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.
3
datasets available to search
ShareScore release 0.9.0
Dataset results
3 results for “text comprehension”
When text simplification is not enough: Could a graph-based visualization facilitate consumers' comprehension of dietary supplement information?
<p>Background: Dietary supplements are widely used. However, dietary supplements are not always safe. For example, an estimated 23,000 emergency room visits every year in the United States were attributed to adverse events related to dietary supplement use. With the rapid development of the Internet, consumers usually seek health information including dietary supplement information online. To help consumers access quality online dietary supplement information, we have identified trustworthy dietary supplement information sources and built an evidence-based knowledge base of dietary supplement information—the integrated DIetary Supplement Knowledge base (iDISK) that integrates and standardizes dietary supplement related information across these different sources. However, as information in iDISK was collected from scientific sources, the complex medical jargon is a barrier for consumers' comprehension. Objective: To assess how different approaches to simplify and represent dietary supplement information from iDISK will affect lay consumers' comprehension.</p> <p>Methods: Using a crowdsourcing platform, we recruited participants to read dietary supplement information in four different representations from iDISK: (1) original text, (2) syntactic and lexical text simplification, (3) manual text simplification, and (4) a graph-based visualization. We then assessed how the different simplification and representation strategies affected consumers' comprehension of dietary supplement information in terms of accuracy and response time to a set of comprehension questions.</p> <p>Results: With responses from 690 qualified participants, our experiments confirmed that the manual approach, as expected, had the best performance for both accuracy and response time to the comprehension questions, while the graph-based approach ranked the second outperforming other representations. In some cases, the graph-based representation outperformed the manual approach in terms of response time.</p> <p>Conclusions: A hybrid approach that combines text and graph-based representations might be needed to accommodate consumers' different information needs and information seeking behavior.</p>
DSText V2: A Comprehensive Video Text Spotting Dataset for Dense and Small Text
<p>Recently, video text detection, tracking, and recognition in natural scenes are becoming very popular in the computer vision community.However, most existing algorithms and benchmarks focus on common text cases~(\eg normal size, density) and single scenario, while ignoring extreme video text challenges, \ie{} dense and small text in various scenarios. In this paper, we establish a video text reading benchmark, named DSText V2, which focuses on \textbf{D}ense and \textbf{S}mall text reading challenges in the video with various scenarios. Compared with the previous datasets, the proposed dataset mainly include three new challenges: 1) Dense video texts, a new challenge for video text spotters to track and read. 2) High-proportioned small texts, coupled with the blurriness and distortion in the video, will bring further challenges. 3) Various new scenarios, \eg{} `Game', `Sports', etc. The proposed DSText V2 includes 140 video clips from 7 open scenarios, supporting three tasks, \ie{} video text detection (Task 1), video text tracking (Task 2), and end-to-end video text spotting (Task 3). In this article, we describe detailed statistical information of the dataset, tasks, evaluation protocols, and the results summaries. Most importantly, a thorough investigation and analysis targeting three unique challenges derived from our dataset are provided, aiming to provide new insights. Moreover, we hope the benchmark will promise video text research in the community. DSText v2 is built upon DSText v1, which was previously introduced to organize the ICDAR 2023 competition for dense and small video text.</p>
When text simplification is not enough: Could a graph-based visualization facilitate consumers’ comprehension of dietary supplement information?
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.