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29 results for “Library Science”
Open access journals in the field of library and information sciences (LIS)
<p>List of foreign open access journals in the field of LIS is presented</p>
Open access practices of selected library science journals
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Regionalized Cultural Access and Participation (Books And Libraries) And Science Attitudes Variables (2013)
<p>This dataset was created from the microdata of the Eurobarometer 79.2 survey using the development version of the eurobarometer package.</p> <p>The read a book variable is a weighted sum of the responses that chose from `QB1 How many times in the last twelve months have you read a book?` any answer apart from "not in the last 12 months."</p> <p>The library access variable is a weighted sum of the responses that chose from `QB1 How many times in the last twelve months have you visited a public libarary?` any answer apart from "not in the last 12 months.</p> <p>The limited library access is a weighted sum of the responses that chose from the question block<br> `QB2 And for each of the following activities, please tell me why you haven’t done it or haven’t done it more often in the last 12 months? ... Visited a public library` the answer option `Limited or poor quality of this activity in the place where you live.` In this case, the number of respondents is rather low and this is not a very reliable statistic on regional level.</p> <p>The supports open access variable is a weighted sum of yes answer options to the `QD 17 Do you think that the results of publicly funded research should be made available online free of charge?` question.</p> <p>The internet access question is a weighted sum of responses to the answer option for `D46 Which of the following do you have? - An Internet connection at home`.</p> <p>The internet access question is a weighted sum of responses to the answer option for `D15 What is your current occupation? - student`. </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>
AMASS MOOC Lesson 7.3.: Mapping research on the social impact of the arts - what characterizes the field? (Presented by the Swedish School of Library and Information Science)
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AMASS MOOC Lesson 7.2.: Podcast - When Policy is turned in to Practice (Presented by the Swedish School of Library and Information Science)
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AMASS MOOC Lesson 7.1.: Cultural Policy in Europe (Presented by the Swedish School of Library and Information Science)
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e One Week Online Short Term Course on "Library and Information Science"
<p><strong>e One Week Online Short Term Course on “Library and Information Science” </strong></p>
Dataset for Towards Understanding Performance Bugs in Popular Data Science Libraries
<div> <div>This dataset contains 138 performance bugs in data science popular libraries, and their impacts, root causes, locating and fixing challenge, and fixing strategy.</div> <div>Our replication package consists of three main folders:RQ1&2_Impacts_and_Root_Causes, RQ3_Root_Causes_Locating_Fixing_Effort_Challenge and RQ4_Fixing_Strategy.</div> <br> <div>RQ1&2_Impacts_and_Root_Causes</div> <br> <div>In this folder we first placed the identified impact (Explicit and Implicit). Then we gave the identified symptoms and root cause taxonomy. In each file (corresponding to each iteration), we provided the repo name, issue number, and the label (symptom and root cause).</div> <br> <div>RQ3_Root_Causes_Locating_Fixing_Effort_Challenge</div> <br> <div>We provided the number of comments, lines of changed code and issue duration involved in handling performance bugs. Furthermore, the challenge in resolving these bugs in data science libraries are identified here.</div> <br> <div>RQ4_Fixing_Strategy</div> <br> <div>We provided the identified fixing strategy with small LOC. In the file, we provided the repo name, issue number, and the label (fixing strategy).</div> </div>
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.