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.
9
datasets available to search
ShareScore release 0.9.0
Dataset results
9 results for “socio-technical”
Socio-Technical Approach for the Assessment of Critical Infrastructure Systems Resiliency in Extreme Weather Events
<p>Datasets generated during and/or analyzed during the Socio-Technical Approach for the Assessment of Critical Infrastructure Systems Resiliency in Extreme Weather Events.</p>
Data of socio-technical evaluation of long-term spent nuclear fuel management options at San Onofre, California
<p>Data file supporting the socio-technical evaluation of long-term spent nuclear fuel management options at San Onofre, California.</p>
Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events
<p>The data provided and the associated MATLAB code were used to build an Artificial Neural Network Model to capture the reconstruction (recovery) of various buildings subjected to tornado events in the State of Missouri. The ANN model utilizes relevant tornado, societal demographic, and structural data to determine a building's resulting damage state from an extreme wind event and the subsequent recovery time. Abstract for the publication is as follows:</p> <p>In a companion article, previously published in Royal Society Open Science, the authors used Graph Theory to evaluate artificial neural network models for potential social and building variables interactions contributing to building wind damage. The results promisingly highlighted the importance of social variables in modeling damage as opposed to the traditional approach of solely considering physical characteristics of a building. Within this update article, the same methods are used to evaluate two different artificial neural networks for modelling building repair and/or rebuild (recovery) time. In contrast to the damage models, the recovery models consider (A) primarily social variables and then (B) introduce structural variables. These two models are then evaluated using centrality and shortest path concepts of Graph Theory as well as validated against data from the 2011 Joplin Tornado. The results of this analysis do not show the same distinctions as were found in the analysis of the damage models from the companion article. The overarching lack of discernible and consistent differences in the recovery models suggests that social variables that drive damage are not necessarily contributions to recovery. The differences also serve to reinforce that machine learning methods are best used when the contributing variables are already well understood.</p>
Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events
Open the record for dataset details and reuse information.
Finding Relationships between Socio-technical Aspects and Personality Traits by Mining Developer E-mails - Dataset
<p>Building on the work done by Gonzalez-Barahona et al. [1] we used the data of the Eclipse project available at [2], with information from the following repositories: source code management (git), issue tracking (Bugzilla), mailing lists (archived<br /> in mbox format), and code review (Gerrit). From the dumps that are provided by Metrics Grimoire, the databases were restored and the datasets used in the experimental stage were built.<br /> Since we are interested in identifying relationships between social and technical aspects in the evolution of FLOSS projects, the source code repository and the mailing lists are the most relevant data for the purpose of this work. Specifically, we used the data of the Eclipse Platform subproject, which in turn is divided into the following components: Ant - Eclipse/Ant integration, Workspace (Team, CVS,<br /> Compare, Resources) - Platform resource management, Debug - Generic execution debug framework, Releng - Release Engineering, Search - Integrated search facility, SWT - Standard Widget Toolkit, Text - Text editor framework and UI - Platform user interface, runtime and help components.</p>
Figure 2 from: Haak L, Greene S, Ratan K (2020) A New Research Economy: Socio-technical framework to open up lines of credit in the academic community. Research Ideas and Outcomes 6: e60477. https://doi.org/10.3897/rio.6.e60477
Figure 2 Collaborative Workflow of an Incremental Dataset in a Consortium Setting (click to view enlarged slide in Present mode). The workflow of an investigator's incremental dataset is shown as it is incorporated into the Facilitated Living Review (FLR), moving along a continuum from closed to open review. (1) After ideation and hypothesis formation by the team, early experimentation creates an incremental dataset. (2) The dataset is shared and discussed with the Project X research consortium, then iterated. (3) The v.3 dataset is shared with the consortium for further analysis, positioning, and citation in the context of the latest published evidence in the FLR. (4) The FLR with its cited, organically peer reviewed dataset is posted on a preprint server under the authorship of the FLR consortium. (5) Feedback from the broader community may lead to further iteration of the dataset at the discretion of the project team in the next round of the FLR revisions.
A Mixed Graph-Relational Dataset of Socio-technical Interactions in Open Source Systems
<p># Realtional Dataset</p> <p>- Downlaod Dump.sql <br> - Before importing Dump.sql icrease your max_allowed_packet=4M to 16M in your MySQL initialization usually located at C:\ProgramData\MySQL\MySQL Server 8.0\my.ini <br> - Import Dump.sql to your local MySQL server by MySQL Workbech->Server->Data Import -> Import from Self-Contained File -> Start Import<br> <br> ---------------------------------------------------------------------------------------------------------------------------------------<br> # Graph Dataset via graphs_cypher</p> <p># Download Neo4j Desktop</p> <p>Download link can be found here https://neo4j.com/</p> <p># Add Graph in Neo4j Desktop</p> <p>Run Neo4j Desktop application and follow instructions:</p> <p>- Click on Add Graph<br> - Set desired name and password<br> - Click on Create<br> - Start the Graph database</p> <p><br> # Loading Cypher scripts to Graph database</p> <p> </p> <p>Once the graph database is running, start Neo4j browser and execute cypher script for each project.<br> as show in the figure below<br> </p> <p>The script will insert the data of the graph in the database.</p> <p>OR</p> <p># Loading from graph.db</p> <p>- Download the graph.db <br> - graph.db contains subfolder DeveloperInteractionGraph.db and SubsystemInteractionGraph.db <br> - To visualize any desired graph from the graph.db/DeveloperInteractionGraph.db or SubsystemInteractionGraph.db <br> - Create a graph in Neo4j<br> - Click on Manage<br> - Click on Open Folder<br> - Paste the desired (project).db inside the data/databases folder<br> - Restart DB<br> - Neo4j credentials:<br> userid = neo4j<br> password = admin</p> <p>## Visualize graph</p> <p>The resultant graph can be visualized using following query:<br> Show only 25 nodes<br> ```<br> $ MATCH (n) RETURN n LIMIT 25<br> ```</p> <p>Show all nodes<br> ```<br> $ MATCH (n) RETURN n<br> ```<br> </p> <p> </p>
Figure 1 from: Haak L, Greene S, Ratan K (2020) A New Research Economy: Socio-technical framework to open up lines of credit in the academic community. Research Ideas and Outcomes 6: e60477. https://doi.org/10.3897/rio.6.e60477
Figure 1 Activities and artifacts at each stage of the research process.
Data from: A Socio-technical Perspective on Software Vulnerabilities: A Causal Analysis
<p>This data package contains supplemental material data for the under review TSE submission: A Socio-technical Perspective on Software Vulnerabilities: A Causal Analysis. The restricted access requirement will be lifted upon approval of the manuscript.</p> <p>The comprehensive explanation of this dataset can be found at: https://sailuh.github.io/causal_commit_flow_docs</p> <p>The following briefly describes the contents of the folders. The analysis presented in the manuscript requires the following:</p> <ul> <li>Git Log </li> <li>Mailing List</li> <li>Software Vulnerabilities (NVD Feed)</li> </ul> <p>This data is provided to a mining software repository tool, Kaiaulu. The data specifications and configuration parameters are defined in the OpenSSL project configuration file (.yml), also included in this package. </p> <p>An R notebook in Kaiaulu, taking the dataset above + project configuration file, can then perform the first analysis step:</p> <p>https://github.com/sailuh/kaiaulu/blob/master/vignettes/issue_social_smell_showcase.Rmd</p> <p>The file 1_openssl_social_smells_timeline.csv is generated as an output of this R Notebook, and included in the causal_model folder of this package. The following files in this folder numbered 2 through 16, describe transformation steps using Excel, Python scripts, and Tetrad (also an open source tool). These are described conceptually in the manuscript, but in more detail in the comprehensive explanation of this dataset linked at the start. </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.