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Dataset results
9 results for “Source Finding”
Compact continuum source finding for next generation radio surveys
<p>This is a data set that accompanies the paper "Compact continuum source finding for next generation radio surveys" (2012MNRAS.422.1812H)</p> <p>The image files and source catalogues contained here were used to test the completeness and false detection rate of a number of source finding algorithms including: Aegean, Selavy, Sfind, SExtractor, and IMSAD. These data can be used to assess the performance of future source finding codes, and to verify the the performance of code during development.</p>
Sharing research data and findings relevant to the novel coronavirus (COVID-19) outbreak - Literature sources
<p>The spreadsheet in the present dataset (CSV format) includes the sources considered during the literature review stage for the report: From intent to impact: Investigating the effects of open sharing commitments. Please note that not all sources in this deposit have been referenced in the above-mentioned report and that the report may include additional sources</p>
Finding Reliable Sources: Evaluation Datasets
<p><strong>Finding Reliable Sources</strong> (<strong>FRS</strong>) is an NLP/ML task, in which, given a single-sentence claim, we aim to automatically find reliable sources that could be used to confirm or refute it. This repository includes evaluation datasets that could be used to train and test solution for this problem. Datasets are organised as large collections of individual records, each consisting of (1) a claim expressed a short text fragment and (2) a list of identifiers (DOI, ISBN, arXiv ID or URL) of reliable sources associated with it.</p> <p>Two datasets are included:</p> <ul> <li>Claim-Source Pairing Dataset (CSP) contains 32 million textual contexts paired with 24 million source identifiers mined from English Wikipedia,</li> <li>FEVER-FRS contains 15,798 claims gathered for the <a href="https://fever.ai/">FEVER shared task</a>, supplemented with source identifiers using Wikipedia.</li> </ul> <p>The data were gathered within a study described in the article <em>"Countering Disinformation by Finding Reliable Sources: a Citation-Based Approach"</em>, presented at the 2022 International Joint Conference on Neural Networks (IJCNN 2022). Please refer to the article (in <a href="https://doi.org/10.1109/IJCNN55064.2022.9891941">conference proceedings</a> or <a href="https://home.ipipan.waw.pl/p.przybyla/bib/Learning_to_Cite_3_CR.pdf">authors' version</a>) for more information on the FRS task, data collection procedures and evaluation results. The research was done within the <a href="https://homados.ipipan.waw.pl/">HOMADOS</a> project at the <a href="https://ipipan.waw.pl/">Institute of Computer Science</a>, Polish Academy of Sciences.</p> <p>The datasets described here are based on the <a href="https://doi.org/10.5281/zenodo.6539054">Wikipedia Complete Citation Corpus</a> (WCCC), which also includes the train/test split for CSP.</p> <p>The CSP dataset is uploaded in three variants, using different text fragments as 'context'. In '<strong>s</strong>', the context consists of a single sentence preceding (or containing) a citation. The '<strong>ts</strong>' variant additionally includes a title of the Wikipedia article, while '<strong>ss</strong>' contains two sentences preceding a citation. Each variant contains subsequently numbered TSV files, including lines with the following values:</p> <ul> <li>document ID (see <a href="http://doi.org/10.5281/zenodo.6539054">WCCC</a> for full document metadata),</li> <li>textual context,</li> <li>a list of (at least one, possibly many) source identifiers.</li> </ul> <p>The FEVER-FRS dataset was created based on the <a href="https://fever.ai">FEVER shared task</a> data. In the original dataset, each claim is matched with a set of 'evidence' sentences from Wikipedia that either support or refute it. For the purpose of evaluating FRS solutions, we replaced each evidence sentence with the identifiers to external sources that are cited alongside it. This was not always possible (e.g. for Wikipedia sentences with no sources, see the paper for details), but we managed to find 4414 and 11384 claims that are, respectively, refuted or supported by the provided sources.</p> <p>Each line of the FEVER-FRS TSV files contains the following:</p> <ul> <li>text of a short claim,</li> <li>a list of (at least one, possibly many) source identifiers.</li> </ul> <p>The source code code for generating both datasets (CSP from Wikipedia and FEVER-FRS from FEVER) can be obtained from a <a href="https://github.com/piotrmp/finding_reliable_sources">GitHub repository</a>.</p>
[Tool demo] Prospector: a Tool to Find Fixes to Known Vulnerabilities of Open-Source Projects
<p> </p> <p><strong>>>>> TOOL DEMO SCREENCAST</strong>: please download file <strong><a href="https://zenodo.org/record/7974442/files/tool_demo_final.mp4?download=1">tool_demo_final.mp4</a></strong>. <strong><<<<</strong></p> <p> </p> <p><strong>ABSTRACT</strong>: Though vulnerability databases are key for monitoring known vulnerabilities in open-source projects, they rarely contain information about the code changes that fix the flaws they describe. Finding them is time-consuming and error-prone as it involves the analysis of multiple, unstructured resources. </p> <p>In this paper we present \prospector, a tool that supports mapping<br> vulnerability advisories from vulnerability databases onto the corresponding fix in the source code. \prospector employs a set of heuristics that mimics and automates the<br> strategies that would be employed by human security experts.</p> <p>Given an advisory expressed in natural language, \prospector processes the commits found in the target source code repository, ranks them based on a set of predefined rules, and produces a report that the user can inspect to<br> determine which commits to retain as the actual fix.<br> The tool is publicly available and is released under the Apache 2.0 license.</p>
Data from: Poor infant and young child feeding practices and sources of caregivers' feeding knowledge in rural Hebei Province, China: findings from a cross-sectional survey
Objectives: To obtain a general overview of infant and young child feeding practices in one rural county in China and identify current delivery channels and challenges. Design: A cross-sectional study. Setting: A rural county, Zhao County, in Hebei Province, China. Participants: 10 clusters were first selected within each township (16 townships in total) with proportional to population size sampling. In each cluster, a name list was used to select 13 children aged 0–23 months. We interviewed caregivers of all the selected children. Primary and secondary outcomes measures: Coverage of infant feeding practices, reasons for low coverage of infant feeding practices and current delivery channels of infant feeding practices. Results: Findings from our survey indicated that infant feeding practices were poor. Early initiation of breastfeeding was only 22.4%, exclusive breastfeeding for 6 months was less than 10% and continued breastfeeding up to the age of two was just 38.2%. Only 32.5% of children were given iron-rich or iron-fortified foods. The leading sources of infant feeding information were family members, neighbours, friends and popular media. Only around 20% of the information came from health facilities and nearly none came from communities. Household property data showed that 99.9% of households owned televisions and 99.4% owned mobile phones. In addition, 61.2% of the households owned computers, with 54.8% having access to the internet. Conclusions: Few caregivers of children in Zhao County received feeding information during pregnancy and after delivery. Moreover, their feeding knowledge and practices were poor. Multi-channel approaches, delivered through health facilities, community resources, popular media, the internet and mobile phones, hold potential to improve infant feeding practices and should be explored in future studies. Strengths and limitations: Although this study took place only within one county, a full range of globally standard feeding indicators was used to assess the feeding practices of caregivers in our study. The name lists of children in some villages may not be complete, and therefore selection bias may have occurred. Some indicators may have recall bias due to long recall time. Trial registration number: ChiCTR-PRC-11001446.
Dataset and source code for the paper "Finding Near-Optimal Configurations in Colossal Product Spaces with Statistical Confidence"
<p>Dataset and source code for the paper "Finding Near-Optimal Configurations in Colossal Product Spaces with Statistical Confidence"</p>
Data from: Poor infant and young child feeding practices and sources of caregivers’ feeding knowledge in rural Hebei Province, China: findings from a cross-sectional survey
Open the record for dataset details and reuse information.
ROP: Dumpster Diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues
GEO Series GSE109484. Homo sapiens. 47 samples. Type: Expression profiling by high throughput sequencing.
ROP: Dumpster Diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues
GEO Series GSE109313. Homo sapiens. 37 samples. Type: Expression profiling by high throughput sequencing; Other.
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
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.