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IMPROVING THE MECHANISM OF INCREASING THE SYNERGIC EFFICIENCY OF THE USE OF LAND AND WATER RESOURCES IN AGRICULTURE
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ASSESSMENT OF RESOURCE COMPETENCE IN THE DEVELOPMENT OF AGRO-TOURISM ACTIVITIES IN FERGANA PROVINCE
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Supplementary Material to: Hannon, M. C. & Schulze, A: Two-year survey of Alitta succinea (Annelida: Nereididae) in fouling communities with notes on morphology and reproduction. Ocean and Coastal Resources
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Light Power Resource Variability for Energy Harvesting Photovoltaics for Self-Powered IoT
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NATURE AND FEATURES OF INNOVATION POTENTIAL OF AN INDUSTRIAL ENTERPRISE IN CONDITIONS OF LIMITED RESOURCES
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ELECTRONIC RESOURCES AND THEIR ROLE IN ORGANIZING STUDENTS' INDEPENDENT WORK
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Dataset for the publication "The Resource Demand of Multi-Terawatt Scale Perovskite Tandem Photovoltaics"
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AT SCHOOL GEOMETRY PEDAGOGICAL GOALS IN LEARNING FROM ELECTRONIC RESOURCES FOR USE
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EFFECTIVE USE OF ENERGY RESOURCES AND SAVING THEM IS THE GUARANTEE OF ECONOMIC EFFICIENCY OF INDUSTRIAL ENTERPRISES.
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Supplementary material 5 from: Timm L, Schaal J, Sann M (2024) A DNA-barcoding-based approach to quantitatively investigate larval food resources of cavity-nesting wasps from trap nests. Journal of Hymenoptera Research 97: 45-56. https://doi.org/10.3897/jhr.97.117410
Trap nests locations
Supplementary material 4 from: Timm L, Schaal J, Sann M (2024) A DNA-barcoding-based approach to quantitatively investigate larval food resources of cavity-nesting wasps from trap nests. Journal of Hymenoptera Research 97: 45-56. https://doi.org/10.3897/jhr.97.117410
Information on sampling sites
Supplementary material 1 from: Timm L, Schaal J, Sann M (2024) A DNA-barcoding-based approach to quantitatively investigate larval food resources of cavity-nesting wasps from trap nests. Journal of Hymenoptera Research 97: 45-56. https://doi.org/10.3897/jhr.97.117410
PCR Conditions
Supplementary material 2 from: Timm L, Schaal J, Sann M (2024) A DNA-barcoding-based approach to quantitatively investigate larval food resources of cavity-nesting wasps from trap nests. Journal of Hymenoptera Research 97: 45-56. https://doi.org/10.3897/jhr.97.117410
Barcode and nest information
Supplementary material 3 from: Timm L, Schaal J, Sann M (2024) A DNA-barcoding-based approach to quantitatively investigate larval food resources of cavity-nesting wasps from trap nests. Journal of Hymenoptera Research 97: 45-56. https://doi.org/10.3897/jhr.97.117410
Infos on arthopods
Figure 2 from: Timm L, Schaal J, Sann M (2024) A DNA-barcoding-based approach to quantitatively investigate larval food resources of cavity-nesting wasps from trap nests. Journal of Hymenoptera Research 97: 45-56. https://doi.org/10.3897/jhr.97.117410
Figure 2 Tri-trophic interaction networks of the studied vespid and apoid wasp species comprising identified prey species and natural enemies. Interaction networks were conducted for the A spider-hunting apoid wasp T. clavicerumB aphid-hunting apoid wasp species P. corniger, P. gracilis and P. fuscipennis and CLepidoptera-hunting vespid wasp A. nigricornis, cricket-hunting apoid wasps I. mexicana and weevil-hunting vespid wasp M. parvulus. Yellow boxes represent the nest cell and the respective wasp larva, blue boxes the natural enemies and green boxes the prey species and the number of prey individuals per species per nest cell. Boxes with no number represent one individual only. The natural enemy Pronotalia sp. was not counted due to a high and randomely distributed number of individuals in the nest cell (> 40). Connections of nests and prey species are marked with grey bars.
Figure 1 from: Timm L, Schaal J, Sann M (2024) A DNA-barcoding-based approach to quantitatively investigate larval food resources of cavity-nesting wasps from trap nests. Journal of Hymenoptera Research 97: 45-56. https://doi.org/10.3897/jhr.97.117410
Figure 1 Nesting site and sample collection procedure: A example of a trap nest placed in the Botanical Garden of the University of Hohenheim, Stuttgart, Germany B, C nests of Passaloecus gracilis and Isodontia mexicana. One nest comprises several nest cells, which are separated by a given nesting material e.g. silky membran (B) or dry grass fragments (C) E morphotyped aphids F morphotyped spiders.
SE-PQA: a Resource for Personalized Community Question Answering
<p>Personalization in Information Retrieval is a topic studied for a long time. Nevertheless, there is still a lack of high-quality, real-world datasets to conduct large-scale experiments and evaluate models for personalized search. This paper contributes to fill this gap by introducing SE-PQA (StackExchange - Personalized Question Answering), a new resource to design and evaluate personalized models related to the two tasks of community Question Answering (cQA). The contributed dataset includes more than 1 million queries and 2 million answers, annotated with a rich set of features modeling the social interactions among the users of a popular cQA platform. We describe the characteristics of SE-PQA and detail the features associated with both questions and answers. We also provide reproducible baseline methods for the cQA task based on the resource, including deep learning models and personalization approaches. The results of the preliminary experiments conducted show the appropriateness of SE-PQA to train effective cQA models; they also show that personalization improves remarkably the effectiveness of all the methods tested. Furthermore, we show the benefits in terms of robustness and generalization of combining data from multiple communities for personalization purposes.</p> <p> </p> <p>Performance on all communities separately:</p> <table> <tbody><tr> <th>Community</th> <th>Model (BM25 +)</th> <th>P@1</th> <th>NDCG@3</th> <th>NDCG@10</th> <th>R@100</th> <th>MAP@100</th> <th>$\lambda$</th> </tr> </tbody><tbody> <tr> <td>Academia</td> <td>MiniLM</td> <td>0.438</td> <td>0.382</td> <td>0.395</td> <td>0.489</td> <td>0.344</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.453</td> <td>0.392</td> <td>0.403</td> <td>0.489</td> <td>0.352</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Anime</td> <td>MiniLM + TAG</td> <td>0.650</td> <td>0.682</td> <td>0.714</td> <td>0.856</td> <td>0.683</td> <td>(.1,.9,.0)</td> </tr> <tr> <td>Apple</td> <td>MiniLM</td> <td>0.327</td> <td>0.351</td> <td>0.381</td> <td>0.514</td> <td>0.349</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.335</td> <td>0.361</td> <td>0.389</td> <td>0.514</td> <td>0.357</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Bicycles</td> <td>MiniLM</td> <td>0.405</td> <td>0.380</td> <td>0.421</td> <td>0.600</td> <td>0.365</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.436</td> <td>0.405</td> <td>0.441</td> <td>0.600</td> <td>0.386</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Boardgames</td> <td>MiniLM</td> <td>0.681</td> <td>0.694</td> <td>0.728</td> <td>0.866</td> <td>0.692</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.696</td> <td>0.702</td> <td>0.736</td> <td>0.866</td> <td>0.699</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Buddhism</td> <td>MiniLM + TAG</td> <td>0.490</td> <td>0.387</td> <td>0.397</td> <td>0.544</td> <td>0.334</td> <td>(.3,.7,.0)</td> </tr> <tr> <td>Christianity</td> <td>MiniLM</td> <td>0.534</td> <td>0.505</td> <td>0.555</td> <td>0.783</td> <td>0.497</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.549</td> <td>0.521</td> <td>0.564</td> <td>0.783</td> <td>0.507</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Cooking</td> <td>MiniLM</td> <td>0.600</td> <td>0.567</td> <td>0.600</td> <td>0.719</td> <td>0.553</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.619</td> <td>0.583</td> <td>0.614</td> <td>0.719</td> <td>0.568</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>DIY</td> <td>MiniLM</td> <td>0.323</td> <td>0.313</td> <td>0.346</td> <td>0.501</td> <td>0.302</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.335</td> <td>0.324</td> <td>0.356</td> <td>0.501</td> <td>0.312</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Expatriates</td> <td>MiniLM + TAG</td> <td>0.596</td> <td>0.653</td> <td>0.682</td> <td>0.832</td> <td>0.645</td> <td>(.1,.9,.0)</td> </tr> <tr> <td>Fitness</td> <td>MiniLM + TAG</td> <td>0.568</td> <td>0.575</td> <td>0.613</td> <td>0.760</td> <td>0.567</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Freelancing</td> <td>MiniLM + TAG</td> <td>0.513</td> <td>0.472</td> <td>0.506</td> <td>0.654</td> <td>0.457</td> <td>(.1,.9,.0)</td> </tr> <tr> <td>Gaming</td> <td>MiniLM</td> <td>0.510</td> <td>0.534</td> <td>0.562</td> <td>0.686</td> <td>0.532</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.519</td> <td>0.547</td> <td>0.571</td> <td>0.686</td> <td>0.541</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Gardening</td> <td>MiniLM</td> <td>0.344</td> <td>0.362</td> <td>0.396</td> <td>0.520</td> <td>0.359</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.345</td> <td>0.369</td> <td>0.399</td> <td>0.520</td> <td>0.363</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Genealogy</td> <td>MiniLM + TAG</td> <td>0.592</td> <td>0.605</td> <td>0.631</td> <td>0.779</td> <td>0.594</td> <td>(.3,.7,.0)</td> </tr> <tr> <td>Health</td> <td>MiniLM + TAG</td> <td>0.718</td> <td>0.765</td> <td>0.797</td> <td>0.934</td> <td>0.765</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Gaming</td> <td>MiniLM</td> <td>0.510</td> <td>0.534</td> <td>0.562</td> <td>0.686</td> <td>0.532</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.519</td> <td>0.547</td> <td>0.571</td> <td>0.686</td> <td>0.541</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Hermeneutics</td> <td>MiniLM</td> <td>0.589</td> <td>0.538</td> <td>0.593</td> <td>0.828</td> <td>0.526</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.632</td> <td>0.570</td> <td>0.617</td> <td>0.828</td> <td>0.552</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Hinduism</td> <td>MiniLM</td> <td>0.388</td> <td>0.415</td> <td>0.459</td> <td>0.686</td> <td>0.416</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.382</td> <td>0.410</td> <td>0.457</td> <td>0.686</td> <td>0.412</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>History</td> <td>MiniLM + TAG</td> <td>0.740</td> <td>0.735</td> <td>0.764</td> <td>0.862</td> <td>0.730</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Hsm</td> <td>MiniLM + TAG</td> <td>0.666</td> <td>0.707</td> <td>0.737</td> <td>0.870</td> <td>0.690</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Interpersonal</td> <td>MiniLM + TAG</td> <td>0.663</td> <td>0.617</td> <td>0.653</td> <td>0.739</td> <td>0.604</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Islam</td> <td>MiniLM</td> <td>0.382</td> <td>0.412</td> <td>0.453</td> <td>0.642</td> <td>0.410</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.395</td> <td>0.427</td> <td>0.464</td> <td>0.642</td> <td>0.421</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Judaism</td> <td>MiniLM + TAG</td> <td>0.363</td> <td>0.387</td> <td>0.432</td> <td>0.649</td> <td>0.388</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Law</td> <td>MiniLM</td> <td>0.663</td> <td>0.647</td> <td>0.678</td> <td>0.803</td> <td>0.639</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.677</td> <td>0.657</td> <td>0.687</td> <td>0.803</td> <td>0.649</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Lifehacks</td> <td>MiniLM</td> <td>0.714</td> <td>0.601</td> <td>0.617</td> <td>0.703</td> <td>0.553</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.714</td> <td>0.621</td> <td>0.631</td> <td>0.703</td> <td>0.568</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Linguistics</td> <td>MiniLM + TAG</td> <td>0.584</td> <td>0.588</td> <td>0.630</td> <td>0.794</td> <td>0.587</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Literature</td> <td>MiniLM + TAG</td> <td>0.871</td> <td>0.878</td> <td>0.889</td> <td>0.934</td> <td>0.876</td> <td>(.3,.7,.0)</td> </tr> <tr> <td>Martialarts</td> <td>MiniLM</td> <td>0.630</td> <td>0.599</td> <td>0.645</td> <td>0.796</td> <td>0.596</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.640</td> <td>0.628</td> <td>0.660</td> <td>0.796</td> <td>0.612</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Money</td> <td>MiniLM</td> <td>0.545</td> <td>0.535</td> <td>0.563</td> <td>0.706</td> <td>0.515</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.559</td> <td>0.542</td> <td>0.571</td> <td>0.706</td> <td>0.523</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Movies</td> <td>MiniLM</td> <td>0.713</td> <td>0.722</td> <td>0.753</td> <td>0.865</td> <td>0.724</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.728</td> <td>0.735</td> <td>0.762</td> <td>0.865</td> <td>0.735</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Music</td> <td>MiniLM</td> <td>0.508</td> <td>0.447</td> <td>0.476</td> <td>0.602</td> <td>0.418</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.522</td> <td>0.460</td> <td>0.486</td> <td>0.602</td> <td>0.427</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Musicfans</td> <td>MiniLM + TAG</td> <td>0.531</td> <td>0.531</td> <td>0.560</td> <td>0.693</td> <td>0.539</td> <td>(.1,.9,.0)</td> </tr> <tr> <td>Opensource</td> <td>MiniLM</td> <td>0.574</td> <td>0.593</td> <td>0.621</td> <td>0.771</td> <td>0.581</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.577</td> <td>0.598</td> <td>0.622</td> <td>0.771</td> <td>0.581</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Outdoors</td> <td>MiniLM + TAG</td> <td>0.681</td> <td>0.643</td> <td>0.675</td> <td>0.819</td> <td>0.629</td> <td>(.1,.9,.0)</td> </tr> <tr> <td>Parenting</td> <td>MiniLM + TAG</td> <td>0.485</td> <td>0.430</td> <td>0.452</td> <td>0.602</td> <td>0.399</td> <td>(.1,.9,.0)</td> </tr> <tr> <td>Pets</td> <td>MiniLM</td> <td>0.509</td> <td>0.531</td> <td>0.565</td> <td>0.685</td> <td>0.523</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.519</td> <td>0.549</td> <td>0.581</td> <td>0.685</td> <td>0.541</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Philosophy</td> <td>MiniLM + TAG</td> <td>0.568</td> <td>0.514</td> <td>0.546</td> <td>0.707</td> <td>0.491</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Politics</td> <td>MiniLM + TAG</td> <td>0.659</td> <td>0.630</td> <td>0.659</td> <td>0.814</td> <td>0.608</td> <td>(.1,.9,.0)</td> </tr> <tr> <td>Rpg</td> <td>MiniLM</td> <td>0.657</td> <td>0.646</td> <td>0.685</td> <td>0.849</td> <td>0.640</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.677</td> <td>0.660</td> <td>0.695</td> <td>0.849</td> <td>0.651</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Scifi</td> <td>MiniLM</td> <td>0.532</td> <td>0.563</td> <td>0.596</td> <td>0.745</td> <td>0.559</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.549</td> <td>0.574</td> <td>0.606</td> <td>0.745</td> <td>0.569</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Skeptics</td> <td>MiniLM + TAG</td> <td>0.862</td> <td>0.869</td> <td>0.887</td> <td>0.969</td> <td>0.867</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Sound</td> <td>MiniLM</td> <td>0.377</td> <td>0.410</td> <td>0.451</td> <td>0.626</td> <td>0.405</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.380</td> <td>0.423</td> <td>0.454</td> <td>0.626</td> <td>0.413</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Sports</td> <td>MiniLM</td> <td>0.673</td> <td>0.721</td> <td>0.756</td> <td>0.902</td> <td>0.724</td> <td>(.2,.8)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.692</td> <td>0.743</td> <td>0.775</td> <td>0.902</td> <td>0.740</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Sustainability</td> <td>MiniLM</td> <td>0.657</td> <td>0.677</td> <td>0.735</td> <td>0.895</td> <td>0.675</td> <td>(.1,.9)</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>694</td> <td>0.716</td> <td>0.763</td> <td>0.895</td> <td>0.706</td> <td>(.1,.8,.1)</td> </tr> <tr> <td>Travel</td> <td>MiniLM + TAG</td> <td>0.546</td> <td>0.547</td> <td>0.576</td> <td>0.700</td> <td>0.530</td> <td>(.1,.9,.0)</td> </tr> <tr> <td>Vegetarianism</td> <td>MiniLM + TAG</td> <td>0.623</td> <td>0.626</td> <td>0.678</td> <td>0.869</td> <td>0.641</td> <td>(.3,.7,.0)</td> </tr> <tr> <td>Woodworking</td> <td>MiniLM + TAG</td> <td>0.656</td> <td>0.654</td> <td>0.692</td> <td>0.847</td> <td>0.645</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Workplace</td> <td>MiniLM + TAG</td> <td>0.574</td> <td>0.444</td> <td>0.429</td> <td>0.495</td> <td>0.359</td> <td>(.3,.7,.0)</td> </tr> <tr> <td>Writers</td> <td>MiniLM + TAG</td> <td>0.561</td> <td>0.490</td> <td>0.516</td> <td>0.644</td> <td>0.466</td> <td>(.2,.8,.0)</td> </tr> <tr> <td>Average</td> <td>MiniLM</td> <td>0.519</td> <td>0.506</td> <td>0.536</td> <td>0.677</td> <td>0.492</td> <td>-</td> </tr> <tr> <td> </td> <td>MiniLM + TAG</td> <td>0.530</td> <td>0.515</td> <td>0.544</td> <td>0.677</td> <td>0.500</td> <td>-</td> </tr> </tbody> </table>
Open Access Resources
<p>Data analysis from from DOAJ, DOAB, OpenDOAR, re3Data, CWTS Leiden Ranking (2023)</p>
Experimental data and software for: Defaults: a double-edged sword in governing common resources
<p>Experimental data and software for the paper: <strong>Defaults: a double-edged sword in governing common resources</strong></p> <p>The experiment consisted in three treatments of the Common Pool Resource Dilemma, where three default interventions were applied: pro-social, self-serving and no default. Plus, the participants had to complete an SVO task and a Risk assessment task.</p> <h4>Description of the data and file structure</h4> <p>In the file called <code>all_participants.csv</code> is the full dataset of all participants that took part of the experiment. This includes participants who will end up excluded and dropouts.</p> <p>The experimental data files come in two formats: wide and long. The wide version, called <code>data_wide_format.csv</code> contains one row per participant and a column for all the fields, including rounds from 1 to 10 of the CPR task. Also, this file includes all demographic information of the participants, times and payments. The ID shown is generated internally and has no relationship with the participants' Prolific ID.</p> <p>The long version, called <code>data_long_format.csv</code>, contains 10 rows per participant, and columns for the extraction and other variables necessary for analysis. This version contains the necessary data to reproduce all the figures and statistics detailed in the main manuscript.</p> <p>In both of the previous files, the participants taken into account were the ones who completed the whole experiment. Those who did not complete the comprehension test, dropped out or did not sign the Informed Consent Form were excluded from the experimental data used. More details in the Methods below.</p> <p>In the file <code>default_opinions.csv</code>, we manually classified the responses by participants to whether they were influenced by the default presented.</p> <p>The file "<code>Instructions of the experiment.pdf</code>" contains the instructions of the experiment as shown to participants, also screenshots of the platform.</p>
Supplementary Material Accompanying the Study "First Steps Towards the Integration of Resources on Historical Glossing Traditions in the History of Chinese: A Collection of Standardized Fǎnqiè Spellings from the Guǎngyùn"
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