Find research datasets worth reusing

York Archaeological Trust 1981.7.33205.SY102 3D Archaeological Use-wear Raw Measurements

This dataset contains the 51 raw measurements/scans taken before the mesh creation step for object 1981.7.33205.SY102 in the collections of York Archaeological Trust. 1981.7.33205.SY102 is a Mortarium in standard Ebor oxidised fabric (M3) (Monaghan 1997, 1028) from Coppergate. Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres. This dataset consists of 105,524,551 points - additional metadata included in associated spreadsheet.

York Archaeological Trust 1989.21.2205 3D Archaeological Use-wear Raw Measurements

This dataset contains the 27 raw measurements/scans taken before the mesh creation step for object 1989.21.2205 in the collections of York Archaeological Trust. 1989.21.2205 is a small Samian ware cup fragment. Close analysis was not undertaken at time of data capture. Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres. This dataset consists of  14,583,615 points - additional metadata included in associated spreadsheet.

York Archaeological Trust 1989.21.2393.LL42 Archaeological 3D Use-wear Raw Measurements

This dataset contains the 18 raw measurements/scans taken before the mesh creation step for object 1989.21.2393.LL42 in the collections of York Archaeological Trust. 1989.21.2393.LL42 is a dish of Ebor Ware 2 (Monaghan 1997, 875) from 34-41 Blossom Street (Lion and Lamb). Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres. This dataset consists of 26,065,666 points - additional metadata included in associated spreadsheet.

York Archaeological Trust 1988.24.7669-7627 Archaeological 3D Use-wear Raw Measurements

This dataset contains the 32 raw measurements/scans taken before the mesh creation step for object 1988.24.7669-7627 in the collections of York Archaeological Trust. 1988.24.7669-7627 is a small campanulate bowl. Close analysis was not undertaken at time of data capture. Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres. This dataset consists of 31,429,144 points - additional metadata included in associated spreadsheet.

York Archaeological Trust 1984.132.4148 3D Archaeological Use-wear (Incomplete Scan) Raw Measurements

This dataset contains the 25 raw measurements/scans taken before the mesh creation step for object 1984.132.4148 in the collections of York Archaeological Trust. 1984.132.4148 is a dish. Close analysis was not undertaken at time of data capture. Scanning was not complete. Holes in scan data: one small circle at the very centre of the base of the dish, and gaps in data capture under the rim.  Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres. This dataset consists of 27,629,672 points - additional metadata included in associated spreadsheet.

York Archaeological Trust 1975.6.7364.4244 Archaeological 3D Use-wear Raw Measurements

This dataset contains the 78 raw measurements/scans taken before the mesh creation step for object 1975.6.7364.4244 in the collections of York Archaeological Trust. 1975.6.7364.4244 is a large Ebor Ware carinated bowl (Monaghan 1993, 783, 875). Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres. This dataset consists of 278,742,689 points - additional metadata included in associated spreadsheet.

opencc-by-4.0Jan 2021View details →

Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects

The dataset contains analyzed projects and modules for the paper "Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects". The contents are the following: <ul> <li><a href="/api/files/f0b463e1-7c71-4f10-8aa4-aa4ed963bd9e/manual-studied-modules.csv?versionId=8258b59c-3f88-487b-a4a3-007cb362a44a">manual-studied-modules.csv</a>: Manually analyzed Maven modules mentioned in Section 5.2</li> <li><a href="https://zenodo.org/api/files/f0b463e1-7c71-4f10-8aa4-aa4ed963bd9e/projects.zip">projects.zip</a>: Instrumented and Mutated Github Projects. Projects list applied mutation changes, and their dynamic and static call graph.</li> </ul>

opencc-by-4.0Jan 2022View details →

Database for Perceived Inclusivity and Trust in Protected Area Management Decisions among Stakeholders in Alaska

This database is part of a state-wide survey in Alaska, USA. An online Qualtrics interface was used to administer the survey to a panel of Alaskan residents from June to August 2020.

opencc-by-sa-4.0Feb 2018View details →

Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action

Mental disorders have an enormous impact in our society, both in personal terms and in the economic costs associated with their treatment. In order to scale up services and bring down costs, administrations are starting to promote social interactions as key to care provision. We analyze quantitatively the importance of communities for effective mental health care, considering all community members involved. By means of citizen science practices, we have designed a suite of games that allow to probe into different behavioral traits of the role groups of the ecosystem. The evidence reinforces the idea of community social capital, with caregivers and professionals playing a leading role. Yet, the cost of collective action is mainly supported by individuals with a mental condition - which unveils their vulnerability. The results are in general agreement with previous findings but, since we broaden the perspective of previous studies, we are also able to find marked differences in the social behavior of certain groups of mental disorders. We finally point to the conditions under which cooperation among members of the ecosystem is better sustained, suggesting how virtuous cycles of inclusion and participation can be promoted in a ’care in the community’ framework.

opencc-by-4.0Dec 2020View details →

Monitoring knowledge, risk perceptions, preventive behaviours and trust to inform pandemic outbreak response.

The study is part of the large project promoted by WHO Regional Office for Europe called “Monitoring knowledge, risk perceptions, preventive behaviours and trust to inform pandemic outbreak response” and carried out in over 30 countries of the WHO European Region (Registered ISRCTN on 11/05/2021, ID: ISRCTN26200758). In Italy, the survey was conducted administering an online questionnaire developed ad hoc by the WHO in four waves (January-May 2021) to a sample of 10.000 individuals aged 18-70 years. A detailed sampling plan was developed to obtain a representative sample of the Italian adult population. The following variables were taken into account for stratification of the participants: gender by age (four age groups: 18-34 years, 35-44 years, 45-54 years, 55-70 years); geographical area (four areas: North West, North East, Centre, South and Islands); size of living centers (two classes: above and below 100,000 inhabitants); level of education (up to lower middle school, beyond lower middle school); and employment situation (employed, not employed). At the end of each survey’s wave, a weighting procedure has been applied to accurately restore the proportionality of the total sample examined with the reference population, according to the most recent data of the Italian Statistics Institute (ISTAT, 12/31/2019). In particular, data have been weighted for the main socio-demographic and geographic variables (e.g., sex by age by geographical area, occupation, educational qualification, geographical area by size of living centers). The sample size made it possible to maintain a sampling error of less than 2% (at the significance level of 95%) and to control the error of estimates within groups or subgroups of interest. The interviews were conducted by Doxa S.p.a. and carried out with the CAWI technique (Computer Assisted Web Interviewing) on an online panel and on the Confirmit software platform used by Doxa S.p.a. The average administration time was about 18-20 minutes. This study was approved by the Ethics Committee of the IRCCS San John of God Fatebenefratelli of Brescia (n° 72-2020), and all participants provided written informed consent. The primary objectives are to: ● Monitor variables that are critical for population behaviour to control transmission of the novel coronavirus, including risk perceptions, knowledge, self-efficacy, confidence in institutions, behaviours, rumours, affect, worry, resilience, trust in/use of information sources and more. ● Document changes over time in these factors to understand the effect of the pandemic process, new developments, events or measures taken. ● Monitor possible issues, e.g. related to misinformation or distrust, as they emerge, to allow early response. ● Identify relationships between variables to identify levers for effective and appropriate responses. ● Explore the relationship of psychological variables (e.g. worry, resilience, trust, affect) with the epidemiological situation and the events and measures taken. ● Identify gaps between perceived and actual knowledge. ● Evaluate the effectiveness of pandemic response measures, and the acceptance and effectiveness of policies and restrictions implemented, including the easing of such restrictions. The secondary objectives are to: ● Contribute to post-outbreak evaluation, thereby contributing to the continued regional/global efforts to better understand mechanisms of crisis response. ● If additional research capacity is available, the data can be triangulated with data on media reporting, COVID-19 cases and other.● If additional research capacity is available, the data can be triangulated with data on media reporting, imported or confirmed cases, etc.: The relationship between psychological variables and characteristics of the outbreak situation can be explored (i.e. how closely the perceived risk mirrors reported cases, relative import risk, media reports). This approach allows a citizen-centred approach where insights into population perceptions and behaviours inform COVID-19 actions, alongside epidemiological data and considerations of economic, cultural, ethical, structural political nature and other. The WHO questionnaire includes 21 different thematic areas noteworthy for the investigation of COVID-19 experience. The questionnaire was translated into specific country language by each recruiting site, following the WHO’s guidelines for translations of tools into other languages. The process included the following steps: forward translation, panel experts, back-translation, pre-test and cognitive interviews and, finally, development of the final version. Variables being surveyed include the following: • Socio-demography; • COVID-19 personal experience; • Health literacy; • COVID-19 risk perception; • Probability and Severity; • Preparedness and Perceived self-efficacy; • Prevention – own behaviours; • Affect; • Trust in sources of information; • Use of sources of information; • Frequency of Information; • Trust in institutions (perceptions); • Policies, interventions (perceptions); • Conspiracies (perceptions); • Resilience (perceptions); • Testing and tracing; • Fairness (perceptions); • Lifting restrictions (pandemic transition phase); • Unwanted behaviour; • Wellbeing; • COVID-19 vaccine.

opencc-by-4.0Jul 2021View details →

Artifacts for Towards Achieving Trust Through Transparency and Ethics [RE 21]

Included in this repository are the Open and Axial/Selective Coding Matrices for the Grounded Theory Methodology for "Towards Achieving Trust Through Transparency and Ethics". Also included are the Softgoal Interdependency graphs developed as a result of the Grounded Theory Literature Review. The methodology is described in the paper, and this dataset exists for anybody to review and re-use these artifacts created as a result of the methodology.

opencc-by-4.0Jul 2019View details →

Artifacts supplementing the ACM DTRAP 2020 article "Will You Trust This TLS Certificate? Perceptions of People Working in IT (extended version)"

These research artifacts supplement the following two publications: <ul> <li>Will You Trust This TLS Certificate? Perceptions of People Working in IT [ACSAC 2019], DOI 10.1145/3359789.3359800, more details at https://crocs.fi.muni.cz/public/papers/acsac2019</li> <li>Will You Trust This TLS Certificate? Perceptions of People Working in IT (extended version) [ACM DTRAP 2020], DOI 10.1145/3419472, more details at https://crocs.fi.muni.cz/public/papers/dtrap2020</li> </ul> The artifacts contain the full experimental setup (as described in Section 2.1 of the paper) and the complete anonymized dataset underlying the evaluation presented in Sections 3 and 4. The experimental setup contains the documents accompanying the task: the informed consent, pre-task questionnaire, task description, trust scales, and the list of questions posed during the post-task interview (all in PDFs). We further include the custom website with certificate validation documentation for the “redesigned” condition (static HTML). While working on the task, participants in the “redesigned” condition could access this website via a link that was in the redesigned error messages. Furthermore, we provide the software with which the participants interacted.  It contains the displayed error messages and validated certificates. These things are available both individually and incorporated in a snapshot of a virtual machine used at the experiment (importable directly into VirtualBox). The collected data is presented in a single dataset (SPSS format; you can use PSPP as a free alternative). It includes the analysis syntax files to obtain the numerical results presented in the paper. For each participant, the dataset contains: 1) pre-task questionnaire answers, 2) reported trust ratings, 3) sub-task timing, 4) information on whether they browsed the Internet and 5) the interview codes assigned. Note that we do not publish the interview transcripts to preserve participant privacy.

opencc-by-4.0Oct 2023View details →

Benchmark movement data set for trust assessment in human robot collaboration

In the Drapebot project, a worker is supposed to collaborate with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from a standard Trust questionnaire (Trust perception scale - HRI, Schaefer 2016). Data has been collected in the transport and draping tasks (counterbalanced) from 20 participants,  7 female and 13 male, average age 25 (SD = 4.0). Average height was 1.74 meters (SD = 0.1). One session consists of 24 trials on average for the transport and draping task resulting in 951 trials across all conditions. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements). Data organization There are 20 files for 20 participants of each task accordingly (transport and draping). The name of the files is P01SD, where the number 01 is the participant the D stands for draping. Accordingly, P01ST stands for transport. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data: <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a> For more information on each specific data and/or sensors please see the xsens manual (Link above) Data Annotation For each procedure there is an annotation file called sorted_draping.xlsx and sorted_transport.xlsx. In these files the first column is the frame and from column 2 until column 21 are the annotations for each procedure for each participant. The annotations describe the different phases during the procedures for each data frame recorded by xsens: <ul> <li>Transport phases: pick, transport, drop, return</li> <li>Draping phases: approach, draping, return</li> </ul> The file trustscores.xlsx includes some demographic data as well as the results of the trust questionaire for each participant and each task, including the scores for the individual items as well as the calculated trust score. The different columns are: <ul> <li>Subject: participant number for crossreferencing with annotation and movement data</li> <li>Transport.Speed: denoting the robot speed (fast or slow)</li> <li>Age: age of the participant</li> <li>Gender: gender of the participant</li> <li>DominantHand: dominant hand of the participant (left or right)</li> <li>Height: height of the participant</li> <li>Score for answers of the participant in related questions category.</li> </ul> This is followed by the trust questionaire items: <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> The last two columns are <ul> <li>TrustScore – Final trust score calculated from all questions</li> <li>Task – Which task is being performed (Transport/Draping)</li> </ul>

opencc-by-sa-4.0May 2020View details →

York Archaeological Trust 1988.24.7669-7627 Archaeological 3D Use-wear

Final 3D dataset for object 1988.24.7669-7627 in the collections of York Archaeological Trust. 1988.24.7669-7627 is a small campanulate bowl. Close analysis was not undertaken at time of data capture. Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres. Final dataset consists of 5,691,673 triangles - additional metadata included in associated spreadsheet.

opencc-by-sa-4.0May 2020View details →

York Archaeological Trust 1987.24.71754.23587 3D Archaeological Use-wear

Final 3D dataset for object 1987.24.71754.23587 in the collections of York Archaeological Trust. 1987.24.71754.23587 is a Lower Nene Valley Colour-coated ware (Tomber and Dore 1998, 118, LNV CC) dish from Wellington Row. Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres. Final dataset consists of 12,044,342 triangles - additional metadata included in associated spreadsheet.

York Archaeological Trust 1975.18.III.176 Archaeological 3D Use-wear Raw Measurements

This dataset contains the 121 raw measurements/scans taken before the mesh creation step for object 1975.18.III.176 in the collections of York Archaeological Trust. 1975.18.III.176 is a mortarium of North Gaulish White ware 4 (Tomber and Dore 1998, 75, NOG WH 4) that has experienced modern reconstruction. Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres. This dataset consists of 370,399,732 points - additional metadata included in associated spreadsheet.

opencc-zeroNov 2023View details →

dryad40/100

Data from: Building trust takes time: Limits to arbitrage for blockchain-based assets

The dataset contains all historical order book snapshots and blockchain network information used to generate the results for the paper "Building Trust takes Time". A blockchain replaces central counterparties with time-consuming consensus protocols to record the transfer of ownership. This settlement latency slows cross-exchange trading, exposing arbitrageurs to price risk. Off-chain settlement, instead, exposes arbitrageurs to costly default risk. We show with Bitcoin network and order book data that cross-exchange price differences coincide with periods of high settlement latency, asset flows chase arbitrage opportunities, and price differences across exchanges with low default risk are smaller. Blockchain-based trading thus faces a dilemma: reliable consensus protocols require time-consuming settlement latency, leading to arbitrage limits. Circumventing such arbitrage costs is possible only by reinstalling trusted intermediation, which mitigates default risk.

opencc-by-4.0Aug 2023View details →

Online trust in Information Society. Four representative database and questionnaire (Hungary, Romania, Poland, Czech Republic

This data collection was conducted by the Institute of the Information Society of the University of Public Service - Ludovika, and covered four Central European countries: the Czech Republic, Hungary, Poland, and Romania, with the aim of examining the characteristics of the use of information technology by the adult population in the region, mainly for communication purposes. A telephone survey was conducted in October and November 2019, and the results are representative of the population over 18 years of age in the four countries, categorized by age, gender, education, type of settlement and region. The data set contains: <ul> <li>Questionnaires in the original languages (Hungarian, Czech, Romanian and Polish), and all questionnaires in English language, too.</li> <li>The four databases with variables in English</li> <li>Merged database of four databases</li> <li>Code table about variables </li> </ul>