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 “Open Definition”
Review of definitions of Open Peer Review in the scholarly literature 2016
<p>This data set contains:</p> <ul> <li>Full data files of a 2016 review of definitions of Open Peer Review in the scholarly literature in xls and csv formats</li> <li>Description of data collection methodology (txt)</li> <li>Readme file (txt)</li> </ul> <p>The term “open peer review” has neither a standardized definition nor an agreed schema of its features and implementations. Recognising the absence of a consensus view on what OPR is, OpenAIRE has undertaken a systematic review of definitions of “open peer review” or “open review”, to create a corpus of 122 definitions. These definitions have been systematically analysed to build a coherent typology of the many different innovations in peer review signified by the term and hence provide the precise technical definition currently lacking. This quantifiable data offers rich information on the range and extent of differing definitions over time and by broad subject area.</p> <p>Contact:</p> <p>Tony Ross-Hellauer: http://orcid.org/0000-0003-4470-7027 / ross-hellauer@sub.uni-goettingen.de</p>
Replication Kit: "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects"
<p><strong>Replication Kit for the Paper "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects"</strong><br> This additional material shall provide other researchers with the ability to replicate our results. Furthermore, we want to facilitate further insights that might be generated based on our data sets.</p> <p><strong>Structure</strong><br> The structure of the replication kit is as follows:</p> <ul> <li><strong>additional_visualizations</strong>: contains additional visualizations (Venn-Diagrams) for each projects for each of the data sets that we used</li> <li><strong>data_analysis</strong>: contains python scripts that we used to analyze our raw data</li> <li><strong>data_collection_tools</strong>: contains all source code used for the data collection, including the used versions of the <a href="https://github.com/comfort-framework">COMFORT framework</a>, the <a href="https://github.com/ftrautsch/BugFixClassifier">BugFixClassifier</a>, and the used tools of the <a href="https://github.com/smartshark">SmartSHARK environment</a>;</li> <li><strong>mongodb_no_authors</strong>: Archived dump of our MongoDB that we created by executing our data collection tools. The "comfort" database can be restored via the mongorestore command.</li> </ul> <p><br> <strong>Additional Visualizations</strong><br> We provide two additional visualizations for each project:<br> 1) <project_name>\_disj\_ieee\_venn (visualizations for the DISJ data set)<br> 2) <project_name>\_all\_ieee\_venn (visualizations for the ALL data set)</p> <p>For each of these data sets there exist one visualization for each project that shows four Venn-Diagrams for each of the different defect types. These Venn-Diagrams show the number of defects that were detected by either unit, or integration tests (or both).</p> <p>Furthermore, we added boxplots for each of the data sets (i.e., ALL and DISJ) showing the scores of unit and integration tests for each defect type.</p> <p><br> <strong>Analysis scripts</strong><br> Requirements:<br> - python3.5<br> - tabulate<br> - scipy<br> - seaborn<br> - mongoengine<br> - pycoshark<br> - pandas<br> - matplotlib</p> <p>Both python files contain all code for the statistical analysis we performed.</p> <p><strong>Data Collection Tools</strong><br> We provide all data collection tools that we have implemented and used throughout our paper:</p> <ul> <li><strong>BugFixClassifier</strong>: Used to classify our defects.</li> <li><strong>comfort-core</strong>: Core of the comfort framework. Used to classify our tests into unit and integration tests and calculate different metrics for these tests.</li> <li><strong>comfort-jacoco-listner</strong>: Used to intercept the coverage collection process as we were executing the tests of our case study projects.</li> <li><strong>jSHARK</strong>: Library that contains models for the used ORM mapper that is used inside the SmartSHARK environment (for Java).<strong> </strong></li> <li><strong>pycoSHARK</strong>: Library that contains models for the used ORM mapper that is used inside the SmartSHARK environment (for Python).</li> <li><strong>tools-changedistiller</strong>: Version of ChangeDistiller that we used within our comfort-core framework.</li> <li><strong>vcsSHARK</strong>: Used to collect data from the VCSs of the projects.</li> </ul> <p> </p> <p> </p>
Publications and filtering decisions for the systematic literature review in "Towards a Common Definition of Open Data Intermediaries"
<p>This file contains the list of publications and filtering decisions of the systematic literature review conducted for the article "Towards a common definition of open data intermediaries" published in the Digital Government: Research and Practice (DGOV) journal (https://doi.org/10.1145/3585537). The literature search was done on 1 June 2022 and there was no start date set (i.e. all relevant literature up to 1 June 2022 was included).</p> <p>There are 4 documents in this folder (apart from README text describing the data in each document):</p> <ol> <li>Stage-0 Search results</li> <li>Stage-1 Remove redundant</li> <li>Stage-2 Remove irrelevant</li> <li>Stage-3 Final filtering</li> </ol> <p>The authors acknowledge the financial support from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 955569, "Towards a sustainable Open Data ECOsystem" (ODECO).</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.