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1,604 results for “acceptance”
Database of local seismicity registered on ocean bottom seismometers (OBS). Database related to Bornstein et al. (accepted in Earth and Space Science), PICKBLUE
<p>We assembled a database of Ocean Bottom Seismometer (OBS) waveforms and manual P and S picks from local seismicity, on which we trained PickBlue, a deep-learning picker, using the seismometer data and the hydrophone channel. The dataset belongs to Bornstein et al. (accepted 2023 in Earth and Space Science). The picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records.</p><p>The complete database is also accessible with SEISBENCH: <br><a href="https://seisbench.readthedocs.io">https://seisbench.readthedocs.io</a><br>SEISBENCH on github:<br><a href="https://github.com/seisbench">https://github.com/seisbench</a></p><p>Related paper:</p><p>Bornstein, T., Lange, D., Münchmeyer, J., Woollam, J., Rietbrock., A., Barcheck, G., Grevemeyer, I., Tilmann, F. (accepted 2023 in Earth and Space Science). PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning, Earth and Space Science. </p>
FULFILL dataset - diet policy acceptability - efficacy and acceptability framing Denmark
<p>This dataset represents survey data on sufficiency-oriented policy acceptability in regard to dietary consumption. The study was part of the second round surveys in Denmark in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from two countries: Denmark and Germany, with representative sampling (age, income, gender, current region). In this survey on the acceptability of sufficiency-oriented diet policies we recruited a representative sample with approximately 800 participants from Denmark and Germany, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The central part of the survey includes a framing experiment including three groups with participants being randomly assigned to. We were interested in peoples' acceptability on three majorly discussed and sufficiency-relevant policies, i.e. meat tax, carbon label or meat-free day at public canteens. We investigated if an information on either the efficacy of the measures or a combination of information with acceptance information or none of these information could influence people's acceptability (overall, self vs. others perspective). We measured several control variables (socio-economics such as age, gender, income, education, household size, life stage, ideological measures such as political orientation or attitudinal measures such as sufficiency orientation and climate change denial). A quantitative assessment of the carbon footprint in the food consumption domain was also included.</p>
FULFILL dataset - diet policy acceptability - health information provision France
<p>This dataset represents survey data on sufficiency-oriented policy acceptability in regard to dietary consumption. The study was part of the second round surveys in France in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from three countries: France, Italy, and Latvia, with representative sampling (age, income, gender, current region). In this survey on the acceptability of sufficiency-oriented diet policies we recruited a representative sample with approximately 800 participants from France, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The central part of the survey includes the randomised provision of information on the health-risks associated with meat consumption. We were interested in peoples' acceptability on three majorly discussed and sufficiency-relevant policies, i.e. meat tax, carbon label or meat-free day at public canteens. We investigated if the information provision impacted people's acceptability (overall, self vs. others perspective). We measured several control variables (socio-economics such as age, gender, income, education, household size, life stage, ideological measures such as political orientation or attitudinal measures such as sufficiency orientation and climate change denial). A quantitative assessment of the carbon footprint in the food consumption domain was also included.</p>
FULFILL dataset - diet policy acceptability - health information provision Latvia
<p>This dataset represents survey data on sufficiency-oriented policy acceptability in regard to dietary consumption. The study was part of the second round surveys in Latvia in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from two countries: France, Italy, and Latvia, with representative sampling (age, income, gender, current region). In this survey on the acceptability of sufficiency-oriented diet policies we recruited a representative sample with approximately 500 participants from Latvia, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The central part of the survey includes the randomised provision of information on the health-risks associated with meat consumption. We were interested in peoples' acceptability on three majorly discussed and sufficiency-relevant policies, i.e. meat tax, carbon label or meat-free day at public canteens. We investigated if the information provision impacted people's acceptability (overall, self vs. others perspective). We measured several control variables (socio-economics such as age, gender, income, education, household size, life stage, ideological measures such as political orientation or attitudinal measures such as sufficiency orientation and climate change denial). A quantitative assessment of the carbon footprint in the food consumption domain was also included.</p>
FULFILL dataset - diet policy acceptability - health information provision Italy
<p>This dataset represents survey data on sufficiency-oriented policy acceptability in regard to dietary consumption. The study was part of the second round surveys in Italy in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from three countries: France, Italy, and Latvia, with representative sampling (age, income, gender, current region). In this survey on the acceptability of sufficiency-oriented diet policies we recruited a representative sample with approximately 800 participants from each country, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The central part of the survey includes the randomised provision of information on the health-risks associated with meat consumption. We were interested in peoples' acceptability on three majorly discussed and sufficiency-relevant policies, i.e. meat tax, carbon label or meat-free day at public canteens. We investigated if the information provision impacted people's acceptability (overall, self vs. others perspective). We measured several control variables (socio-economics such as age, gender, income, education, household size, life stage, ideological measures such as political orientation or attitudinal measures such as sufficiency orientation and climate change denial). A quantitative assessment of the carbon footprint in the food consumption domain was also included.</p>
FULFILL dataset - diet policy acceptability - efficacy and acceptability framing Germany
<div> <p>This dataset represents survey data on sufficiency-oriented policy acceptability in regard to dietary consumption. The study was part of the second round surveys in Germany in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from two countries: Denmark and Germany, with representative sampling (age, income, gender, current region). In this survey on the acceptability of sufficiency-oriented diet policies we recruited a representative sample with approximately 800 participants from Denmark and Germany, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The central part of the survey includes a framing experiment including three groups with participants being randomly assigned to. We were interested in peoples' acceptability on three majorly discussed and sufficiency-relevant policies, i.e. meat tax, carbon label or meat-free day at public canteens. We investigated if an information on either the efficacy of the measures or a combination of information with acceptance information or none of these information could influence people's acceptability (overall, self vs. others perspective). We measured several control variables (socio-economics such as age, gender, income, education, household size, life stage, ideological measures such as political orientation or attitudinal measures such as sufficiency orientation and climate change denial). A quantitative assessment of the carbon footprint in the food consumption domain was also included.</p> </div>
FULFILL dataset - housing policy acceptability - framing experiment Latvia
<p>This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in Latvia in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.</p>
FULFILL dataset - housing policy acceptability - framing experiment Italy
<p>This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in Italy in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.</p>
FULFILL dataset - housing policy acceptability - framing experiment France
<p>This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in France in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.</p>
FULFILL dataset - housing policy acceptability - framing experiment Germany
<p>This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in Germany in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.</p>
FULFILL dataset - housing policy acceptability - framing experiment Denmark
<p>This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in Denmark in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.</p>
Swiss public's acceptance and sustainability perceptions of food produced with chemical, digital and mechanical weed control measures and the influence of information source on technology perception in agriculture
<p><span>This data was obtained from an online survey conducted with the Swiss public from the two biggest language regions (German and French) in Switzerland. The survey was conducted in February 2023. Participants were recruited through a professional panel provider and quotas were used for age, gender and language region. The final sample contained </span><span>542 respondents. </span><span>In the first part of the survey, respondents provided basic sociodemographic information. In the second part, their sustainability perceptions regarding four different weed management practices (full-surface spraying, hoeing machine, spot spraying and precise spraying) were investigated. Respondents were then assigned to one of five information source groups, in which information on a hoeing and a milking robot was presented, using 5 different information sources (male/female farmer, male/female scientist, no source). Technology perception was assessed using several questions and aspects. Finally, respondents answered several questions assessing their attitudes towards the perception of farmers, food technology neophobia, chemophobia and the importance of naturalness. The survey can be used and adapted to different contents, aiming to investigate public perception of smart farming technologies and the influence of information sources on technology perception. </span></p>
Anthropomorphic Mechanisms for User Acceptance in Human-Robot Interaction - PRISMA pass data
<p>This is the data produced in the course of selecting relevant literature for the <em>"User Acceptance in Human-Robot Interaction"</em> literature review article.</p> <p><strong>Contents:</strong></p> <ul> <li>Initial pass records: <em>prisma0_wos.xlsx + prisma0_scopus.xlsx</em></li> <li>Initial pass eligibility assessment:<em><strong> </strong>prisma0_eval.xlsx</em></li> <li>Second pass records, filtering and coarse assessment:<em><strong> </strong>prisma1.xlsx</em></li> <li>Third pass records, filtering and coarse assessment:<em><strong> </strong>prisma2.xlsx</em></li> <li>Fine eligibility assessment of 2nd and 3rd pass: <em>prisma_avalanche_1_and_2_report_update_04_26.pdf</em></li> </ul> <p> </p>
Accepted Artifact for Profiling and Optimizing Java Streams
<p>The accepted artifact for the journal article "Profiling and Optimizing Java Streams" published in Volume 7, Issue 3 of <a href="https://programming-journal.org/"><em>The Art, Science, and Engineering of Programming</em></a>.</p> <p>Please refer to the README.md to work with the artifact and reproduce results from the article.</p>
ProjectÔ - Social Acceptance Studies - Dataset
<p>This dataset has been collected as part of the EU-funded ProjectÔ within WP6 and has been analysed in <a href="https://doi.org/10.5281/zenodo.7577225">D6.13 Social Acceptance Studies</a>.</p> <p>The social acceptance research was designed to inform the implementation of the Project Ô water treatment innovations in four countries: Spain, Croatia, Italy, and Israel. Indeed, the data collection undertaken in each of these locations primarily focussed on attitudes toward the technology-based innovations for water treatment being implemented in respondents’ local Project Ô demosite. Further background information was collected through these studies to identify factors affecting social acceptance in each demonstration site’s region. The social acceptance research for the project was designed and implemented by the Institute for Methods Innovation, one of the Project Ô partners, and the Work Package 6 lead.</p> <p>To measure these attitudes, an explainer video of Project Ô’s implementation of innovative water treatment technologies focusing on the project’s innovation work in the respondents’ country was produced. The survey design used in these studies measured key aspects of local views about local Project Ô innovation plans for water reuse, circular water economy, and related issues. To understand their background views relevant to water reuse, respondents were asked about their local water consumption patterns, their perceptions of the local water supply, and their familiarity with water treatment technology. To understand differences in perceptions about environmental factors, respondents were asked to indicate their views related to different aspects of environmental attitudes relevant to water treatment innovations. This is important for clarifying drivers of social acceptance of new water treatment solutions. Finally, to understand the role of trust towards science and technology, scientists and engineers, and the European Commission in social acceptance of Project Ô’s innovation work.</p>
L3Pilot Global User Acceptance Survey, Second Phase Data
<p>The research leading to these results received funding from the European Commission Horizon 2020 programme under the project L3Pilot (L3Pilot.eu), grant agreement number 723051. The L3Pilot Global User Acceptance Survey investigated the acceptance of SAE Level 3 (L3) conditionally automated cars. Survey data was collected in two phases. This dataset contains the data from the second phase of the survey with responses collected from 9 countries on five continents. This document contains information about the survey methodology and coding of the variables. For a detailed description of the first and second phase survey methodology, please consult L3Pilot deliverable D7.1 ‘Annual quantitative survey about user acceptance towards ADAS and vehicle automation’ by Nordhoff et al. (2021).</p> <p>If you use the dataset, please cite it as: L3Pilot (2023). L3Pilot Global User Acceptance Survey, Second Phase Data. <a href="https://doi.org/10.5281/zenodo.8389718">https://doi.org/10.5281/zenodo.8389718</a></p> <p>For further information, please contact: <a href="mailto:user-survey@eict.de">user-survey@eict.de</a></p>
General CCU (Product) Acceptance & Trade-Off Decisions
<p>The dataset obtained for WP6 of the CO2SMOS project contains anonymized data including demographic and attitudinal information, perceptions of benefits and barriers regarding CCU adoption and acceptance data obtained from a choice-based conjoint experiment on trade-off decisions in CCU product purchase situations, obtained through an online survey conducted with participants from Germany, Norway, Poland, and Spain.</p>
Accepted Artifact for Privacy-Respecting Type Error Telemetry at Scale
<p>This artifact packages the data for the paper: <em>Privacy-Respecting Type Error Telemetry at Scale</em></p> <p>There are two files on Zenodo:</p> <ul> <li>data.tar.gz has the original Luau telemetry data</li> <li>artifact.tar.gz has a result PDF, intermediate data, and scripts for processing the data</li> </ul> <p>The artifact code and the source for the paper are also on GitHub:</p> <ul> <li><a href="https://github.com/bennn/luau-telemetry">https://github.com/bennn/luau-telemetry</a></li> </ul> <p>This artifact is primarily a **dataset**. It shows how we reached the conclusions in the paper.</p> <p>The scripts in this artifact are provided as-is for completeness. They may have bugs. They may not work as advertised.</p>
Weekly county-level pollution data for China from Zhang, Carleton, Lin, and Zhou (accepted, Nature Sustainability), "Estimating the role of air quality improvements in the decline of suicide rates in China"
<p>This dataset contains weekly, county-level air pollution data for 2,839 counties from 2013 to early 2018. These data are used and described in Zhang, Carleton, Lin, and Zhou (accepted, <em>Nature Sustainability</em>), "Estimating the role of air quality improvements in the decline of suicide rates in China". When the paper is published a link to the manuscript will be added here. </p> <p>The manuscript Methods section details data construction. In summary, these county-level observations are obtained from monitoring stations maintained by the China National Environmental Monitoring Center (CNEMC), which is affiliated with the Ministry of Ecology and Environment of China. CNEMC began publishing hourly air pollution data in 2013, including the Air Quality Index, PM2.5, PM10, ozone, sulfur dioxide, nitrogen dioxide, and carbon monoxide. We average hourly data to the station-day level and use inverse-distance weighting with a radius of 200km to convert data from station to the county level. We average across days to generate county-level weekly values. Any missing station-hour observations in the raw data are omitted in this spatial and temporal aggregation. Our main analysis relies on PM2.5, but all pollutants are released here.</p>
AI-TAM: a model to investigate user acceptance and collaborative intention in human-in-the-loop AI applications
<p>More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities<br> to provide advanced features; on the other hand, human-in-the-loop approaches are on the<br> rise to involve people in AI-powered pipelines for data collection, results validation and decision making.<br> Does the introduction of AI features affect user acceptance? Does the AI result quality<br> affect people’s willingness to use such applications? Does the additional user effort required in<br> human-in-the-loop mechanisms change the application adoption and use?<br> This study aims to provide a reference approach to answer those questions. We propose a model<br> that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to<br> AI – user trust in AI and perceived quality of AI output, from explainable AI (XAI) literature – and<br> collaborative intention – willingness to contribute to AI pipelines.<br> We tested the proposed model with an application for car damage claim reporting with AI-powered<br> damage estimation for insurance customers. The results showed that the XAI related factors have<br> a strong and positive effect on behavioral intention, perceived usefulness, and ease of use of the<br> application. Moreover, there is a strong link between behavioral intention and collaborative intention,<br> indicating that indeed human-in-the-loop approaches can be successfully adopted in final user<br> applications.</p> <p>Users were invited to test the interactive prototype of the BumpOut application and to report the given car accident from start to finish. These are the two interactive prototypes experienced by users:</p> <ul> <li> <p><a href="https://bit.ly/bo-prototype-flawlessAI">FlawlessAI-Group prototype</a></p> </li> <li> <p><a href="https://bit.ly/bo-prototype-failingAI">FailingAI-Group prototype</a></p> </li> </ul> <p> </p> <p>This study is shared as a research object adopting the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification.</p>
ScienceDex guides
Understand access before you commit
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.