51 research outputs found
Clinical research in renal transplantation: a bibliometric perspective on a half-century of innovation and progress
No full textA dataset of GitHub Actions workflow histories
No full text<p>This replication package accompagnies the dataset and exploratory empirical analysis reported in the paper "A dataset of GitHub Actions workflow histories" published in the IEEE MSR 2024 conference. </p>
<p>The Jupyter notebook Exploratory_Analysis.ipynb requires Python 3.11.6 and some dependencies that are listed in the notebook and in the file "requirements_notebook.txt".</p>
<p>"workflows.tar.gz" contains the dataset of GitHub Actions workflow file histories.</p>
<p>"repositories.csv.gz" contains metadata about the GitHub repositories containing the workflow files. This metadata was extracted using the SEART GitHub Search tool. </p>
<p>"workflows.csv.gz" contains the metadata for the extracted workflow files. The metadata is separated in different columns:</p>
<ol>
<li>`repository`: The repository (author and repository name) from which the workflow was extracted. The separator "---" allows to distinguish between the author and the repository name.</li>
<li>`commit_hash`: The commit hash returned by git</li>
<li>`author_name`: The name of the author that changed this file</li>
<li>`author_email`: The email of the author that changed this file</li>
<li>`committer_name`: The name of the committer</li>
<li>`committer_email`: The email of the committer</li>
<li>`committed_date`: The committed date of the commit</li>
<li>`authored_date`: The authored date of the commit</li>
<li>`file_path`: The path to this file in the repository</li>
<li>`previous_file_path`: The path to this file before it has been touched</li>
<li>`file_hash`: The name of the related workflow file in the dataset</li>
<li>`previous_file_hash: The name of the related workflow file in the dataset, before it has been touched</li>
<li>`change_type`: A single letter (A,D or M) representing the type of change made to the workflow (Added, Deleted or Modified)</li>
</ol>
<p>"auxiliaries.csv.gz" is a similar file for auxiliary files.</p>
A dataset of GitHub Actions workflow histories
No full text<p>This replication package accompagnies the dataset and exploratory empirical analysis reported in the paper "A dataset of GitHub Actions workflow histories" published in the IEEE MSR 2024 conference. (The Jupyter notebook can be found in previous version of this dataset).</p>
<p>The dataset was created as follow : </p>
<ol>
<li>First, we used GitHub SEART to get a list of every non-fork repositories created before January 1st, 2023. having at least 300 commits and at least 100 stars where at least one commit was made after January 1st, 2023. (The goal of these filter is to exclude experimental and personnal repositories).</li>
<li>We checked if a folder <code>.github/workflows</code> existed. We filtered out those that did not contained this folder.</li>
<li>We applied the tool <code>gigawork</code> (version 1.3.0) to extract every files from this folder. The exact command used is <code>python scripts/batch.py -d /ourDataFolder/repositories/ -e /ourDataFolder/errors -o /ourDataFolder/output -- -w /ourDataFolder/workflows</code>. (The script <code>batch.py</code> can be found <a href="https://github.com/cardoeng/gigawork/blob/master/scripts/batch.py" target="_blank" rel="noopener">on GitHub</a>).</li>
</ol>
<p>Using the extracted data, the following files were created :</p>
<ol>
<li><code>workflows.tar.gz</code> contains the dataset of GitHub Actions workflow file histories.</li>
<li><code>repositories.csv.gz</code> contains metadata about the GitHub repositories containing the workflow files. These metadata were extracted using the SEART Search tool. </li>
<li><code>workflows.csv.gz</code> contains the metadata for the extracted workflow files.</li>
<li><code>auxiliaries.csv.gz</code> is a similar file for auxiliary files.</li>
</ol>
<p>The metadata is separated in different columns:</p>
<ol>
<li><code>repository</code>: The repository (author and repository name) from which the workflow was extracted. The separator "---" allows to distinguish between the author and the repository name</li>
<li><code>commit_hash</code>: The commit hash returned by git</li>
<li><code>author_name</code>: The name of the author that changed this file</li>
<li><code>author_email</code>: The email of the author that changed this file</li>
<li><code>committer_name</code>: The name of the committer</li>
<li><code>committer_email</code>: The email of the committer</li>
<li><code>committed_date</code>: The committed date of the commit</li>
<li><code>authored_date</code>: The authored date of the commit</li>
<li><code>file_path</code>: The path to this file in the repository</li>
<li><code>previous_file_path</code>: The path to this file before it has been touched</li>
<li><code>file_hash</code>: The name of the related workflow file in the dataset</li>
<li><code>previous_file_hash</code>: The name of the related workflow file in the dataset, before it has been touched</li>
<li><code>change_type</code>: A single letter (A,D or M) representing the type of change made to the workflow (Added, Deleted or Modified)</li>
<li><code>valid_yaml</code>: A boolean indicating if the file is a valid YAML file.</li>
<li><code>probably_workflow</code>: A boolean representing if the file contains the YAML key <code>on</code> and <code>jobs</code>. (Note that it can still be an invalid YAML file).</li>
<li><code>valid_workflow</code>: A boolean indicating if the file respect the syntax of GitHub Actions workflow. A freely available JSON Schema (used by gigawork) was used in this goal.</li>
</ol>
<p>Both <code>repositories.csv.gz</code> and <code>auxiliaries.csv.gz</code> are following this format.</p>
A dataset of GitHub Actions workflow histories
No full text<p>This replication package accompagnies the dataset and exploratory empirical analysis reported in the paper "A dataset of GitHub Actions workflow histories" published in the IEEE MSR 2024 conference. (The Jupyter notebook can be found in previous version of this dataset).</p>
<p><em><strong>Important notice :</strong> It looks like Zenodo is compressing gzipped files two times without notice, they are "double compressed". So, when you download them they should be named : <code>x.gz.gz</code> instead of <code>x.gz</code>. Notice that the provided MD5 refers to the original file. </em></p>
<p>The dataset was created as follow : </p>
<ol>
<li>First, we used GitHub SEART (on October 11th, 2023) to get a list of every non-fork repositories created before January 1st, 2023. having at least 300 commits and at least 100 stars where at least one commit was made after January 1st, 2023. (The goal of these filter is to exclude experimental and personnal repositories).</li>
<li>We checked if a folder <code>.github/workflows</code> existed. We filtered out those that did not contained this folder and pulled the others (between 11th and 13th<br>of October 2023).</li>
<li>We applied the tool <code>gigawork</code> (version 1.4.0-pre) to extract every files from this folder. The exact command used is <code>python scripts/batch.py -d /ourDataFolder/repositories/ -e /ourDataFolder/errors -o /ourDataFolder/output -- -w /ourDataFolder/workflows</code>. (The script <code>batch.py</code> can be found <a href="https://github.com/cardoeng/gigawork/blob/master/scripts/batch.py" target="_blank" rel="noopener">on GitHub</a>).</li>
<li>We concatenated every files in <code>/ourDataFolder/output</code> into a csv (using <code>cat headers.csv output/*.csv > workflows_auxiliaries.csv</code> in <code>/ourDataFolder</code>) and compressed it.</li>
<li>Finally, we archived with pigz the folder <code>/ourDataFolder/workflows</code> (<code>tar -c --use-compress-program=pigz -f workflows_auxiliaries.tar.gz /ourDataFolder/workflows</code>)</li>
</ol>
<p>Using the extracted data, the following files were created :</p>
<ol>
<li><code>workflows.tar.gz</code> contains the dataset of GitHub Actions workflow file histories.</li>
<li><code>workflows_auxiliaries.tar.gz</code> is a similar file containing also auxiliary files.</li>
<li><code>workflows.csv.gz</code> contains the metadata for the extracted workflow files.</li>
<li><code>workflows_auxiliaries.csv.gz</code> is a similar file containing also metadata for auxiliary files.</li>
<li><code>repositories.csv.gz</code> contains metadata about the GitHub repositories containing the workflow files. These metadata were extracted using the SEART Search tool. </li>
</ol>
<p>The metadata is separated in different columns:</p>
<ol>
<li><code>repository</code>: The repository (author and repository name) from which the workflow was extracted. The separator "/" allows to distinguish between the author and the repository name</li>
<li><code>commit_hash</code>: The commit hash returned by git</li>
<li><code>author_name</code>: The name of the author that changed this file</li>
<li><code>author_email</code>: The email of the author that changed this file</li>
<li><code>committer_name</code>: The name of the committer</li>
<li><code>committer_email</code>: The email of the committer</li>
<li><code>committed_date</code>: The committed date of the commit</li>
<li><code>authored_date</code>: The authored date of the commit</li>
<li><code>file_path</code>: The path to this file in the repository</li>
<li><code>previous_file_path</code>: The path to this file before it has been touched</li>
<li><code>file_hash</code>: The name of the related workflow file in the dataset</li>
<li><code>previous_file_hash</code>: The name of the related workflow file in the dataset, before it has been touched</li>
<li><code>git_change_type</code>: A single letter (A,D, M or R) representing the type of change made to the workflow (Added, Deleted, Modified or Renamed). This letter is given by <code>gitpython</code> and provided as is. </li>
<li><code>valid_yaml</code>: A boolean indicating if the file is a valid YAML file.</li>
<li><code>probably_workflow</code>: A boolean representing if the file contains the YAML key <code>on</code> and <code>jobs</code>. (Note that it can still be an invalid YAML file).</li>
<li><code>valid_workflow</code>: A boolean indicating if the file respect the syntax of GitHub Actions workflow. A freely available JSON Schema (used by gigawork) was used in this goal.</li>
<li><code>uid</code>: Unique identifier for a given file surviving modifications and renames. It is generated on the addition of the file and stays the same until the file is deleted. Renamings does not change the identifier.</li>
</ol>
<p>Both <code>workflows.csv.gz</code> and <code>workflows_auxiliaries.csv.gz</code> are following this format.</p>
Lurasidone hydrochloride
No full textIn the crystal structure of the title compound, C28H37N4O2S+&#183;Cl&#8722; [systematic name: 4-(1,2-benzothiazol-3-yl)-1-({2-[(3,5-dioxo-4-azatricyclo[5.2.1.02,6]decan-4-yl)methyl]cyclohexyl}methyl)piperazin-1-ium chloride], the anions and cations are linked by N&#8212;H...Cl hydrogen bonds. The crystal structure is further stabilized by C&#8212;H...&#960; and C&#8212;H...O interactions
On the rise and fall of CI services in GitHub
No full textThe repository contains the replication package of the study on CI landscape in GitHub.
the file "requirements.txt" contains a list of dependencies needed to execute the scripts, and can be installed as normal using "pip install -r requirements.txt". Before using the notebooks, it is strongly recommended that you create an isolated environment with Python 3.6. This can be done easily using 'pew' or 'virtualevn'
pip install pew pew new -p python3.6 -r requirements.txt CIstudy
or
pip install virtualenv virtualenv -p python3.6 CIstudy
The scripts can be found under the "Notebooks" directory and they must be run with 'Jupyter notebook'. Notebook "_A_Data_Cleaning" uses 2 raw datasets of "dataset_raw_700k_allCIs.csv.gz" and "all_activerepos_notfork676K.csv.gz" and generates "NPM_cs_protions_700k_7cis" dataset for "_B_Analysing CI landscape in NPM". All datasets are provided in the data directory.
We used (https://replicate.npmjs.com/_all_docs) endpoint to be able to get the list of NPM packages and their info. The info we managed to obtain from the API includes:
Name: the package name Created: time of the creation Updated: last update time recorded for the package Maintainers_name: name of the maintainer Maintainers_email: email address of the maintainer Homepage: home page Repository: repository address of the package Author: author name License: license type
Out of the 1601989 packages listed in the packages list, we managed to access and clone 676K of them. Using git commands, we extracted files from the entire history of git, then iterated over all commits, and found where CI files were added or deleted. In order to determine which repositories are forked and which are not, we used the GitHub API
2-[(E)-({3-[(E)-(2-Hydroxybenzylidene)aminomethyl]-1,4-dioxaspiro[4.5]decan-2-yl}methyl)iminomethyl]phenol
No full textIn the title compound, C24H28N2O4, the dioxalane ring has an envelope conformation. The cyclohexane ring adopts a chair conformation. The dihedral angle between the benzene rings is 72.5&#8197;(3)&#176;. The molecular conformation is stabilized by two intramolecular O&#8212;H...N hydrogen-bonding interactions with an S(6) graph-set motif. The crystal structure is stabilized by van der Waals interactions
Long noncoding RNA LINC01296 promotes tumor growth and progression by sponging miR-5095 in human cholangiocarcinoma
No full textEchingridimer A, an Oxaspiro Dimeric Sesquiterpenoid with a 6/6/5/6/6 Fused Ring System from Echinops grijsii and Aphicidal Activity Evaluation
No full textA new dimeric eudesmane sesquiterpenoid with an unreported 6/6/5/6/6
fused carbon skeleton, echingridimer A (1), and five
monomers (2–6), including two new
compounds (2 and 3), were isolated from
the roots of Echinops grijsii. Their
structures and absolute configurations were elucidated by comprehensive
spectroscopic analyses and confirmed by X-ray crystallography or electronic
circular dichroism spectra. Compound 1 represents the
first example of a 3,15′-linked Michael-type adduct of two
eudesmanes, which feature a unique 1-oxaspiro[4.5]decan moiety. The
plausible biogenetic pathway for 1–6 was briefly discussed. The oxaspiro dimer (1) not only
showed stronger aphicidal activity than closely related monomeric
compounds (2–6) and the commercial
aphicide pymetrozine but also exhibited remarkable insecticide activity
against a broad range of aphids, thus could be used as a potential
biobased insecticide to suppress aphids
Echingridimer A, an Oxaspiro Dimeric Sesquiterpenoid with a 6/6/5/6/6 Fused Ring System from Echinops grijsii and Aphicidal Activity Evaluation
No full textA new dimeric eudesmane sesquiterpenoid with an unreported 6/6/5/6/6
fused carbon skeleton, echingridimer A (1), and five
monomers (2–6), including two new
compounds (2 and 3), were isolated from
the roots of Echinops grijsii. Their
structures and absolute configurations were elucidated by comprehensive
spectroscopic analyses and confirmed by X-ray crystallography or electronic
circular dichroism spectra. Compound 1 represents the
first example of a 3,15′-linked Michael-type adduct of two
eudesmanes, which feature a unique 1-oxaspiro[4.5]decan moiety. The
plausible biogenetic pathway for 1–6 was briefly discussed. The oxaspiro dimer (1) not only
showed stronger aphicidal activity than closely related monomeric
compounds (2–6) and the commercial
aphicide pymetrozine but also exhibited remarkable insecticide activity
against a broad range of aphids, thus could be used as a potential
biobased insecticide to suppress aphids
- …
