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.
2
datasets available to search
ShareScore release 0.9.0
Dataset results
2 results for “argumentation in science”
[Dataset] Is it a match? Motivations on citizen science volunteers and recruitment arguments in project descriptions
<p>This dataset contains the necessary details to reproduce the experiments of the paper:</p> <p><em>Kai Nils, W., Gutiérrez Páez,N.F., Sabel, O. and Hämäläinen, R. (2022) “Is It a Match? Motivations on Citizen Science Volunteers and Recruitment Arguments in Project Descriptions.” In Proceedings of the ECSA2022 conference: Citizen Science for Planetary Health, 69–70. <a href="https://2022.ecsa-conference.eu/files/ecsa/Bilder/ECSA2022_Conference_Proceedings.pdf">https://2022.ecsa-conference.eu/files/ecsa/Bilder/ECSA2022_Conference_Proceedings.pdf</a></em></p> <p>Data has been collected by quantitative triangulation. 1076 participants in citizen science projects answered a survey about the 12 motivational factors for participating. They had access to the survey by social media posts or email invitations sent to people in charge of projects. Data regarding motivational arguments in recruitment come from quantitative content analysis of 367 project descriptions of the website Zooniverse. The content analysis of the project descriptions was done manually by two coders independently. Then, both coders analysed their codings and reached consensus.</p>
Replication Package: Unboxing Default Argument Breaking Changes in 1 + 2 Data Science Libraries in Python
<p><strong>Replication Package</strong></p> <p>This repository contains data and source files needed to replicate our work described in the paper "Unboxing Default Argument Breaking Changes in Scikit Learn".</p> <p><strong>Requirements</strong></p> <p>We recommend the following requirements to replicate our study:</p> <ol> <li>Internet access</li> <li>At least 100GB of space</li> <li>Docker installed</li> <li>Git installed</li> </ol> <p><strong>Package Structure</strong></p> <p>We relied on Docker containers to provide a working environment that is easier to replicate. Specifically, we configure the following containers:</p> <ul> <li><code>data-analysis</code>, an R-based Container we used to run our data analysis.</li> <li><code>data-collection</code>, a Python Container we used to collect Scikit's default arguments and detect them in client applications.</li> <li><code>database</code>, a Postgres Container we used to store clients' data, obtainer from Grotov et al.</li> <li><code>storage</code>, a directory used to store the data processed in <code>data-analysis</code> and <code>data-collection</code>. This directory is shared in both containers.</li> <li><code>docker-compose.yml</code>, the Docker file that configures all containers used in the package.</li> </ul> <p>In the remainder of this document, we describe how to set up each container properly.</p> <p><strong>Using VSCode to Setup the Package</strong></p> <p>We selected VSCode as the IDE of choice because its extensions allow us to implement our scripts directly inside the containers. In this package, we provide configuration parameters for both <code>data-analysis</code> and <code>data-collection</code> containers. This way you can directly access and run each container inside it without any specific configuration.</p> <p>You first need to set up the containers</p> <pre><code>$ cd /replication/package/folder $ docker-compose build $ docker-compose up # Wait docker creating and running all containers </code></pre> <p>Then, you can open them in Visual Studio Code:</p> <ol> <li>Open VSCode in project root folder</li> <li>Access the command palette and select "Dev Container: Reopen in Container" <ol> <li>Select either <em>Data Collection</em> or <em>Data Analysis</em>.</li> </ol> </li> <li>Start working</li> </ol> <p>If you want/need a more customized organization, the remainder of this file describes it in detail.</p> <p><strong>Longest Road: Manual Package Setup</strong></p> <p><strong>Database Setup</strong></p> <p>The database container will automatically restore the dump in <code>dump_matroskin.tar</code> in its first launch. To set up and run the container, you should:</p> <p>Build an image:</p> <pre><code>$ cd ./database $ docker build --tag 'dabc-database' . $ docker image ls REPOSITORY TAG IMAGE ID CREATED SIZE dabc-database latest b6f8af99c90d 50 minutes ago 18.5GB </code></pre> <p>Create and enter inside the container:</p> <pre><code>$ docker run -it --name dabc-database-1 dabc-database $ docker exec -it dabc-database-1 /bin/bash root# psql -U postgres -h localhost -d jupyter-notebooks jupyter-notebooks=# \dt List of relations Schema | Name | Type | Owner --------+-------------------+-------+------- public | Cell | table | root public | Code_cell | table | root public | Md_cell | table | root public | Notebook | table | root public | Notebook_features | table | root public | Notebook_metadata | table | root public | repository | table | root </code></pre> <p>If you got the tables list as above, your database is properly setup.</p> <p>It is important to mention that this database is extended from the one provided by <a href="https://markdowntohtml.com/">Grotov et al.</a>. Basically, we added three columns in the table <code>Notebook_features</code> (<code>API_functions_calls</code>, <code>defined_functions_calls</code>, and<code>other_functions_calls</code>) containing the function calls performed by each client in the database.</p> <p><strong>Data Collection Setup</strong></p> <p>This container is responsible for collecting the data to answer our research questions. It has the following structure:</p> <ul> <li><code>dabcs.py</code>, extract DABCs from Scikit Learn source code, and export them to a CSV file.</li> <li><code>dabcs-clients.py</code>, extract function calls from clients and export them to a CSV file. We rely on a modified version of <a href="https://markdowntohtml.com/">Matroskin</a> to leverage the function calls. You can find the tool's source code in the `matroskin`` directory.</li> <li><code>Makefile</code>, commands to set up and run both <code>dabcs.py</code> and <code>dabcs-clients.py</code></li> <li><code>matroskin</code>, the directory containing the modified version of matroskin tool. We extended the library to collect the function calls performed on the client notebooks of Grotov's dataset.</li> <li><code>storage</code>, a docker volume where the data-collection should save the exported data. This data will be used later in <a href="https://markdowntohtml.com/#data-analysis-setup">Data Analysis</a>.</li> <li><code>requirements.txt</code>, Python dependencies adopted in this module.</li> </ul> <p>Note that the container will automatically configure this module for you, e.g., install dependencies, configure matroskin, download scikit learn source code, etc. For this, you must run the following commands:</p> <pre><code>$ cd ./data-collection $ docker build --tag "data-collection" . $ docker run -it -d --name data-collection-1 -v $(pwd)/:/data-collection -v $(pwd)/../storage/:/data-collection/storage/ data-collection $ docker exec -it data-collection-1 /bin/bash $ ls Dockerfile Makefile config.yml dabcs-clients.py dabcs.py matroskin storage requirements.txt utils.py </code></pre> <p>If you see project files, it means the container is configured accordingly.</p> <p><strong>Data Analysis Setup</strong></p> <p>We use this container to conduct the analysis over the data produced by the <a href="https://markdowntohtml.com/#data-collection-setup">Data Collection</a> container. It has the following structure:</p> <ul> <li><code>dependencies.R</code>, an R script containing the dependencies used in our data analysis.</li> <li><code>data-analysis.Rmd</code>, the R notebook we used to perform our data analysis</li> <li><code>datasets</code>, a docker volume pointing to the <code>storage</code> directory.</li> </ul> <p>Execute the following commands to run this container:</p> <pre><code>$ cd ./data-analysis $ docker build --tag "data-analysis" . $ docker run -it -d --name data-analysis-1 -v $(pwd)/:/data-analysis -v $(pwd)/../storage/:/data-collection/datasets/ data-analysis $ docker exec -it data-analysis-1 /bin/bash $ ls data-analysis.Rmd datasets dependencies.R Dockerfile figures Makefile </code></pre> <p>If you see project files, it means the container is configured accordingly.</p> <p>A note on <code>storage</code> shared folder</p> <p>As mentioned, the <code>storage</code> folder is mounted as a volume and shared between <code>data-collection</code> and <code>data-analysis</code> containers. We compressed the content of this folder due to space constraints. Therefore, before starting working on <a href="https://markdowntohtml.com/#data-collection-setup">Data Collection</a> or <a href="https://markdowntohtml.com/#data-analysis-setup">Data Analysis</a>, make sure you extracted the compressed files. You can do this by running the <code>Makefile</code> inside <code>storage</code> folder.</p> <pre><code>$ make unzip # extract files $ ls clients-dabcs.csv clients-validation.csv dabcs.csv Makefile scikit-learn-versions.csv versions.csv $ make zip # compress files $ ls csv-files.tar.gz Makefile</code></pre>
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.