Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5
datasets available to search
ShareScore release 0.9.0
Dataset results
5 results for “smart additives”
Additional Resources for End-user Comprehension of Transfer Risks in Smart Contracts
<p>Google Docs versions of most of the PDFs can be found on https://linktr.ee/tethersurvey</p> <p> </p> <p>Additionally, a survey data visualizer is found on https://tether-survey.onrender.com/</p> <p> </p>
Additional Resources for Understanding End-User Perception of Transfer Risks in Smart Contracts
<p>This contains further resources for the work titled "Understanding End-User Perception of Transfer Risks in Smart Contracts", which is set to appear in CHI 2025.</p> <p>This work details an investigation into user understanding of transfer risks in ERC-20 blockchain smart contracts. An example transfer risk is a user being unable to transfer due to their account being blacklisted by the owner of the contract.</p> <p>A large portion of this work focuses on the most popular Ethereum smart contract (USD Tether). This details responses to a 110-participant survey on smart contract users, establishing their knowledge of transfer risks and various other perceptions. Included also are the results of statistical tests on these responses, the follow-up message to the respondents and more.</p> <p>Another portion of this work investigates the presence of transfer risks in other top ERC-20 contracts. The results of this investigation is also found here.</p> <p>This also includes results of blockchain analytics to establish the top ERC-20 addresses, as well as code used for processing.</p>
Smart Meter Water Consumption Measurements – Additional Evaluation Data
<p><strong>Data</strong><br> We collected the cumulative water consumption data in Germany using the commercially available smart meters <em>Hydrus 1.3, DN 20,00</em> from <em>Diehl Metering</em>. For evaluation purpose we further collected some label data on activity events and on out-of-house periods: <br> <em>Activity Data:</em> During defined time spans, we performed two typical household activities associated with water consumption and recorded the time at which they were performed. Specifically, we turned on various taps for 30 seconds and flushed the toilets.<br> We avoided concurrent water consumption and always waited at least 5~minutes between two activities.<br> <em>Out-of-house Data</em>: A separate data set is also collected in two test households. Besides the readings from the smart water meters, the absence times of the residents were also collected using the diary method. We handed the residents a log form in which they recorded those time points when no residents were in the house. The residents were also asked to note whether a washing machine or a dishwasher was running during a period of absence.</p> <p>Further information on data collection and description of the dataset can be found in Section 3 and Section 6 of the original article, which is available at the following DOI: <strong>TBD.</strong></p> <p><strong>Structure</strong><br> The <em>Zenodo</em> Archive has two folders, which distinguishes the two different sets of evaluation data. <br> The <em>Activity Data</em> contains subfolders for each of the examined frame. Each of these subfolders contains general information about the frame (e.g., household ID), the smart meter readings (<em>smartmeter.csv</em>) and a list of performed activities (<em>activities.csv</em>).<br> The <em>Out-of-house Data</em> contains two subfolders for the sampled households. These subfolders in turn contain a <em>periods.csv</em> file with the recorded times of presence, a <em>smartmeter.csv </em>file with the smart meter readings during the survey, and a general <em>info</em> document.</p>
SMART Building Additional Data
<p><strong>SMART Building Additional Data</strong></p> <p>Authors: J. Barthelemy, B. Arshad, N. Verstaevel, D. Guastella, P. Perez<br> Contact: SMART Infrastructure Facility - smart-iot@uow.edu.au<br> Version: 09 July 2020</p> <p><strong>Description</strong></p> <p>The time series data has been generated by Droplet and Pycom sensors installed in the SMART Infrastructure Facility of the University of Wollongong.</p> <p>This dataset consist of</p> <ul> <li>a subset of the original complete SMART Infrastructure Facility Building Data (see 10.5281/zenodo.3935060)</li> <li>and additional data collected by the Pycom sensors</li> </ul> <p>See the floor plans for the disposition of the additional sensors.</p> <p><strong>Notes</strong></p> <p>- Droplet sensors specifications: https://nube-io.com/wp-content/uploads/Droplet-Specifications-V5.0-1.pdf<br> - More information about the SMART Infrastructure Facility is available here: https://www.uow.edu.au/smart/</p>
Additional Resources for End-user Comprehension of Transfer Risks in Smart Contracts
Open the record for dataset details and reuse information.
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.