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867 results for “repositories”
Data for: Sustainable connectivity in a community repository
Open the record for dataset details and reuse information.
Code for: Multi-modal screening for synergistic neuroprotection of mild extremely preterm brain injury: Cell counting code repository
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Repository Analytics and Metrics Portal (RAMP) 2017 data
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Alignment between CARE Principles and Data Repository Activities
This dataset is designed to accompany the paper submitted to Data Science Journal: O'Brien et al, "Earth Science Data Repositories: Implementing the CARE Principles". This dataset holds a list of activites typically carried out by repositories that are related to the CARE Principles for Indigenous Data Governance. Activities are divided into four categories to reflect broad skill sets usually found in repository personnel: Situational Awareness, Outreach, Repository Protocols, Technology. Each activity was mapped to one or more of the CARE sub-principles, with booleans in individual columns.
Libraries.io Open Source Repository and Dependency Metadata
<p><strong>What is in this release?</strong></p> <p>In this release you will find data about software distributed and/or crafted publicly on the Internet. You will find information about its development, its distribution and its relationship with other software included as a dependency. You will not find any information about the individuals who create and maintain these projects.</p> <p>Further information and documentation on this data set can be found at https://libraries.io/data</p> <p>For enquiries please contact data@libraries.io</p> <p>This dataset contains seven csv files:</p> <p><strong>Projects</strong></p> <p>A project is a piece of software available on any one of the 34 package managers supported by Libraries.io.</p> <p><strong>Versions</strong></p> <p>A Libraries.io version is an immutable published version of a Project from a package manager. Not all package managers have a concept of publishing versions, often relying directly on tags/branches from a revision control tool.</p> <p><strong>Tags</strong></p> <p>A tag is equivalent to a tag in a revision control system. Tags are sometimes used instead of Versions where a package manager does not use the concept of versions. Tags are often semantic version numbers.</p> <p><strong>Dependencies</strong></p> <p>Dependencies describe the relationship between a project and the software it builds upon. Dependencies belong to Version. Each Version can have different sets of dependencies. Dependencies point at a specific Version or range of versions of other projects.</p> <p><strong>Repositories</strong></p> <p>A Libraries.io repository represents a publically accessible source code repository from either github.com, gitlab.com or bitbucket.org. Repositories are distinct from Projects, they are not distributed via a package manager and typically an application for end users rather than component to build upon.</p> <p><strong>Repository dependencies</strong></p> <p>A repository dependency is a dependency upon a Version from a package manager has been specified in a manifest file, either as a manually added dependency committed by a user or listed as a generated dependency listed in a lockfile that has been automatically generated by a package manager and committed.</p> <p><strong>Projects with related Repository fields</strong></p> <p>This is an alternative projects export that denormalizes a projects related source code repository inline to reduce the need to join between two data sets.</p> <p><strong>Licence</strong></p> <p>This dataset is released under the Creative Commons Attribution-ShareAlike 4.0 International Licence.</p> <p>This licence provides the user with the freedom to use, adapt and redistribute this data. In return the user must publish any derivative work under a similarly open licence, attributing Libraries.io as a data source. The full text of the licence is included in the data.</p> <p><strong>Access, Attribution and Citation</strong></p> <p>The dataset is available to download from Zenodo at https://zenodo.org/record/2536573.</p> <p>Please attribute Libraries.io as a data source by including the words ‘Includes data from Libraries.io, a project from Tidelift’ and reference the Digital Object identifier: 10.5281/zenodo.3626071</p>
GitHub developer behavior and repository evolution dataset
<p>In this work, based on GitHub Archive project and repository mining tools, we process all available data into concise and structured format to generate GitHub developer behavior and repository evolution dataset. With the self-configurable interactive analysis tool provided by us, it will give us a macroscopic view of open source ecosystem evolution.</p>
Data showcase papers published in the Mining Software Repositories (MSR) conference (v2.2)
<p>Data regarding data showcase papers published in the Mining Software Repositories (MSR) conference.</p> <p>The following data files are included.</p> <ul> <li>citing_dp_dois_citations.txt: Strong and weak citations of (strong and weak) citation papers</li> <li>data_paper_clustering.csv: The clustering process of MSR data papers</li> <li>data_paper_clusters.csv: Clusters of MSR data papers</li> <li>data_papers.bib: Bibliographic details of MSR data papers, along with their assigned clusters (field 'cluster') and strong citations (field 'usedby')</li> <li>dp_dois_citations.txt: Strong and weak citations of MSR data papers</li> <li>msr-all: Bibliographic details of all MSR (data and non-data) papers</li> <li>ndp_dois_citations.txt: Strong and weak citations of MSR non-data papers</li> <li>ndp_rand_dois_citations.txt: Strong and weak citations of a randomly chosen MSR non-data paper weighted sample</li> <li>self-citations.txt: Strong citations of MSR data papers by their authors</li> <li>strong_citation_classification.csv: The classification process of strong citation papers according to the SWEBOK knowledge areas</li> <li>strong_citation_fields.csv: SWEBOK knowledge areas of strong citation papers</li> <li>strong_citations.bib: Bibliographic details of strong citation papers</li> <li>survey_questionnaire.pdf: The final survey questionnaire</li> <li>survey_responses.csv: Anonymized responses of the final survey questionnaire (Email addresses have been excluded for privacy reasons.)</li> <li>weak_citations_notes.bib: Weak citations of MSR data papers and the use they make</li> </ul>
Bias Away - Background sequence repositories
<p>This repository contains all Background Sequences available for the following modules of our <a href="https://biasaway.uio.no/">BiasAway</a> web interface:</p> <ul> <li>Mononucleotide distribution matched (subcommand g in the command-line tool).</li> <li>Mononucleotide distribution within a sliding window (subcommand c in the command-line tool).</li> </ul> <p>For every organism, we provide background sequences with the following lengths: 100bp, 250bp, 500bp, 750bp and 1000bp. The genome assemblies used were:</p> <ul> <li>Homo sapiens: hg38.</li> <li>Mus musculus: mm10.</li> <li>Rattus norvegicus: Rnor_6.0.</li> <li>Arabidopsis thaliana: TAIR10.</li> <li>Danio rerio: GRCz11.</li> <li>Drosophila melanogaster: dm6.</li> <li>Caenorhabditis elegans: WBcel235.</li> <li>Saccharomyces cerevisiae: R64-1-1.</li> <li>Schizosaccharomyces pombe: ASM294v2.</li> </ul> <p>The code and necessary documentation to generate them can be found at our <a href="https://bitbucket.org/CBGR/biasaway_background_construction/src/master/">BitBucket repository</a>.</p> <p>The documentation for the BiasAway tool can be found at <a href="https://biasaway.readthedocs.io/en/latest/">readthedocs</a>.</p>
Data repository
<p>Data repository of the bachelor's thesis <em>T</em><em>he effect of drought on the aboveground biomass of plant functional groups</em></p>
A calibrated groundwater model (Modflow-NWT) data repository in the Koga Irrigation Project area, Ethiopia
<p>The repository includes the research data pertaining to the Modflow-NWT based groundwater model developed for the Koga irrigation project area, Ethiopia. The database constitutes three archived data folders namely, 1. MainData (mostly excel files which include model forcings, data used in model calibration, citizen science data, etc.), 2. GIS (mostly geospatial files to assist readers with the spatial locations of the irrigation project structures, as well as the important data and administrative locations), 3. ModelFiles (mostly text files which include model inputs and outputs).</p> <p>The data has been used in preparation of the manuscript titled, "A numerical framework to advance agricultural water management under hydrological stress conditions in a data scarce environment", published in the Agricultural Water Management journal (<a href="http://dx.doi.org/10.1016/j.agwat.2021.106947">10.1016/j.agwat.2021.106947</a>). Readers are requested to go through this article to find more details on the data. The model simulations ranged from 1st January 2008 to 15th August 2019.</p> <p> </p>
Implementing Traceability Repositories as Graph Databases for Software Quality Improvement: Datasets used to test our methodology that is presented in the paper 10.1109/QRS.2018.00040
<p>The first dataset is the Event Based Traceability for Managing Evolutionary Change (EBT), it is a public dataset provided by CoEST, the original artifacts and trace links are represented in XML and text format. From the EBT dataset, we selected the 41<em> requirements </em>and 25<em> test case </em>artifacts, in addition to the answer set of 51 trace links which relates the <em>requirements </em>with the<em> test case. </em>Artifacts and trace links are prepared in XML format<strong>. </strong>The data set contains XML for each artifact such as RQ.xml, EBTrelations.xml is the answer set file, TradModel.xml which describes the defined model and TradTraceabilityRule.xml that includes the rules applied for trace link types.</p> <p> </p> <p>The second dataset AgileOERP is collected from commercial management tool to customize an open source ERP applying agile methodology. It contains 350<em> user stories (US), </em>1323<em> tasks (TS) </em>and 198<em> developer test (DT) </em>artifacts, in addition to answer set of trace links that manually generated by developers which relates the <em>user story </em>artifact with<em> task (</em>1304) artifact, as such relates the <em>task </em>artifact with<em> developer test </em>artifact (65). Artifacts and trace links are prepared in XML format<strong>. </strong>The data set contains XML for each artifact such as US.xml, ERPrelations.xml is the answer set file, AgileModel.xml which describes the defined model and AgileTraceabilityRule,xml that includes all rules applied for trace links type</p> <p> </p> <p>The original dataset of the last dataset is the Aqualush irrigation system which is used as a case study in “C. Fox, Introduction to Software Engineering Design: Processes, Principles and Patterns with UML2. Addison-Wesley, 2006”. The trace links are generated and provided in “E. Ben Charrada, D. Caspar, C. Jeanneret, and M. Glinz, towards a benchmark for traceability, in Joint EVOL and IWPSE 2011, pp. 21-30”, in HTML format. For our work, we selected the <em>software requirements specification (</em>396 SRS), <em>user level requirements (</em>48 ULR), <em>use case (</em>74 UC), <em>detailed design (85 DD) </em>and <em>software architecture(15 SArch) </em>artifacts in addition to the answer set of trace links that relate the SRS with other artifacts(4038) and thus relates the DD artifact with other artifacts (1719) . Artifacts and trace links are prepared in XML<strong>. </strong>The data set contains XML for each artifact such as SRS.xml, AqualushRelations.xml is the answer set file, TradModel.xml which describes the defined model and TradTraceabilityRule.xml that includes the rules applied for trace link types.</p>
Qualisign: Software Metrics and GoF Design Patterns of the Maven Central Repository
<p>This dataset contains software metric and design pattern data for around 100,000 projects from the Maven Central repository. The data was collected and analyzed as part of my master's thesis "Mining Software Repositories for the Effects of Design Patterns on Software Quality" (https://www.overleaf.com/read/vnfhydqxmpvx, https://zenodo.org/record/4048275).</p> <p>The included qualisign.* files all contain the same data in different formats:<br> - qualisign.sql: standard SQL format (exported using "pg_dump --inserts ..."),<br> - qualisign.psql: PostgreSQL plain format (exported using "pg_dump -Fp ..."),<br> - qualisign.csql: PostgreSQL custom format (exported using "pg_dump -Fc ...").</p> <p>create-tables.sql has to be executed before importing one of the qualisign.* files. Once qualisign.*sql has been imported, create-views.sql can be executed to preprocess the data, thereby creating materialized views that are more appropriate for data analysis purposes.</p> <p>---</p> <p>Software metrics were calculated using CKJM extended:<br> http://gromit.iiar.pwr.wroc.pl/p_inf/ckjm/</p> <p>Included software metrics are (21 total):<br> - AMC: Average Method Complexity<br> - CA: Afferent Coupling<br> - CAM: Cohesion Among Methods<br> - CBM: Coupling Between Methods<br> - CBO: Coupling Between Objects<br> - CC: Cyclomatic Complexity<br> - CE: Efferent Coupling<br> - DAM: Data Access Metric<br> - DIT: Depth of Inheritance Tree<br> - IC: Inheritance Coupling<br> - LCOM: Lack of Cohesion of Methods (Chidamber and Kemerer)<br> - LCOM3: Lack of Cohesion of Methods (Constantine and Graham)<br> - LOC: Lines of Code<br> - MFA: Measure of Functional Abstraction<br> - MOA: Measure of Aggregation<br> - NOC: Number of Children<br> - NOM: Number of Methods<br> - NOP: Number of Polymorphic Methods<br> - NPM: Number of Public Methods<br> - RFC: Response for Class<br> - WMC: Weighted Methods per Class</p> <p>In the qualisign.* data, these metrics are only available on the class level. create-views.sql additionally provides averages of these metrics on the package and project levels.</p> <p>---</p> <p>Design patterns were detected using SSA:<br> https://users.encs.concordia.ca/~nikolaos/pattern_detection.html</p> <p>Included design patterns are (15 total):<br> - Adapter<br> - Bridge<br> - Chain of Responsibility<br> - Command<br> - Composite<br> - Decorator<br> - Factory Method<br> - Observer<br> - Prototype<br> - Proxy<br> - Singleton<br> - State<br> - Strategy<br> - Template Method<br> - Visitor</p> <p>---</p> <p>The code to generate the dataset is available at:<br> https://github.com/jaichberg/qualisign</p> <p>The code to perform quality analysis on the dataset is available at:<br> https://github.com/jaichberg/qualisign-analysis</p>
Experimental Repository for "Cutting to the Core of Pseudo-Boolean Optimization: Combining Core-Guided Search with Cutting Planes Reasoning"
<p>Code and Data supplement to "Cutting to the Core of Pseudo-Boolean Optimization: Combining Core-Guided Search with Cutting Planes Reasoning"</p>
Mining Software Repositories for the Characterization of Continuous Integration and Delivery
<p>Continuous Integration (CI) and Delivery (CD) are software development practices increasingly adopted in industry, which aiming at ensuring quality and stability, and making the whole process more efficient and less error-prone. Software engineers that incorporate these practices may have difficulties trying to get an overview of the impact of adopting them and tracking progress. Therefore, performing analysis regarding the level of the application of CI/CD practices in a software repository can help to understand how such practices are adopted, which can result in a better characterization of their benefits and limitations. To support this analysis, we developed Garimpeiro, a web application that supports the characterization of open source repositories hosted on GitHub regarding the level of adoption of CI/CD practices. This video describes the tool and demonstrates how it can be used.<br> </p>
100 NPM Repositories used for Anomalicious Commit Detection Experiment
<p>100 NPM Repositories used to evaluate positive rates of the Anomalicious Commit Detector</p>
Institutional Repositories in Kenya
<p>This dataset, collected in August 2020, lists institutional repositories belonging to institutions of higher learning accredited by the Commission for University Education in Kenya by June 2020.</p>
ABOME: A Multi-platform Data Repository of Artificially Boosted Online Media Entities
<p><strong>Motivation</strong></p> <p>The rise of online media has enabled users to choose various unethical and artificial ways of gaining social growth to boost their credibility (number of followers/retweets/views/likes/subscriptions) within a short time period. In this work, we present ABOME, a novel data repository consisting of datasets collected from multiple platforms for the analysis of blackmarket-driven collusive activities, which are prevalent but often unnoticed in online media. ABOME contains data related to tweets and users on Twitter, YouTube videos, YouTube channels. We believe ABOME is a unique data repository that one can leverage to identify and analyze blackmarket based temporal fraudulent activities in online media as well as the network dynamics.</p> <p><strong>License</strong></p> <p>Creative Commons License.</p> <p><strong>Description of the dataset</strong></p> <p>In this work, we focused on collecting data from credit-based freemium services. We divide the datasets into two parts:</p> <p><strong>- Historical Data (</strong><strong>historical_anon.zip</strong><strong>)</strong></p> <p>This consists of all the data for Twitter and YouTube from blackmarket services gathered via sequential querying of the website’s URLs between the period March-June, 2019. We collected the metadata of each entity present in the historical data.</p> <p><strong>Twitter:</strong></p> <p>We collected the following fields for retweets and followers on Twitter:</p> <p><code>user_details</code>: A JSON object representing a Twitter user.</p> <p><code>tweet_details</code>: A JSON object representing a tweet.</p> <p><code>tweet_retweets</code>: A JSON list of tweet objects representing the most recent 100 retweets of a given tweet.</p> <ol> <li> <p><a href="https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/user-object">https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/user-object</a><a href="#fnref1">↩︎</a></p> </li> <li> <p><a href="https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object">https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object</a><a href="#fnref2">↩︎</a></p> </li> </ol> <p><strong>YouTube:</strong></p> <p>We collected the following fields for YouTube likes and comments:</p> <p><code>is_family_friendly:</code> Whether the video is marked as family friendly or not.</p> <p><code>genre:</code> Genre of the video.</p> <p><code>duration:</code> Duration of the video in ISO 8601 format (duration type). This format is generally used when the duration denotes the amount of intervening time in a time interval.</p> <p><code>description:</code> Description of the video.</p> <p><code>upload_date:</code> Date that the video was uploaded.</p> <p><code>is_paid:</code> Whether the video is paid or not.</p> <p><code>is_unlisted:</code> The privacy status of the video, i.e., whether the video is unlisted or not. Here, the flag <em>unlisted</em> indicates that the video can only be accessed by people who have a direct link to it.</p> <p><code>statistics:</code> A JSON object containing the number of dislikes, views and likes for the video.</p> <p><code>comments:</code> A list of comments for the video. Each element in the list is a JSON object of the text (<em>the comment text</em>) and time (<em>the time when the comment was posted</em>).</p> <p>We collected the following fields for YouTube channels:</p> <p><code>channel_description:</code> Description of the channel.</p> <p><code>hidden_subscriber_count:</code> Total number of hidden subscribers of the channel.</p> <p><code>published_at:</code> Time when the channel was created. The time is specified in ISO 8601 format (YYYY-MM-DDThh:mm:ss.sZ).</p> <p><code>video_count:</code> Total number of videos uploaded to the channel.</p> <p><code>subscriber_count:</code> Total number of subscribers of the channel.</p> <p><code>view_count:</code> The number of times the channel has been viewed.</p> <p><code>kind:</code> The API resource type (e.g., <em>youtube#channel</em> for YouTube channels).</p> <p><code>country:</code> The country the channel is associated with.</p> <p><code>comment_count:</code> Total number of comments the channel has received.</p> <p><code>etag:</code> The ETag of the channel which is an HTTP header used for web browser cache validation.</p> <p>The historical data is stored in five directories named according to the type of data inside it. Each directory contains JSON files corresponding to the data described above. <strong>'historical_sample.zip'</strong> contains a small sample of the historical dataset.</p> <p>- <strong>Time-series Data (time_series_anon.zip)</strong></p> <p>This consists of time-series data (collected every 8 hours) of Twitter users and tweets collected from the blackmarket services between the period of March-June, 2019. We collect the following time-series data for retweets and followers on Twitter:</p> <p><code>user_timeline</code>: This is a JSON list of tweet objects in the user’s timeline, which consists of the tweets posted, retweeted and quoted by the user. The file created at each time interval contains the new tweets posted by the user during each time interval.</p> <p><code>user_followers</code>: This is a JSON file containing the user ids of all the followers of a user that were added or removed from the follower list during each time interval.</p> <p><code>user_followees</code>: This is a JSON file consisting of the user ids of all the users followed by a user, i.e., the followees of a user, that were added or removed from the followee list during each time interval.</p> <p><code>tweet_details</code>: This is a JSON object representing a given tweet, collected after every time interval.</p> <p><code>tweet_retweets</code>: This is a JSON list of tweet objects representing the most recent 100 retweets of a given tweet, collected after every time interval.</p> <p>The time-series data is stored in directories named according to the timestamp of the collection time. Each directory contains sub-directories corresponding to the data described above. <strong>'time_series_sample.zip'</strong> contains a small sample of the time series dataset.</p> <p><strong>Data Anonymization</strong></p> <p>The data is anonymized by removing all Personally Identifiable Information (PII) and generating pseud-IDs corresponding to the original IDs. A consistent mapping between the original and pseudo-IDs is maintained to maintain the integrity of the data.</p> <p> </p>
The Breakout Moment for open repositories is now - How can we build the best future for our users?
<p>Closing keynote held at the 14th International Conference on Open Repositories, OR2019 in Hamburg.</p> <p>Chair: Torsten Reimer, British Library</p>
The OBO Foundry Ontology Repository (Snapshot December 2016)
<p>The datasets contains an snapshot of the OBO ontology repository (http://www.obofoundry.org/) from December 2016.</p> <p>The dataset contains:</p> <ol> <li>A directory with the ontologies with all downloadable files (files) </li> <li>A directory with the ontologies that were not parsable with the OWL API (broken)</li> <li>A description of the ontology gathering (README.txt)</li> <li>Metadata of the OWL API 4.2.8. parsable ontologies (obo2016.12.csv)</li> <li>Metadata of the OWL API 4.2.8. NON-parsable ontologies (failed_obo2016.12.csv)</li> </ol>
A list of eletronic scientific repositories
<p>This is a list of scientific repositories in the internet.</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.