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8 results for “Replicable science”
Replication data for: Consumer preference testing of boiled sweetpotato (Ipomoea batatas (L.) Lam.) using crowdsourced citizen science in Ghana and Uganda
<p>Crowdsourced citizen science is an emerging approach in plant sciences. The triadic comparison of technologies (tricot) approach has been successfully utilised by demand-led breeding programmes to identify varieties for dissemination suited to specific geographic and climatic regions. An important feature of this approach is the independent way in which farmers individually evaluate the varieties on their own farms as ‘citizen scientists’. In this study, we adapted this approach to evaluate consumer preferences to boiled sweetpotato (<em>Ipomoea batatas</em> (L.) Lam) roots of 21 advanced breeding materials and varieties in Ghana and 6 released varieties in Uganda. We were specifically interested in evaluating if a more independent style of evaluation (home tasting) would produce results comparable to an approach that involves control over preparation (centralised tasting). We compiled data from 1,433 participants who individually contributed to a home tasting (de-centralised) and a centralised tasting trial in Ghana and Uganda, evaluating overall acceptability, and indicating the reasons for their preferences. Geographic factors showed important contribution to define consumers’ preference to boiled sweetpotato genotypes. Home and centralised tasting approaches gave similar rankings for overall acceptability, which was strongly correlated to taste. In both Ghana and Uganda, it was possible to robustly identify superior sweetpotato genotypes from consumers’ perspectives. Our results indicate that the tricot approach can be successfully applied to consumer preference studies.</p>
Social Science Theories in Software Engineering Research - Replication Package
<p>Replication package for the article "Social Science Theories in Software Engineering Research".</p> <p>See README file for additional details.</p>
Replication data for: A global perspective on social stratification in science
<p><strong>Replication materials for "A global perspective on social stratification in science"</strong></p> <p>This data comprises the number of Scopus authors by OECD macro field of science, country, authors' inferred gender, author's academic age, and bibliometric class. The sum of the column counts is: 8,225,368. </p> <p>**Please note**: there are 554 rows in the table indicated with "NA" where some of these variables are missing.</p> <p><strong>Paper metadata</strong><br>**Title**: A global perspective on social stratification in science</p> <p>**Authors**: Aliakbar Akbaritabara (Max Planck Institute for Demographic Research, Rostock, Germany), Andres F. Castro Torres (Max Planck Institute for Demographic Research, Rostock, Germany; Center for Demographic Studies, Barcelona, Spain), Vincent Larivière (Université de Montréal, Montréal, Canada)</p> <p>**Corresponding author and email**: Aliakbar Akbaritabar, akbaritabar@demogr.mpg.de</p> <p>**Abstract**: <br>To study stratification among scientists, we reconstruct the career-long trajectories of 8.2 million scientists worldwide using 12 bibliometric measures of productivity, geographical mobility, collaboration, and research impact. While most previous studies examined these variables in isolation, we study their relationships using Multiple Correspondence and Cluster Analysis. We group authors according to their bibliometric performance and academic age across six macro fields of science, and analyze co-authorship networks and detect collaboration communities of different sizes. We found a stratified structure in terms of academic age and bibliometric classes, with a small top class and large middle and bottom classes in all collaboration communities. Results are robust to community detection algorithms used and do not depend on authors’ gender. These results imply that increased productivity, impact, and collaboration are driven by a relatively small group that accounts for a large share of academic outputs, i.e., the top class. Mobility indicators are the only exception with bottom classes contributing similar or larger shares. We also show that those at the top succeed by collaborating with various authors from other classes and age groups. Nevertheless, they are benefiting disproportionately from these collaborations which may have implications for persisting stratification in academia.</p> <p>**Keywords**:<br>bibliometric data; research productivity; scientific collaboration; scientific mobility; scholarly impact and citations<br><br></p> <p><strong>Description of the data</strong><br>The dataset contains SIX columns as follows:</p> <p>**base_oecd**: Six OECD macro fields of science such as "Agricultural_Sciences", "Engineering_and_Technology", "Humanities", "Medical_and_Health_Sciences", "Natural_Sciences", "Social_Sciences", and "NA" (for missing) </p> <p>**base_countr**: Country name (see details in the paper)</p> <p>**gender**: Inferred gender based on the name of the author; it includes "fem" for "female", "mal" for "male", and "unkn" for "uknown" gender who are the authors whose gender could not be determined. Please see methods section for the details.</p> <p>**basc_acadag**: Academic age grouped into age groups such as "10-14", "15-20", "2-5", "21-25", "6-9", "One", and "NA" (for missing age group)</p> <p>**cluster6**: Bibliometric class assigned to authors aggregated at the country level as reported in the paper such as "BT", "ML", "LW", "MH", "TP", "HG", and "NA" (for missing). Please see methods section in the paper for the details.</p> <p>**count**: Number of unique authors</p> <p>For queries, contact: akbaritabar@demogr.mpg.de</p>
Replication package for: Forbidden Fruits: The Political Economy of Science, Religion, and Growth
<p>The zipped folder contains all the files need to replicate the results reported in the paper “Forbidden Fruits: The Political Economy of Science, Religion, and Growth” by Roland Bénabou, Davide Ticchi, and Andrea Vindigni, published in the Review of Economic Studies.</p>
Replication materials for "The Impact of a National Research Assessment on the Publications of Sociologists in Italy" to appear in Science and Public Policy
<p>Replication materials for "The Impact of a National Research Assessment on the Publications of Sociologists in Italy" to appear in Science and Public Policy</p> <p>DOI: 10.1093/scipol/scab013</p> <p><strong>More information on data on GitHub page</strong>: https://github.com/akbaritabar/SPP-ANVUR-sociologists-2021</p> <p> </p> <p><strong>Introduction to data</strong></p> <p>Data were collected from Scopus in September 2016 and included all records published by Italian sociologists between 2006 and 2015. This period covered five full years before and after ANVUR, whose original call for participation was on 7 November 2011. By considering five years before and after the call, we aimed to trace pre-existing behaviour and examine scientists’ reactions to institutional policies.</p>
Replication Data for "Grammar-based fuzzing of data integration parsers in computational materials science"
Open the record for dataset details and reuse information.
Computer Science Students Perceptions Regarding AI Open-Source Repositories Replication Package
Open the record for dataset details and reuse information.
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.