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125 results for “Crowdsourcing”
Crowdsourcing Dataset
<p>Crowdsourcing Dataset</p>
Rainfall-Runoff Modeling Using Crowdsourced Water Level Data
<p>Input data, geodata, model outputs, and Python scripts used for running and analyzing the hydrological model <a href="https://philippkraft.github.io/cmf/">CMF </a>(parameterized using <a href="https://github.com/thouska/spotpy">SPOTPY</a>) in the frame of the publication "Rainfall-Runoff Modeling Using Crowdsourced Water Level Data" by Weeser et al. (Water Resource Research).</p> <p>The folder WRR_CrowdMod_2019_08_23.zip contains:</p> <ul> <li>Folder <em>input_data</em>: Input data used for the model</li> <li>Folder <em>script_model</em>: The model including the SPOTPY set-up for calibration and validation <ul> <li>Subfolder <em>parameter_validation</em>: Parameter sets used during validation for all analyzed scenarios</li> <li>Subfolder <em>parameter_fluxes</em>: Parameter sets used for the analysis of the fluxes</li> </ul> </li> <li>Folder <em>calibration</em>: Model output during calibration with all 10<sup>6</sup> runs</li> <li>Folder <em>validation</em>: Model outputs generated during validation <ul> <li>Subfolder <em>accepted_simulation_results</em>: modeled discharge by using all accepted parameter sets for each scenario</li> </ul> </li> <li>Folder <em>Fluxes</em>: The fluxes released by the different model components</li> <li>4 Jupyter notebooks: <ul> <li>1_calibration: Script for analyzing the model output generated during calibration. Generates the parameter sets used for validation</li> <li>2_validation: Analyze the results during validation</li> <li>3_fig5_calibration_validation: Script used to generate figure 5</li> <li>4_fig6_fluxes: Script used to generate figure 6 showing the fluxes within the model during validation</li> </ul> </li> </ul> <p>The folder geodata contains two shapefiles representing the spatial data of the catchment.</p>
A Dataset of Crowdsourced Smarthome Requirements with Creativity Ratings
<p>This dataset contains the data analyzed in the paper titled "Crowdsourcing Requirements: Does Teamwork Enhance Crowd Creativity?"</p> <p>The dataset contains the following csv files:</p> <p>1. presurvey-questions: List of presurvey questions to collect demographics</p> <p>2. disc-questions: List of DISC personality questions to cause a crowd worker’s personality. Each group has a set of 4 statements out of which the worker was expected to select one</p> <p>3. post-survey-questions: List of postsurvey questions</p> <p>4. users: List of crowd workers in the study; values 1 and 2 of the column ‘group_type’ correspond to workers in solo and interacting teams respectively</p> <p>5. presurvey-responses: Workers' responses to the presurvey</p> <p>6. personality_data: Workers’ IPIP (O, C, E, A, N metrics) and DISC (raw and normalized) scores</p> <p>7. post-survey-responses: Workers' responses to the postsurvey</p> <p>8. all_requirements: Requirements in a user story format, elicited by the crowd workers</p> <p>9. creativity-ratings.csv: Authors’ average ratings for each requirement for the metrics ‘detailedness’, ‘novelty’ and ‘usefulness’</p>
EU-Level Crowdsourced Survey Data on Biomass Communities – BECoop project
<p><strong><em>Activity Description:</em></strong></p> <p>In this initiative, an English survey was strategically linked to a crowdsourcing campaign with the goal of garnering 5,000 responses from a diverse range of stakeholders and the general public across the European Union (EU).</p> <p> </p> <p><strong><em>Purpose of the Survey:</em></strong></p> <p>A comprehensive questionnaire was developed with the primary aim of identifying the perceptions, needs, barriers, and misconceptions affecting the market adoption of community bioenergy at the EU level. The survey sought to gain a nuanced understanding of stakeholders' and the general public's perspectives, ultimately contributing to informed decision-making and policy formulation in this vital domain.</p> <p>Leveraging platforms such as Clickworker, this approach facilitated the engagement of an independent global workforce to address defined tasks, offering swift access to data from a broad spectrum of participants encompassing various demographics, backgrounds, and interests.</p> <p>The data collection phase was executed between mid-April and June 2021, spanned across multiple waves. This approach was aimed at meticulously monitoring responses and ensuring the integrity and quality of the collected data.</p> <p> </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement no. 952930.</p>
Dataset to support "Explore the potential of using social media crowdsourcing for earthquake damage assessment"
<p>This dataset is used to support findings in the paper "<strong>Explore the potential of using social media crowdsourcing for earthquake damage assessment.</strong>"</p> <p>In this paper, we investigated six earthquake events to explore the potential of using social media crowdsourcing for earthquake damage assessment. These cases include the 2014 Iquique earthquake, the 2015 Napa earthquake, the 2015 Nepal earthquake, the 2019 Ridgecrest earthquake, the 2021 Fukushima earthquake, and the 2021 Haiti earthquake. For the first three cases, tweet ID information was collected from the publicly available website CrisisNLP (https://crisisnlp.qcri.org/). For the last three cases, we collected “near-real-time” tweets with the key search term “earthquake.” For the 2019 Ridgecrest earthquake, the search date range was from July 4 to 10, 2019. For the 2021 Fukushima earthquake, the search date range was February 13 to 17, 2021. For the 2021 Haiti earthquake, the search date range was August 14 to 19, 2021. It should be noted that restrictions apply to the availability of tweet data due to the policy from <em>Twitter</em> Inc. Tweet data showing user profiles or text content is not allowed to be shared publicly, but tweet IDs are available to download from this dataset. </p> <p>In addition, we attached our processed results to this dataset, including,</p> <ol> <li>Damage estimation. The damage estimation for each event is based on the average estimated level of all damage-related tweets identified by text classification models, as documented in our paper.</li> <li>Temporal dynamics of damage estimation. We conducted the temporal analysis to explore the convergence of damage estimates over time, in which data were first binned based on the number of damage-related tweets in each 2-hour interval.</li> </ol> <p>If you need the training and testing datasets or have any further questions regarding the dataset or paper, please do not hesitate to reach us. </p>
Crowdsourcing A Public Health Campaign
ClinicalTrials.gov study NCT02796963. IPD Sharing: UNDECIDED. Countries: 0. Publications: 5.
Crowdsourcing to Promote HBV and HCV Testing in China
ClinicalTrials.gov study NCT03482388. IPD Sharing: YES. Countries: 1. Publications: 2.
Crowdsourcing to Reduce HIV Stigma Among Adolescents and Young Adults in Kazakhstan
ClinicalTrials.gov study NCT05107401. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Crowdsourcing the identification of organisms: a case-study of iSpot
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Data from social learning development, during COVID-19 how international students use media platforms as crowdsource technology to solve learning difficulties
<p>This study lens cannot detach mobile-learning communication from efficient broadband, during social distance protocol, for continuous learning. The study aims to self-actualized knowledge-sourcing among individuals in an interconnected cluster of multi-community platforms. Crowdsource is characterized by a communication science perspective using mobile-learning in groups and/or clusters. Integrated TPB and Bandura's Social Learning Theory (SLT) induced and investigated 361 respondents, stratified of students, teachers, and researchers during the COVID-19. The IBM Amos v. 25 for the analysis found R² = 0.92 and (65.4, 34.6) for male and female demographic respectively. Results found significant direct and indirect Attitude, Self-learner usefulness, and crowdsource were positive to actual performance. Where broadband moderated on mobile learning behavior run-up significantly. Mobile learning mediation gives in magnificent interactive operation. Generally, crowdsource at the individual level enhanced collaborated problem-solving tasks pro-COVID-19. An Individual's sourcing knowledge is a creative task and timely routine learning in any critical period. Results suggested mobility of learning makes a mountain of molehills in knowledge interactivity among learners in groups. Therefore, encourages the clustering of network learning, easing learning, through effective broadband social intervention phenomena.</p>
Data from: Crowdsourced geometric morphometrics enable rapid large-scale collection and analysis of phenotypic data
1. Advances in genomics and informatics have enabled the production of large phylogenetic trees. However, the ability to collect large phenotypic datasets has not kept pace. 2. Here, we present a method to quickly and accurately gather morphometric data using crowdsourced image-based landmarking. 3. We find that crowdsourced workers perform similarly to experienced morphologists on the same digitization tasks. We also demonstrate the speed and accuracy of our method on seven families of ray-finned fishes (Actinopterygii). 4. Crowdsourcing will enable the collection of morphological data across vast radiations of organisms, and can facilitate richer inference on the macroevolutionary processes that shape phenotypic diversity across the tree of life.
Notre Dame crowdsourced photogrammetry test
Notre Dame crowdsourced photogrammetry test Source: Objaverse 1.0 / Sketchfab
Notre Dame day crowdsourced photogrammetry test
Notre Dame day crowdsourced photogrammetry test Source: Objaverse 1.0 / Sketchfab
Notre Dame crowdsourced photogrammetry
Notre Dame crowdsourced photo/video photogrammetry Source: Objaverse 1.0 / Sketchfab
PHARAOH: A Collaborative Crowdsourcing Platform for PHenotyping And Regional Analysis Of Histology: KIRK Validation Cohort Grades 3 and 4
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PHARAOH: A Collaborative Crowdsourcing Platform for PHenotyping And Regional Analysis Of Histology: KIRK Validation Cohort Grades 1 and 2
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PHARAOH: A Collaborative Crowdsourcing Platform for PHenotyping And Regional Analysis Of Histology: CCRCC Validation Cohort Grades 3 and 4
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PHARAOH: A Collaborative Crowdsourcing Platform for PHenotyping And Regional Analysis Of Histology: CCRCC Validation Cohort Grades 1 and 2
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Figure 7 from: Silvertown J, Harvey M, Greenwood R, Dodd M, Rosewell J, Rebelo T, Ansine J, McConway K (2015) Crowdsourcing the identification of organisms: A case-study of iSpot. ZooKeys 480: 125-146. https://doi.org/10.3897/zookeys.480.8803
Figure 7 - a iSpot participants who made at least one observation, ranked on the horizontal axis by the number of observations each made (shown on the vertical axis). b iSpot participants who made at least one determination ranked on the horizontal axis by the number of identifications each made. n = 201,711 observations on ispotnature.org for both.
Figure 6 from: Silvertown J, Harvey M, Greenwood R, Dodd M, Rosewell J, Rebelo T, Ansine J, McConway K (2015) Crowdsourcing the identification of organisms: A case-study of iSpot. ZooKeys 480: 125-146. https://doi.org/10.3897/zookeys.480.8803
Figure 6 - Schematic showing how the iSpot reputation system works. See the text for further explanation.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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DANDI Archive for NWB datasets
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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.