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.
709
datasets available to search
ShareScore release 0.9.0
Dataset results
709 results for “Coverage”
The Performance of Posterior Partial Coverage Coronal Restorations Luted With Two Differrent Systems.
ClinicalTrials.gov study NCT06457737. IPD Sharing: NO. Countries: 1. Publications: 15.
Sectoral coverage and prices of carbon pricing mechanisms introduced since 1990
Open the record for dataset details and reuse information.
Data from: The invisible species: Big data unveil coverage gaps in the Atlantic forest hotspot
Open the record for dataset details and reuse information.
Data from: Low coverage genomic data resolve the population divergence and gene flow history of an Australian rain forest fig wasp
Open the record for dataset details and reuse information.
Coverage data in males and females, and genetic markers used for genetic mapping of the guppy LG12 (sex chromosome pair)
Open the record for dataset details and reuse information.
Data coverage, biases, and trends in a global citizen-science resource for monitoring avian diversity
Open the record for dataset details and reuse information.
Low-coverage whole genome sequencing for highly accurate population assignment: Mapping migratory connectivity in the American Redstart (Setophaga ruticilla)
Open the record for dataset details and reuse information.
Laminar specificity and coverage of viral-mediated gene expression restricted to GABAergic interneurons and their parvalbumin subclass in marmoset primary visual cortex
Open the record for dataset details and reuse information.
Dataset for: How eDNA data filtration, sequence coverage, and primer selection influence assessment of fish communities in northern temperate lakes
Open the record for dataset details and reuse information.
Data from: Age-appropriate vaccination coverage and its associated factors for pentavalent 1-3 and measles vaccine doses, in northeast Ethiopia: A community-based cross-sectional study
Open the record for dataset details and reuse information.
Genotypes of Aedes aegypti mosquitoes derived from SNP chip and low-coverage whole genome sequencing for platform cross-validation
Open the record for dataset details and reuse information.
International media coverage of the Bolivian jaguar trade
Open the record for dataset details and reuse information.
Epidemiological and evolutionary consequences of periodicity in treatment coverage
Open the record for dataset details and reuse information.
A comparison of phylogenomic inference pipelines for low-coverage whole-genome sequencing in Formica ants
Open the record for dataset details and reuse information.
Ambiguity in medical concept normalization: An analysis of types and coverage in electronic health record datasets
Open the record for dataset details and reuse information.
Data from: Standardising fossil disparity metrics using sample coverage
Open the record for dataset details and reuse information.
EDI and NEON dataset descriptions and coverage to support the paper "ecocomDP: A flexible data design pattern for ecological community survey data"
This dataset contains an inventory for the paper entitled "ecocomDP: A flexible data design pattern for ecological community survey data" (O'Brien et al), submitted to Ecological Informatics. The paper describes an approach for harmonizing and reformatting community survey data such as organism abundance or cover measurements. Data currently using this data model and workflow approach are from the repository of the Environmental Data Initiative (EDI), the Long Term Ecological Research (LTER) Network, and the National Ecological Observatory Network (NEON). Data were assembled for this analysis in late 2020. The inventory is composed of two tables, describing data from EDI (including LTER) and data from NEON. The EDI inventory includes information for 70 datasets: identifiers for both the original and converted datasets, and basic coverage information such as temporal coverage (range of years and a measurement of sampling evenness), spatial coverage (maximum bounding coordinates and area of the "bounding box"), and taxonomic coverage (taxonomic classes). The NEON inventory contains information from 11 continent-wide NEON data products, divided into individual field sites to be more spatially compatible with EDI and LTER data. Taxonomic coverage is by group (e.g., algae, birds) rather than explicit taxonomic classes. Spatial coverage is the area of a field sampling site polygon. Temporal coverage includes the same minimum and maximum sampling years and temporal evenness measures as for the EDI data plus a count of months during that period when sampling occurred. At the time of data download, NEON data was considered provisional, however identifiers are persistent and now deliver final, "released" data. Also included in the data package is a script to reformat inventory data and create Figure 3 of the paper.
Data used in "Evaluation of topography and vegetation coverage impacts on watershed-scale active layer freeze-thaw processes with a simple algorithm in permafrost region on the Qinghai-Tibet Plateau"
<p>This is the data used in the manuscript "Evaluation of topography and vegetation coverage impacts on watershed-scale active layer freeze-thaw processes with a simple algorithm in permafrost region on the Qinghai-Tibet Plateau" (JGR earth surface 2020JF005564 ).</p>
TesCaV: An Approach for Learning Model-based Testing and Coverage in Practice, Experimental Data
<p>The data in this sheet provides the result of the exploratory experiment presented in the following paper:</p> <p>Beatriz Marín, Sofía Alarcón, Giovanni Giachetti, and Monique Snoeck. (2020) TesCaV: An Approach for Learning Model-based Testing and Coverage in Practice, in Fabiano Dalpiaz, Jelena Zdravkovic, Pericles Loucopoulos (eds), Proceedings of the 14th International Conference on Research Challenges in Information Science, LNCS, Springer.</p> <p> </p>
Understanding the Differences in the Unit Tests Produced by Humans and Coverage-Directed Automated Generation
<p>Automated test generation - the use of tools to create all or part of test cases - has a critical role in controlling the cost of testing. A particular area of focus in automated test generation research is unit testing. Unit tests are intended to test the functionality of a small isolated unit of code - typically a class. </p> <p>In automated test generation research, it is not abnormal to compare the effectiveness of the test cases generated by automation to those written by humans. Indeed, a common premise of automation research - implicitly or explicitly - is that effective automation can replace human effort. The hypothesis postulated is that, if we make enough advances, a tool could replace the tremendous effort expended by a human tester to create those unit tests. </p> <p>This observation leads to two natural questions. Do the tests produced by humans and automation differ in the types of faults they detect? If so, in what ways are the tests produced and the faults detected different? Understanding when and how to deploy automation requires a clearer understanding of how the tests produced by humans and automation are different, and how those differences in turn affect the ability of those test cases to detect faults. Insight into the differences between human and automation-produced test cases could lead not only to improvements in the ability of automation to replace human effort, but improvements in our ability to use automation to augment human effort. The goal of this study is to explore and attempt to quantify those differences. </p> <p>In this study, we make use of the EvoSuite test generation framework for Java. We generate test suites targeting two configurations - a traditional single-criterion configuration targeting Branch Coverage over the source code and a more sophisticated multi-objective configuration targeting eight criteria. Controlling for coverage level, we compare the suites generated by EvoSuite to those written by humans for five mature, popular open-source systems in terms of both their syntactic structure and their ability to detect 45 different types of faults. Our goal is not to declare a "winner'", but to identify the areas where humans and automation differ in their capabilities, and - in turn - to make recommendations on how human and automation effort can be combined to overcome gaps in the coverage of the other. We aim to identify lessons that will improve human practices, lead to the creation of more effective automation, and present natural opportunities to both augment and replace human effort. </p>
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.