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317
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ShareScore release 0.9.0
Dataset results
317 results for “Experts”
Improving the Quality of Patient Care by Using a Clinical Expert System.
ClinicalTrials.gov study NCT00430755. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Clinical Evaluation of the BlueDop Vascular Expert for Assessing Peripheral Arterial Disease
ClinicalTrials.gov study NCT05073510. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Measuring agreement among experts in classifying camera images of similar species
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Data from: The role of deliberate practice in expert performance: revisiting Ericsson, Krampe, & Tesch-Römer
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Data from: Control at stability's edge minimizes energetic costs: expert stick balancing
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List of 131 harlequin toad species (in alphabetical order) with information on occurence, population status 2004 and 2022, threats, IUCN Red List status and contributing experts
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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
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Data from: Experts’ consensus on use of electronic cigarettes: a Delphi survey from Switzerland
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Data from: Prioritizing management actions for invasive populations using cost, efficacy, demography, and expert opinion for 14 plant species worldwide
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Data from: Evidence-based tool surpasses expert opinion in predicting probability of eradication of aquatic nonindigenous species
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Figure 3 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 3 Occurrence records of calling frogs across Australia during year 1 of the FrogID project.
Expert demonstrations for O-NAIL
<p>This archive contains expert demonstrations for the O-NAIL experiments. See https://www.github.com/OlegArenz/O-NAIL for the implementation and more details</p>
Data sets collected as part of the workshops conducted with secondary school teachers and TEL experts.
<p>This dataset contains all the material collected during the workshops with teachers and TEL experts. It consists of activities designed by teachers, canva indicating relevant information and platform designs by means of charts and illustrations. It is part of two studies published by Calvera-Isabal M. referenced below (one pending publication). </p> <p>This work has been funded by PID2020-112584RB-C33 funded by MCIN/AEI/10.13039/501100011033, the CS Track project, EU Horizon 2020 programme [grant agreement No 872522], grant for activities to increase the social impact of research in 2021 from Universitat Pompeu Fabra (UPF) and H2O Learn project PID2020-112584RB-C33 funded by MCIN/ AEI / 10.13039/501100011033.</p> <p>Please contact miriam.calvera@upf.edu for data availability.</p>
Safer Spaces für queere Asylbewerber*innen: Expert*inneninterviews mit LSBT-Organisationen in Deutschland
<p>Im Jahr 2018 wurden Expert*inneninterviews mit LSBT-Organisationen zu ihrer Arbeit mit queeren Asylbewerber*innen und Geflüchteten geführt. Alle Beteiligten gaben ihr Einverständnis, die Transkripte für die Forschung zu verwenden und zu publizieren. Das Interview E4 fand im Rahmen einen studentischen Seminars statt. Die Autor*in begleitete die Student*innen bei der Durchführung des Interviews mit einem Vertreter von Queer Haven Potsdam. Alle weiteren Interviews wurden von der Autor*in allein durchgeführt. </p>
Variation in forest root image annotation by experts, novices, and AI
<p><strong><span>Background</span></strong></p> <p><span>The manual study of root dynamics using images requires huge investments of time and resources and is prone to previously poorly quantified annotator bias. AI image-processing tools have been successful in overcoming limitations of manual annotation in homogeneous soils, but their efficiency and accuracy is yet to be widely tested on less homogenous, non-agricultural soil profiles, e.g., that of forests, from which data on root dynamics are key to understanding the carbon cycle. Here, we quantify variance in root length measured by human annotators with varying experience levels. We evaluate the application of a convolutional neural network (CNN) model, trained on a software accessible to researchers without a machine learning background, on a heterogeneous minirhizotron image dataset taken in a multispecies, mature, deciduous temperate forest.</span></p> <p><strong><span>Results</span></strong></p> <p><span>Less experienced annotators consistently identified more root length than experienced annotators. Root length annotation also varied between experienced annotators. The CNN root length results were neither precise nor accurate, taking ~10% of the time but significantly overestimating root length compared to expert manual annotation (p=0.01). The CNN net root length change results were closer to manual (p=0.08) but there remained substantial variation.</span></p> <p><strong><span>Conclusions</span></strong></p> <p><span>Manual root length annotation is contingent on the individual annotator. The only accessible CNN model cannot yet produce root data of sufficient accuracy and precision for ecological applications when applied to a complex, heterogeneous forest image dataset. A continuing evaluation and development of accessible CNNs for natural ecosystems is required.</span></p>
ISP Multi-expert Electrocardiography Dataset
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Replication Package for: No Free Lunch? Welfare Analysis of Firms Selling Through Expert Intermediaries
<p>Citation: Grennan, M., Myers, K., Swanson, A., & Chatterji, A. "No Free Lunch? Welfare Analysis of Firms Selling Through Expert Intermediaries." <em>Review of Economic Studies (forthcoming).</em></p> <p>Contains all of the code publicly available data used to produce the results of Grennan et al. (<em>forthcoming)</em>. </p>
Interview data for 'Risks in the offshore wind supply chain and tendering process impacts: Insights from industry expert elicitations'
<p>Updated 3 category labels to reduce potential for confusion. (v3)</p> <p>Interview data with restored functionality of some unused data analysis methods. (v2)</p> <p>Original upload. (v1)</p>
SYNDIAG: an expert system for disease syndrome diagnosis of traditional Vietnamese medicine
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Wildwatch Kenya expert verified data
<p class="Body"><span>Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail-cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail-cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy are poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photo was circulated to multiple volunteers.</span></p> <p class="Body"><span>All volunteer classifications were aggregated to a single best answer for each photo using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g. false species or false empty), and reason for the false classification (e.g. misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open-grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail-camera set up protocol with a burst of three consecutive photos, a short field of view, and determine camera sensitivity settings based on </span><i><span>in situ</span></i><span> testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data.</span></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.