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4 results for “wisdom of crowd”
Lexical Relations from the Wisdom of the Crowd 1.0
<p>A set of 300 most frequent nouns has been extracted from the Russian National Corpus. Then, each method or resource, including RuThes, produced at most five hypernyms, if possible. In case it is not possible, missing answers treated as empty results. This resulted in 9 322 unique non-empty subsumption pairs that have been passed for crowdsourcing annotation on the Yandex.Toloka microtask platform. Each pair has been annotated by seven different annotators whose mother tongue is Russian and the age is at least 20 by February 1, 2017.</p> <p>The layout of the human intelligence task (HIT) design assumes the direct answer to a simple question: does the given pair of words represent a meaningful <em>is-a</em> relation? Since the crowd workers are not expert lexicographers and this question might be difficult for them, it has been rephrased as “Is it correct that a <em>kitten</em> is a kind of <em>mammal</em>?” (in Russian).</p> <p>The answers have been aggregated using the Yandex.Toloka proprietary answer aggregation mechanism. As the result, 3 940 out of 9 322 pairs have been annotated as positive while the rest 5 382 have been annotated as negative.</p> <p>Interestingly, the workers were more confident in negative answers rather than in the positive ones. These negative answers are extremely useful for both training and testing different relation extraction methods. To the best of our knowledge, this is the first dataset of this kind made for the Russian language using microtask-based crowdsourcing.</p>
Lexical Relations from the Wisdom of the Crowd 1.1
<p>A set of 300 most frequent nouns has been extracted from the Russian National Corpus. Then, each method or resource, including RuThes and RuWordNet, produced at most five hypernyms, if possible. In case it is not possible, missing answers treated as empty results. This resulted in 10,600 unique non-empty subsumption pairs that have been passed for crowdsourcing annotation on the Yandex.Toloka microtask platform. Each pair has been annotated by seven different annotators whose mother tongue is Russian and the age is at least 20 by February 1, 2017.</p> <p>The layout of the human intelligence task (HIT) design assumes the direct answer to a simple question: does the given pair of words represent a meaningful <em>is-a</em> relation? Since the crowd workers are not expert lexicographers and this question might be difficult for them, it has been rephrased as “Is it correct that a <em>kitten</em> is a kind of <em>mammal</em>?” (in Russian).</p> <p>The answers have been aggregated using the Yandex.Toloka proprietary answer aggregation mechanism. As the result, 4,576 out of 10,600 pairs have been annotated as positive while the rest 6,024 have been annotated as negative.</p> <p>Interestingly, the workers were more confident in negative answers rather than in the positive ones. These negative answers are extremely useful for both training and testing different relation extraction methods. To the best of our knowledge, this is the first dataset of this kind made for the Russian language using microtask-based crowdsourcing.</p>
Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials - Data and results
<p>This repository contains the input data and the generated results utilized as part of the research article titled <em>"Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials"</em> by Saarimäki & Fratello et al.</p> <h1>Data</h1> <p>This dataset includes anonymized responses of a panel of experts to a questionnaire focused on nanomaterials safety.</p> <h2>Additional Data Sources</h2> <p>This entry further includes additional data from the following sources:</p> <ul> <li><strong>Saarimäki et al. (2021)</strong>: Preprocessed data and primary physicochemical characteristics are available on <a href="https://doi.org/10.5281/zenodo.6425445">Zenodo</a>.</li> <li><strong>Gallud et al. (2020)</strong>: Data is available from the NCBI Gene Expression Omnibus (GEO) under accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE148705">GSE148705</a>.</li> <li><strong>del Giudice et al. (2023)</strong>: The advanced descriptors are accessible through the associated <a href="https://doi.org/10.5281/zenodo.7674574">Zenodo repository</a>.</li> <li><strong>Labouta et al. (2019)</strong>: The harmonized dataset of ENMs cell viability assays is available as <a href="https://pubs.acs.org/doi/suppl/10.1021/acsnano.8b07562/suppl_file/nn8b07562_si_001.xlsx">supporting information of the reasearch article</a>.</li> </ul> <h1>Outputs</h1> <ul> <li><strong>Statistical modeling:</strong> The inferred parameters of the statistical model developed to analyze the expert responses and identify the consensus among the experts.</li> <li><strong>Machine Learning classifiers:</strong> The performances of several machine learning classifiers trained on the consensus responses to learn models that can predict safety concerns of new nanomaterials based on transcriptomics and physicochemical descriptors data.</li> <li><strong>Feature Importance:</strong> The relevant features extracted from the models analyzed to understand which aspects of the molecular responses to ENMs and which physicochemical properties are most important in driving the predictions.</li> </ul> <h1>Contents</h1> <p><code>data/Combined_cleaned_responses_anon.xlsx</code>: Contains the anonymized responses from the expert panel.</p> <p><code>data/enms_grouping.txt</code>: Contains a categorization of the ENMs based on their core material.</p> <p><code>data/expert_bibliographies_anon.pickle</code>: Contains the anonymized bibliography of the experts to assess the multidisciplinarity of the panel assembled.</p> <p><code>data/gex.csc.gz</code>: Contains the gene expression after exposure to the ENMs.</p> <p><code>data/phenodata.txt</code>: Contains the meta-data of the experiments</p> <p><code>data/physicochemical_descriptors.txt</code>: Contains the physicochemical descriptors of the ENMs.</p> <p><code>data/external/nn8b07562_si_001.xlsx</code>: Contains a panel of harmonized ENMs cytotoxicity assays.</p> <p><code>outputs/concern_scores.csv</code>: Contains the consensus scores inferred by the statistical model.</p> <p><code>outputs/cross_validation/</code>: Contains the logs and performances of all the machine learning classification runs .</p> <p><code>outputs/important_{genes,physchem}_weighted.xlsx</code>: Feature relevance scores of both views.</p> <p><code>outputs/model_inference_anon.nc</code>: Inferred parameters of the statistical model.</p>
Replication package for "Finding the Wise and the Wisdom in a Crowd: Estimating Underlying Qualities of Reviewers and Items"
<p>Replication Package for the article "Finding the Wise and the Wisdom in a Crowd: Estimating Underlying Qualities of Reviewers and Items" by Nicolas Carayol and Matthew O. Jackson, to be published in the Economic Journal</p>
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