTERENV_Label_to_change_input.csv allows the reclassification of land use into forest edge, core and herbaceous areas Code_Habitats-1.csv contrains the values associated to each support of land use for natural pest control. (2024-02-06) <div>Collapse Description [-]</div> </div> </td> </tr> <tr></tr> </tbody> </table>
Mapping the páramo land cover in the Northern Andes: Figure S4 Expert land-cover classification of the Andean páramo and distribution according to three groups: natural vegetation, natural abiotic and anthropogenic, and 12 classes
<p>The Andean páramo is a biodiverse and vulnerable tropical high-mountain region, whose spatio-ecological patterns remain understudied. The lack of general characterization of its overall extent, land-cover classes, and treeline spatial features hinders our capacity to understand its responses to human impacts and predict future land-system changes. To address this knowledge gap, we classified the land-cover of the páramo in the northern Andes. Moreover, we estimated 1) the páramo's total extent and distribution among countries, 2) the relative extent of 12 of its main land-cover classes, categorized into <i>natural vegetation, natural abiotic</i> and <i>anthropogenic </i>groups, and 3) the preliminary position and anthropogenic influence of its bordering treeline. Relying on Landsat 8 imagery, we performed hybrid manual-automated classifications using the Maximum Likelihood and Random Forest algorithms. The two resulting <i>final classifications</i> were manually checked for errors compared to Google Earth and VegPáramo data, and used to produce the <i>expert classification</i>. Finally, we delimited the treeline based on regional forest connectivity, and applied it to the expert classification to evaluate páramo elevations, surface areas and land-cover classes above the treeline. The páramo extent was estimated at 24,301 km<sup>2</sup>, distributed between Ecuador (47%), Colombia (43%), Venezuela (8%) and Peru (2%). Natural vegetation, especially shrublands, rosette plant communities and grasslands were dominant (altogether, 65%), whereas classes reflecting intense land-use covered 12% overall. The average treeline reached 3546 m and was bordered uphill at 16% with anthropogenic land-cover classes. The páramo's extent is smaller than previously suggested. It remains a (semi-) natural region, yet crop and pasture expansion towards high elevations is a critical concern for long-term sustainability. Future research can build on our findings to predict land-system changes and assess priority areas for conservation. We recommend for future research to focus on remnant forest patches and treeline connectivity in priority.</p>
Supplementary material 2 from: Kracke I, Essl F, Zulka KP, Schindler S (2021) Risks and opportunities of assisted colonization: the perspectives of experts. Nature Conservation 45: 63-84. https://doi.org/10.3897/natureconservation.45.72554
The questions used in the online survey
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.