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20 results for “publication package”
Gender Differences in Public Code Contributions: a 50-year Perspective - Replication Package
<p>This page details the steps needed to replicate the findings of the paper: <a href="https://upsilon.cc/~zack/">Stefano Zacchiroli</a>, <em>Gender Differences in Public Code Contributions: a 50-year Perspective</em>, <a href="https://www.computer.org/csdl/magazine/so">IEEE Software</a>, 2021.</p> <p>After retrieving the replication package, follow the instruction described in the README.html file.</p>
Reproduction package for the publication 'Galaxy cluster photons alter the ionisation state of the nearby warm-hot intergalactic medium'
<p>The following files can be used to reproduce the figures and data from the paper <strong>Galaxy cluster photons alter the ionisation state of the nearby warm-hot intergalactic medium</strong><strong> </strong>by L. Štofanová, A. Simionescu, N. A. Wijers, J. Schaye, and J. Kaastra to be accepted in Monthly Notices of the Royal Astronomical Society (MNRAS).</p>
Datasets associated with the publication of the "satuRn" R package
<p>On this Zenodo link, we share the data that is required to reproduce all the analyses from our publication "satuRn: Scalable Analysis of differential Transcript Usage for bulk and single-cell RNA-sequencing applications".</p> <p>This repository includes input transcript-level expression matrices and metadata for all datasets, as well as intermediate results and final outputs of the respective DTU analyses. For a more elaborate description of the data, we refer to the companion GitHub for our publications; https://github.com/statOmics/satuRnPaper. Note that this is version 1.0.3 of the data (uploaded on 2022-07-08). If any changes were to be made to the datasets in the future, this will also be communicated on our companion GitHub page. </p>
Dataset to the publication "A unique signal sequence of the chemokine receptor CCR7 promotes package into COPII vesicles for efficient receptor trafficking"
<p>This repository accompanies the paper:</p> <p>"A unique signal sequence of the chemokine receptor CCR7 promotes package into COPII vesicles for efficient receptor trafficking".</p> <p>Organized into 14 folders it provides the data allowing the replication of all analyses and the manuscript's figures. For a more convenient download files were zipped.</p> <p>Upon publication of results using this dataset, please cite the following paper:</p> <p>Uetz-von Allmen E, Rippl AV, Farhan H, Legler DF. 2018. A unique signal sequence of the chemokine receptor CCR7 promotes package into COPII vesicles for efficient receptor trafficking. J Leukoc Biol 104(2):375-389.</p>
Reproduction package for the publication 'New radiative loss curve from updates to collisional excitation in the low-density, optically thin plasmas in SPEX'
<p>The following files can be used to reproduce the Figures and data from the paper <strong>New radiative loss curve from updates to collisional excitation in the low-density, optically thin plasmas in SPEX </strong>by L. Štofanová, J. Kaastra, M. Mehdipour, and J. de Plaa accepted to be publish in Section 12. Atomic, molecular, and nuclear data of Astronomy and Astrophysics (acceptance date - 27/06/2021).</p> <p> </p> <p>Note: version 2 is the most updated version (change in Fig.7).</p>
Reproduction package for the publication 'Prospects for detecting the circum- and intergalactic medium in X-ray absorption using the extended intracluster medium as a backlight'
<p>The uploaded files can be used to reproduce the dataset and figures in the paper<strong> Prospects for detecting the circum- and intergalactic medium in X-ray absorption using the extended intracluster medium as a backlight</strong> by Lýdia Štofanová, Aurora Simionescu, Nastasha A. Wijers, Joop Schaye, Jelle Kaastra, Yannick M. Bahé, and Andrés Arámburo-García.</p><p>NOTE: Files will be published with a new version. </p>
Replication package: Dataset and stata-do-file for analysis in "Intragroup communication in social dilemmas: An artefactual public good field experiment in small-scale communities"
<p>This dataset was used for the analysis in "Intragroup communication in social dilemmas: An artefactual public good field experiment in small-scale communities". The data was collected in Namibia in 2017 as part of the SASSCAL research project by Nils Christian Hoenow and Adrian Pourviseh as members of the Chair for Development and Cooperative Economics at the University of Marburg. Funded by the Southern African Science Service Center for Climate Change and Adaptive Land-UseManagement (SASSCAL) through the German Federal Ministry for Education and Research (Grant No. 01LG1201B).</p> <p> </p> <p>Article Title: Intragroup communication in social dilemmas: An artefactual public good field experiment in small-scale communities </p> <p>Authors: Nils Christian Hoenow* and Adrian Pourviseh**</p> <p> </p> <p>*RWI – Leibniz Institute for Economic Research, Essen, Germany and & School of Business and Economics, University of<br>Marburg, Marburg, Germany</p> <p>**School of Business and Economics, University of<br>Marburg, Marburg, Germany</p> <p>Abstract: <br>Communication is well-known to increase cooperation rates in social dilemma situations, but the exact mechanisms behind this remain largely unclear. This study examines the impact of communication on public good provisioning in an artefactual field experiment conducted with 216 villagers from small, rural communities in northern Namibia. In line with previous experimental findings, we observe a strong increase in cooperation when face-to-face communication is allowed before decision-making. We additionally introduce a condition in which participants cannot discuss the dilemma but talk to their group members about an unrelated topic prior to learning about the<br>public good game. It turns out that this condition already leads to higher cooperation rates, albeit not as high as in the condition in which discussions about the social dilemma are possible. The setting in small communities also allows investigating the effects of pre-existing social relationships between group members and their interaction with communication.We find that both types of communication are primarily effective among socially more distant group members, which suggests that communication and social ties work as substitutes in increasing cooperation. Further analyses rule out better comprehension of the game and increased mutual expectations of one’s group members’ contributions as drivers for the communication effect. Finally, we discuss the role of personal and injunctive norms to keep commitments made during discussions.</p>
Public metagenome datasets annotated using SingleM, using a supplemented reference package.
<p>The SingleM package used for supplementing is available at 10.5281/zenodo.10360136</p>
Worldwide Gender Differences in Public Code Contributions - Replication Package
<p><strong>Worldwide Gender Differences in Public Code Contributions - Replication Package</strong></p> <p>This document describes how to replicate the findings of the paper: Davide Rossi and Stefano Zacchiroli, 2022, <em>Worldwide Gender Differences in Public Code Contributions</em>. In Software Engineering in Society (ICSE-SEIS'22), May 21-29, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 12 pages. <a href="https://doi.org/10.1145/3510458.3513011">https://doi.org/10.1145/3510458.3513011</a></p> <p>This document comes with the software needed to mine and analyze the data presented in the paper.</p> <p><strong>Prerequisites</strong></p> <p>These instructions assume the use of the <a href="https://www.gnu.org/software/bash/">bash</a> shell, the <a href="https://www.python.org/">Python</a> programming language, the <a href="https://www.postgresql.org/">PosgreSQL</a> DBMS (version 11 or later), the <a href="https://facebook.github.io/zstd/">zstd</a> compression utility and various usual *nix shell utilities (cat, pv, ...), all of which are available for multiple architectures and OSs.<br> It is advisable to create a <a href="https://docs.python.org/3/tutorial/venv.html">Python virtual environment</a> and install the following PyPI packages: <code>click==8.0.3 cycler==0.10.0 gender-guesser==0.4.0 kiwisolver==1.3.2 matplotlib==3.4.3 numpy==1.21.3 pandas==1.3.4 patsy==0.5.2 Pillow==8.4.0 pyparsing==2.4.7 python-dateutil==2.8.2 pytz==2021.3 scipy==1.7.1 six==1.16.0 statsmodels==0.13.0</code></p> <p><strong>Initial data</strong></p> <ul> <li><code>swh-replica</code>, a PostgreSQL database containing a copy of Software Heritage data. The schema for the database is available at <a href="https://forge.softwareheritage.org/source/swh-storage/browse/master/swh/storage/sql/">https://forge.softwareheritage.org/source/swh-storage/browse/master/swh/storage/sql/</a>.<br> We retrieved these data from <a href="https://www.softwareheritage.org">Software Heritage</a>, in collaboration with the archive operators, taking an archive snapshot as of 2021-07-07. We cannot make these data available in full as part of the replication package due to both its volume and the presence in it of personal information such as user email addresses. However, equivalent data (stripped of email addresses) can be obtained from the Software Heritage archive dataset, as documented in the article: Antoine Pietri, Diomidis Spinellis, Stefano Zacchiroli, <em>The Software Heritage Graph Dataset: Public software development under one roof</em>. In proceedings of MSR 2019: The 16th International Conference on Mining Software Repositories, May 2019, Montreal, Canada. Pages 138-142, IEEE 2019. <a href="http://dx.doi.org/10.1109/MSR.2019.00030">http://dx.doi.org/10.1109/MSR.2019.00030</a>.<br> Once retrieved, the data can be loaded in PostgreSQL to populate <code>swh-replica</code>.</li> <li><code>names.tab</code> - forenames and surnames per country with their frequency</li> <li><code>zones.acc.tab</code> - countries/territories, timezones, population and world zones</li> <li><code>c_c.tab</code> - ccTDL entities - world zones matches</li> </ul> <p><strong>Data preparation</strong></p> <ul> <li>Export data from the <code>swh-replica</code> database to create <code>commits.csv.zst</code> and <code>authors.csv.zst</code> <code>sh> ./export.sh</code></li> <li>Run the authors cleanup script to create <code>authors--clean.csv.zst</code> <code>sh> ./cleanup.sh authors.csv.zst</code></li> <li>Filter out implausible names and create <code>authors--plausible.csv.zst</code> <code>sh> pv authors--clean.csv.zst | unzstd | ./filter_names.py 2> authors--plausible.csv.log | zstdmt > authors--plausible.csv.zst</code></li> </ul> <p><strong>Gender detection</strong></p> <ul> <li>Run the gender guessing script to create <code>author-fullnames-gender.csv.zst</code> <code>sh> pv authors--plausible.csv.zst | unzstd | ./guess_gender.py --fullname --field 2 | zstdmt > author-fullnames-gender.csv.zst</code></li> </ul> <p><strong>Database creation and data ingestion</strong></p> <ul> <li> <p>Create the PostgreSQL DB <code>sh> createdb gender-commit </code>Notice that from now on when prepending the <code>psql></code> prompt we assume the execution of psql on the <code>gender-commit</code> database.</p> </li> <li> <p>Import data into PostgreSQL DB <code>sh> ./import_data.sh</code></p> </li> </ul> <p><strong>Zone detection</strong></p> <ul> <li>Extract commits data from the DB and create <code>commits.tab</code>, that is used as input for the gender detection script<br> <code>sh> psql -f extract_commits.sql gender-commit</code></li> <li>Run the world zone detection script to create <code>commit_zones.tab.zst</code> <code>sh> pv commits.tab | ./assign_world_zone.py -a -n names.tab -p zones.acc.tab -x -w 8 | zstdmt > commit_zones.tab.zst </code>Use <code>./assign_world_zone.py --help</code> if you are interested in changing the script parameters.</li> <li>Read zones assignment data from the file into the DB<br> <code>psql> \copy commit_culture from program 'zstdcat commit_zones.tab.zst | cut -f1,6 | grep -Ev ''\s$'''</code></li> </ul> <p><strong>Extraction and graphs</strong></p> <ul> <li>Run the script to execute the queries to extract the data to plot from the DB. This creates <code>commits_tz.tab</code>, <code>authors_tz.tab</code>, <code>commits_zones.tab</code>, <code>authors_zones.tab</code>, and <code>authors_zones_1620.tab</code>.<br> Edit <code>extract_data.sql</code> if you whish to modify extraction parameters (start/end year, sampling, ...). <code>sh> ./extract_data.sh</code></li> <li>Run the script to create the graphs from all the previously extracted tabfiles. This will generate <code>commits_tzs.pdf</code>, <code>authors_tzs.pdf</code>, <code>commits_zones.pdf</code>, <code>authors_zones.pdf</code>, and <code>authors_zones_1620.pdf</code>. <code>sh> ./create_charts.sh</code></li> </ul> <p><strong>Additional graphs</strong></p> <p>This package also includes some already-made graphs</p> <ul> <li><code>authors_zones_1.pdf</code>: stacked graphs showing the ratio of female authors per world zone through the years, considering all authors with at least one commit per period</li> <li><code>authors_zones_2.pdf</code>: ditto with at least two commits per period</li> <li><code>authors_zones_10.pdf</code>: ditto with at least ten commits per period</li> </ul>
OpenPack: Public multi-modal dataset for packaging work recognition in logistics domain
<p><strong>OpenPack</strong> is an open-access logistics dataset for human activity recognition, which contains human movement and package information from 16 subjects in four scenarios. Human movement information is subdivided into three types of data, acceleration, physiological, and depth-sensing. The package information includes the size and number of items included in each packaging job. </p> <p>In the "Humanware laboratory" at IST Osaka University, with the supervision of industrial engineers, an experiment to mimic logistic center labor was designed. 12 workers with previous packaging experience and 4 without experience performed a set of packaging tasks according to an instruction manual from a real-life logistics center. During the different scenarios, subjects were recorded while performing packing operations using Lidar, Kinect, and Realsense depth sensors while wearing 4 ATR IMU devices and 2 Empatica E4 wearable sensors. Besides sensor data, this dataset contains timestamp information collected from the hand terminal used to register product, packet, and address label codes as well as package details that can be useful to relate operations to specific packages.</p> <p>The 4 different scenarios include; sequential packing, worker-decided sequence changes, pre-ordered item packing, and time-sensitive stressors. Each of the subjects performed 20 packing jobs in 5 work sessions for a total of 100 packing jobs. <strong>53+</strong> hours of packaging operations have been labeled into 10 global operation classes and 16 sub-action classes for this dataset. Action classes are not unique to each operation but may only appear in one or two operations. </p> <p>You can find information on how to use this dataset at: <a href="https://open-pack.github.io/">https://open-pack.github.io/</a>. For details on how this dataset was collected please check the following publication "OpenPack: A Large-Scale Dataset for Recognizing Packaging Works in IoT-Enabled Logistic Environments" <a href="https://doi.ieeecomputersociety.org/10.1109/PerCom59722.2024.10494448">10.1109/PerCom59722.2024.10494448</a>.</p> <p> </p> <p><strong>Full Dataset</strong></p> <p>In this repository, the data and label files are contained in separate files for each worker. Each worker's file contains; IMU, E4, 2d keypoint, 3d keypoint, annotation, and system-related<em> </em>data<em>.</em></p> <p><em><strong>Preprocessed Dataset (IMU with operation and action Labels)</strong></em></p> <p>We have received many comments that it was difficult to combine multiple workers' IMU and annotation data. Therefore, we have created several CSV files containing the four IMU's sensor data and the operation labels in a single file. These files are now included as "imu-with-operation-action-labels.zip". </p> <p><em><strong>Preprocessed Dataset (Kinect 2D and 3D keypoint data with operation and action Labels)</strong></em></p> <p>We have received several requests for a preprocessed dataset containing only specific types of keypoint data with its assigned operation and action labels. Two new preprocessed files have been added for 2D and 3D keypoint data extracted from the frontal view Kinect camera. These files are:</p> <p>"<a href="11059235" target="_blank" rel="noopener noreferrer">kinect-2d-kpt-with-operation-action-labels.zip</a>", and</p> <p>"<a href="11059235" target="_blank" rel="noopener noreferrer">kinect-3d-kpt-with-operation-action-labels.zip</a>".</p> <p> </p> <p>Work is continuously being done to update and improve this dataset. When downloading and using this dataset please verify that the version is up to date with the latest release. The latest release <strong>[1.1.0]</strong> was uploaded on 24/04/2024. </p> <p><strong><em>Changes LOG:</em></strong></p> <ul> <li>v1.0.0: Add tutorial preprocessed dataset for IMU data with operation labels.</li> <li>v1.1.0: Update preprocessed datasets. (Include Kinect 2d and 3d keypoint data with Operation and action labels)</li> </ul> <p> </p> <p><strong>We hosted an activity recognition competition using this dataset (OpenPack v0.3.x) awarded at a PerCom 2023 Workshop! The task was very simple: Recognize 10 work operations from the OpenPack dataset. You can refer to this website for coding materials relevant to this dataset. </strong><a href="https://open-pack.github.io/challenge2022"><strong>https://open-pack.github.io/challenge2022</strong></a></p>
Publication packages for academic research
<p>Infographic summarizing what to include in a publication package, based on the <a href="https://doi.org/10.5281/zenodo.7583831">Guideline for the archiving of academic research for Faculties of Behavioural and Social Sciences in the Netherlands (March 2022)</a></p> <p> </p>
EUR publication package example
<p>This publication package serves as an example for how a publication package could look like based on the <a href="https://doi.org/10.5281/zenodo.7583831">Guideline for the archiving of academic research for Faculties of Behavioural and Social Sciences in the Netherlands</a> (but might be useful for other disciplines and countries as well).<br> Dataset used in this example is the <a href="https://doi.org/10.6084/m9.figshare.6262019.v4">SAFI Survey Results</a> dataset. The original dataset was generated by Philip Woodhouse, Gert Jan Veldwisch, Daniel Brockington, Hans C. Komakech, Angela Manjichi, Jean-Philippe Venot</p>
The Impact of the COVID-19 Pandemic on Women's Contribution to Public Code - Replication Package
<p>Replication package for the article "The Impact of the COVID-19 Pandemic on Women's Contribution to Public Code"</p>
Replication package for 'Medically assisted reproduction and non-normative family forms: legislation and public opinion in Europe'
<p>Replication package for the paper "Medically assisted reproduction and non-normative family forms: legislation and public opinion in Europe", accepted for publication in <em>European Societies</em> (2024). </p> <p>This repository provides the R code to replicate the results. It utilizes data from the European Values Study (available at: https://europeanvaluesstudy.eu/) and an original database on the timing of MAR access legislation for single women and same-sex female couples in Europe. </p> <p> </p> <p> </p>
Replication package for `How Do Voters Respond to Welfare vis-a-vis Public Good Programs? Theory and Evidence of Political Clientelism'
<p>Contents of Replication Package:</p> <p>Readme file, Data Dictionary, Household Codes (do, csv and dta files), VillageYear Codes (do, csv and data files), folders containing maps, survey details and replication output</p>
Geographic Diversity in Public Code Contributions — Replication Package
<p>Geographic Diversity in Public Code Contributions - Replication Package</p> <p>This document describes how to replicate the findings of the paper: Davide Rossi and Stefano Zacchiroli, 2022, <em>Geographic Diversity in Public Code Contributions - An Exploratory Large-Scale Study Over 50 Years</em>. In 19th International Conference on Mining Software Repositories (MSR ’22), May 23-24, Pittsburgh, PA, USA. ACM, New York, NY, USA, 5 pages. <a href="https://doi.org/10.1145/3524842.3528471">https://doi.org/10.1145/3524842.3528471</a></p> <p>This document comes with the software needed to mine and analyze the data presented in the paper.</p> <p>Prerequisites</p> <p>These instructions assume the use of the <a href="https://www.gnu.org/software/bash/">bash</a> shell, the <a href="https://www.python.org/">Python</a> programming language, the <a href="https://www.postgresql.org/">PosgreSQL</a> DBMS (version 11 or later), the <a href="https://facebook.github.io/zstd/">zstd</a> compression utility and various usual *nix shell utilities (cat, pv, …), all of which are available for multiple architectures and OSs.<br> It is advisable to create a <a href="https://docs.python.org/3/tutorial/venv.html">Python virtual environment</a> and install the following PyPI packages:</p> <pre><code>click==8.0.4 cycler==0.11.0 fonttools==4.31.2 kiwisolver==1.4.0 matplotlib==3.5.1 numpy==1.22.3 packaging==21.3 pandas==1.4.1 patsy==0.5.2 Pillow==9.0.1 pyparsing==3.0.7 python-dateutil==2.8.2 pytz==2022.1 scipy==1.8.0 six==1.16.0 statsmodels==0.13.2</code></pre> <p>Initial data</p> <ul> <li><code>swh-replica</code>, a PostgreSQL database containing a copy of Software Heritage data. The schema for the database is available at <a href="https://forge.softwareheritage.org/source/swh-storage/browse/master/swh/storage/sql/">https://forge.softwareheritage.org/source/swh-storage/browse/master/swh/storage/sql/</a>.<br> We retrieved these data from <a href="https://www.softwareheritage.org">Software Heritage</a>, in collaboration with the archive operators, taking an archive snapshot as of 2021-07-07. We cannot make these data available in full as part of the replication package due to both its volume and the presence in it of personal information such as user email addresses. However, equivalent data (stripped of email addresses) can be obtained from the Software Heritage archive dataset, as documented in the article: Antoine Pietri, Diomidis Spinellis, Stefano Zacchiroli, <em>The Software Heritage Graph Dataset: Public software development under one roof</em>. In proceedings of MSR 2019: The 16th International Conference on Mining Software Repositories, May 2019, Montreal, Canada. Pages 138-142, IEEE 2019. <a href="http://dx.doi.org/10.1109/MSR.2019.00030">http://dx.doi.org/10.1109/MSR.2019.00030</a>.<br> Once retrieved, the data can be loaded in PostgreSQL to populate <code>swh-replica</code>.</li> <li><code>names.tab</code> - forenames and surnames per country with their frequency</li> <li><code>zones.acc.tab</code> - countries/territories, timezones, population and world zones</li> <li><code>c_c.tab</code> - ccTDL entities - world zones matches</li> </ul> <p>Data preparation</p> <ul> <li> <p>Export data from the <code>swh-replica</code> database to create <code>commits.csv.zst</code> and <code>authors.csv.zst</code></p> <pre><code>sh> ./export.sh</code></pre> </li> <li> <p>Run the authors cleanup script to create <code>authors--clean.csv.zst</code></p> <pre><code>sh> ./cleanup.sh authors.csv.zst</code></pre> </li> <li> <p>Filter out implausible names and create <code>authors--plausible.csv.zst</code></p> <pre><code>sh> pv authors--clean.csv.zst | unzstd | ./filter_names.py 2> authors--plausible.csv.log | zstdmt > authors--plausible.csv.zst</code></pre> </li> </ul> <p>Zone detection by email</p> <ul> <li> <p>Run the email detection script to create <code>author-country-by-email.tab.zst</code></p> <pre><code>sh> pv authors--plausible.csv.zst | zstdcat | ./guess_country_by_email.py -f 3 2> author-country-by-email.csv.log | zstdmt > author-country-by-email.tab.zst</code></pre> </li> </ul> <p>Database creation and initial data ingestion</p> <ul> <li> <p>Create the PostgreSQL DB</p> <pre><code>sh> createdb zones-commit</code></pre> <p>Notice that from now on when prepending the <code>psql></code> prompt we assume the execution of psql on the <code>zones-commit</code> database.</p> </li> <li> <p>Import data into PostgreSQL DB</p> <pre><code>sh> ./import_data.sh</code></pre> </li> </ul> <p>Zone detection by name</p> <ul> <li> <p>Extract commits data from the DB and create <code>commits.tab</code>, that is used as input for the zone detection script</p> <pre><code>sh> psql -f extract_commits.sql zones-commit</code></pre> </li> <li> <p>Run the world zone detection script to create <code>commit_zones.tab.zst</code></p> <pre><code>sh> pv commits.tab | ./assign_world_zone.py -a -n names.tab -p zones.acc.tab -x -w 8 | zstdmt > commit_zones.tab.zst</code></pre> Use <code>./assign_world_zone.py --help</code> if you are interested in changing the script parameters.</li> <li> <p>Ingest zones assignment data into the DB</p> <pre><code>psql> \copy commit_zone from program 'zstdcat commit_zones.tab.zst | cut -f1,6 | grep -Ev ''\s$'''</code></pre> </li> </ul> <p>Extraction and graphs</p> <ul> <li> <p>Run the script to execute the queries to extract the data to plot from the DB. This creates <code>commit_zones_7120.tab</code>, <code>author_zones_7120_t5.tab</code>, <code>commit_zones_7120.grid</code> and <code>author_zones_7120_t5.grid</code>.<br> Edit <code>extract_data.sql</code> if you whish to modify extraction parameters (start/end year, sampling, …).</p> <pre><code>sh> ./extract_data.sh</code></pre> </li> <li> <p>Run the script to create the graphs from all the previously extracted tabfiles.</p> <pre><code>sh> ./create_stackedbar_chart.py -w 20 -s 1971 -f commit_zones_7120.grid -f author_zones_7120_t5.grid -o chart.pdf</code></pre> </li> </ul>
Reproduction package for the publication "Tidal disruption event AT2020ocn: early-time X-ray flares caused by a possible disc alignment process"
<p>This package contains the data analysed in the paper "Tidal disruption event AT2020ocn: early–time X–ray flares caused by a possible disc alignment process". The software XSPEC (Arnaud 1996) is needed to perform the spectral analysis and reproduce the results shown in the paper.</p> <p>The structure is as follows:</p> <p>./reproduction_ocn/nicer: contains all processed NICER data used in the paper, grouped by their epochs. "speclist-early.dat" lists all the early-time epochs before MJD 59130. "en_range.dat" lists the selected energy range at each epoch during the early-time period for X-ray spectral analysis. Within each epoch-specific folder, "src.fits" and "bkg.fits" are the source+background and background spectra re-binned using the FTOOL "ftgrouppha"; "*.arf" and "*.rmf" are ancillary file and response file for spectral analysis; rest files are direct products of the NICER data reduction process. See the paper for details.</p> <p>./reproduction_ocn/swift: contains all Swift/UVOT data used in the paper, grouped by their observation IDs. "m2.fits", "w1.fits", and "w2.fits" contain the UV lightcurves from three UV filters, produced by Swift task "uvotproduct". "swfxraypclc.dat" is the Swift/XRT lightcurve, produced by the online Swift pipeline: https://www.swift.ac.uk/user_objects/ (Evans et al. 2009). "./reproduction_ocn/MOSFiT-products/" includes MCMC products from the MOSFiT package (Mockler et al. 2019).</p> <p>./reproduction_ocn/xmm: contains the reduced XMM-Newton/EPIC-pn spectra of three epochs used in the paper. "1and2-slim.xcm" is fitting the XMM#1 and XMM#2 spectra using the slim disc model. "3-phenmnlgcl.xcm" and "3-relxillCp.xcm", are fitting the XMM#3 spectrum with, a powerlaw+zbbody model and a slim disc+relxillCp model, respectively.</p>
Replication package for: "Mitigating Consequences of Prestige in Citations of Publications"
<p>This package contains replication files for "Mitigating Consequences of Prestige in Citations of Publications". The package is composed of the data sets concerned with the citations of publication in the field of biomedicine. Furthermore, code written in the programming language R is additionally provided for the reproducibility of all results presented in the paper. Instructions for the starting points for replication of all results are provided in the README file.</p>
Reproduction package for publication 'Testing afterglow models of FRB 200428 with early post-burst observations of SGR 1935+2154'
<p>The scripts in this package allows for the reproduction of all figures and data within the publication. Observing data can be found in the LOFAR long-term archive. For further information about reproducing figures pertaining to observing data please contact the authors. </p> <p>X-ray data pertaining to Figure 4 is available from the public reproduction packages of the relevant cited publications. </p>
Optional data for R-package from publication: "fsbrain: an R package for the visualization of structural neuroimaging data"
<p>This is the optional data that be downloaded from within the R packages 'freesurferformats' and 'fsbrain'. See publication: https://doi.org/10.1101/2020.09.18.302935</p> <p> </p> <p>Due to CRAN limits, this data cannot be stored in the package. The author therefore stores this on a private server, which is not optimal. This uploads serves as a backup and an alternate way to access the data, e.g., for future maintainers of the software.</p> <p>Note that the files in directories 'subjects_dir/fsaverage' and 'subjects_dir/fsaverage3' are part of FreeSurfer6 and distributed under the FreeSurfer license.</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.