In a survey conducted amongst executives and corporate directors of organizations in Australia in 2021, just over one third of the organizations had fully implemented formal processes regarding data inventory knowledge, including where data comes from, how it moves through the business, and how it is transformed. Less than one third of organizations had fully implemented practices regarding personally identifiable information, sensitive data, intellectual property and other high value data and where it resides throughout the enterprise.

Dataset Card for Trust framework

  Description

Repository: https://github.com/declare-lab/trust-align Paper: https://arxiv.org/abs/2409.11242

  Data Summary

The Trust-score evaluation dataset includes the top 100 GTR-retrieved results for ASQA, QAMPARI, and ExpertQA, along with the top 100 BM25-retrieved results for ELI5. The answerability of each question is assessed based on its accompanying documents. The Trust-align training dataset comprises 19K high-quality… See the full description on the dataset page: https://huggingface.co/datasets/declare-lab/Trust-Data.

This bar chart displays the share of French people who trust enough various type of actors or institutions to entrust them their health data in a survey from 2019. It appears that health professionals as doctors, hospitals or pharmacists were the most trusted by the French when it comes to confidentiality and security of their health data.

The main aim of the iCAREdata-project (Improving Care And Research Electronic Data Trust Antwerp) is to develop a central, clinical research database in out-of-hours (OOH) care in Belgium. With this project, the research team of CHA-ELIZA is developing a state-of-the-art database, in sync with the most recent legal, ethical and privacy aspects present in Belgium and Europe. One crucial aspect of the project is the unique way it links data between different health care services. Subsequently, we are able to study the chain of care that patients follow in OOH care. This gives a broader view on what is exactly happening with patients suffering an unplanned medical problem.

An overview of weekly results is shown on http://icare.uantwerpen.be

The City of Austin as approved by Council resolution agreed to serve as the endorsing municipality.

The data set contains 9 MATLAB code files.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

According to a survey conducted at the EmTech Digital conference in March 2019, U.S. business leaders shared their opinions on trust issues with regard to AI data quality and privacy. Nearly half of respondents reported a lack of trust in the quality of AI data in their companies, showing that there is still a long way to go to get quality AI data.

The Global Trust dataset measures how much trust people around the world have in major institutions and social networks. It contains two data files, one with the raw survey data and one putting the raw data into percentages of trust in certain institutions. These can be analyzed in different ways. The data comes from surveys of over 119,088 people from 113 countries. Survey respondents were asked such things as “How much do you trust each of the following: other people in your neighborhood; your national government; scientists; journalists; doctors and nurses; people who work at non-governmental or non-profit organizations; healers? Do you trust them a lot, some, not much, or not at all?" The National Trust Codebook contains both the survey and the national rate codebook files, titled “Survey” and “Rate” respectively. Both files contain the same variables such as neighbors, government, and journalists, with the only difference being that “Survey” has id as a variable to account for the 119,088 unique responses. The Survey file has the raw data of all the 119,088 unique responses and both categorical and ordinal variables. It can be used to analyze how different countries feel about trust in different people or institutions as well as how those variables can relate to each other. The Rate file creates a percentage of how much people from each country trust certain communities or institutions and this can be used to analyze how different countries feel about certain things, this allows room to analyze each country with each other in a more clear way than the raw data. Both files are unique in the sense of the data being worldwide, it is a unique trait to be able to compare from different countries survey respondents that were asked the same questions with the same methodology, making comparison all the more easy. Another interesting element of this survey data is the number of responses per nation. There were, at minimum, 1000 responses gathered from each nation featured in the survey. The sample size allows for better than typical representation for each country.

Using a large-scale hybrid laboratory and online trust experiment with and without pre-play communication, we investigate how the passage of time affects trust, trustworthiness, and cooperation. Communication (predominantly through promises) raises cooperation, trust, and trustworthiness by about 50 percent. This result holds even when three weeks pass between the time of the trustee’s message/the trustor’s decision to trust and the time of the trustee’s contribution choice and even when this contribution choice is made outside of the lab. Delay between the beginning of the interaction and the time to reciprocate neither substantially alters trust or trustworthiness nor affects how subjects communicate.

The Trust in Digital Health project was conducted by the Centre for Social Research in Health, UNSW Sydney in collaboration with community organisations to assess views of digital health systems in Australia, particularly among communities affected by bloodborne viruses and sexually transmissible infections. We conducted a national, online survey of Australians’ attitudes to digital health in April–June 2020. The sample (N=2,240) was recruited from the general population and four priority populations affected by HIV and other sexually transmissible infections: gay and bisexual men, people living with HIV, sex workers, and trans and gender-diverse people. The deidentified dataset and syntax provided here were used for an analysis of factors associated with greater knowledge of My Health Record and the likelihood of opting out of the system. My Health Record is Australia’s national, digital, personal health record system. Methods The data were collected from a national, online, cross-sectional survey conducted in Australia in April–June 2020. The dataset has been deidentified and cleaned using Stata version 16.1 (College Station, TX).

This table provides information on how many inhabitants of various European countries aged 15 years or older trust other people, the legal system and politics. Figures are from 2002 onwards. The question concerning trust in other people is: Overall, do you think most people can be trusted, or that you can’t be too careful? Trust in the legal system and politics is determined by asking people how much they trust a number of political and organisational institutions, viz. national parliament, the legal system, the police, politicians, political parties, the European Parliament and the United Nations. The figures in this table are based on the European Social Survey (ESS). The ESS is conducted every two years commissioned by the European Committee, the European Science Foundation and various national organisations for scientific research.

Data available from: 2002

Status of the figures: Figures of 2020 are preliminary. Figures of 2002 until 2018 are definite.

Changes as of April 5, 2024. The preliminary figures of 2018 are corrected and made definite. Figures of 2020 are new.

When will new figures be published? New figures will be published in 2025.

Data summary = this is the raw data in the study: Marmolejo-Ramos, F, Marrone, R., Korolkiewicz, M., Gabriel, F., Siemens, G., Joksimovic, S., Yamada, Y., Mori, M., Rahwan T., Sahakyan, M., Sonna, B., Samekin, A., Som, B., Ndukaihe, I., Arinze, N. C., Kundrat, J., Ngo, G., Nguyen, G., Lacia, M., Kung, C., Irmayanti, M., Muktadir, A., Timoria Samosir. F., Liuzza, M, Omid, Hassan, Ozdogru, A., Ariyabuddhiphongs, K., Rakchai, W., Trujillo, N., Maris Valencia S., Janyan, A., Kostov, K., Montoro, P., Hinojosa, J., Medeiros, K., Hunt, T., Freitag, R., Posada, J., Tejada, J. Trust in algorithms. An experimental approach

"ID": D for each participant "Country" "e" : factor variable identifying trials with or without explainability "S" : Factor variable identifying conditions of low and high stake "Item": Factor variable identifying each of the six scenarios. "Probability": Mean probability answered for the question 1 and 2 on the condition of stake described in the column S and e "Age" "Gender" "ADA": Numeric variable whihc represents the participant's level of familiarity with algorithms "BLISS": avearega number of correct answers of the fourteen items selected from Literacy In Statistics (BLIS)

China Trust: Trust Asset data was reported at 27,003,244.000 RMB mn in Jun 2024. This records an increase from the previous number of 23,923,796.000 RMB mn for Dec 2023. China Trust: Trust Asset data is updated quarterly, averaging 20,218,607.000 RMB mn from Mar 2010 (Median) to Jun 2024, with 57 observations. The data reached an all-time high of 27,003,244.000 RMB mn in Jun 2024 and a record low of 2,374,540.000 RMB mn in Mar 2010. China Trust: Trust Asset data remains active status in CEIC and is reported by China Trustee Association. The data is categorized under China Premium Database’s Financial Market – Table CN.ZT: Trust Industry: Trust Asset.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This dataset is a supplementary document to the article entitled "It is not (only) about privacy: How multi-party computation redefines control, trust, and risk in data sharing." It is also a supplementary document for Chapter 4 of the dissertation entitled "The impact of Multi-Party Computation on data sharing decisions in data marketplaces: insights from businesses and consumers". The data was collected through semi-structured interviews conducted in June-October 2020. Further details are provided in the article.

There is a duality of trust in participatory science (citizen science) projects in which the data produced by volunteers must be trusted by the scientific community and participants must trust the scientists who lead projects. Facilitator organizations can diversify recruitment and broaden learning outcomes. We investigated the degree to which they can broker trust in participatory science projects. In Crowd the Tap, we recruited participants through partnerships with facilitators, including high schools, faith communities, universities, and a corporate volunteer program. We compared data quality (a proxy for scientists’ trust in the project) and participant privacy preferences (a proxy for participants’ trust in the project leaders) across the various facilitators as well as to those who came to the project independently (unfacilitated). In general, we found that data quality differed based on the project’s level of investment in the facilitation partner in terms of both time and money..., The data was collected through an IRB approved survey in which Crowd the Tap participants submitted data on the types of pipes they had, the age of their home, water aesthetics, and demographic information. As part of this process, participants also indicated if they came to the project through a partner organization (what we call facilitator organizations). Using the data available to us, we determined how completely, accurately, and informatively (understandability) they participated in the project to assess data quality. We also asked if they had interest in being publically associated with the project or if they referred to remain private. We used this and the number of times they selected "Prefer not to say" as indicators of privacy. We compared data quality and privacy preferences to the facilitator organization through which they came to the project. , , # Data from: The dual nature of trust in participatory science: An investigation into data quality and household privacy preferences

The dataset contains data on participation in Crowd the Tap, a large-scale participatory science (citizen science) project focused on identifying and addressing lead contamination in household drinking water. The project crowdsources information on plumbing materials, age of home, water aesthetics, and demographic data to learn more about the geographic spread of lead plumbing and social and environmental correlates to lead plumbing. We investigated how data quality (completeness, accuracy, and understandability) and participant privacy (whether or not they select to be public or private, the number times they select “prefer not to say†) preferences differed by facilitators. Data quality relates to scientists’ trust in the project, and privacy relates to the trust that participants have in the project leadership team. As participatory science projects inc...

As of November 2021 in the United States, ** percent of surveyed participants said that they trusted Amazon to handle their personal data, whereas ** percent said they distrusted the service with their information. Overall, ** percent of respondents said that they did not trust Facebook with their private data, and ** percent said they did not trust TikTok with such information. Just under half of all respondents stated that they trusted Google and ** percent trusted Microsoft.

Economic Trust in Children: Study 1Trial data from Study 1 (Access versus No Access). Key for variable codes included as a separate tab in the file.Rosati_etal_Trust_Study1.xlsxEconomic Trust in Children: Study 2Trial data from Study 2 (Investment Game versus Dictator Game). Key for variable codes included as a separate tab in the file.Rosati_etal_Trust_Study2.xlsxEconomic Trust in Children: Study 3Trial data from Study 3 (Trustworthy Partner versus Untrustworthy Partner). Key for variable codes included as a separate tab in the file.Rosati_etal_Trust_Study3.xlsx

Listing of Trust Companies in Texas