Find research datasets worth reusing

Replication Data and Analyses for: J. Monsimet, S. Sjögersten, N.J. Sanders, M. Jonsson, J. Olofsson & M. Siewert, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, Remote Sensing in Ecology and Conservation.

This dataset corresponds to the article: "Jérémy Monsimet*¹, Sofie Sjögersten², Nathan J. Sanders³, Micael Jonsson¹, Johan Olofsson¹, Matthias Siewert¹, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, Remote Sensing in Ecology and Conservation" DOI: <a href="https://doi.org/10.1002/rse2.400" target="_blank" rel="nofollow noreferrer noopener">10.1002/rse2.400</a> 1 Department of Ecology and Environmental Science, Umeå University, Sweden 2 School of Biosciences, University of Nottingham, Loughborough, UK 3 Department of Ecology and Evolutionary Biology, University of Michigan, US The gitlab repository of this dataset is available at: <a href="https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/">https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/</a> In this repository, you will find the analyses and results presented in the paper. In each folder, there is a html file that can be read after downloading locally the whole folder. You can either run the .qmd file used to produce the html file or walk through the html files (see the readme.md for more information). Paper abstract: High‐resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in ecosystem functioning, yet their distribution and effects on entire landscapes remain poorly understood, in part because they and their mounds are too small for satellite remote sensing. This research maps the distribution and impact of ant mounds in a 20 ha treeline ecotone. We evaluate the detectability from UAV imagery using a deep learning model for object detection and different combinations of RGB, thermal and multispectral sensor data. We were able to detect ant mounds in all imagery using manual detection and deep learning. However, the highest precision rates were achieved by deep learning using RGB data which has the highest spatial resolution (1.9 cm) at comparable UAV flight height. While multispectral data were outperformed for detection, it allows for novel insights into the ecology of ants and their spatial impact on vegetation productivity using the normalized difference vegetation index. Scaling up, this suggests that ant mounds quantifiably impact vegetation productivity for up to 4% of our study area and up to 8% of the Betula nana vegetation communities, the vegetation type with the highest abundance of ant mounds. Therefore, they could have an overlooked role in nutrient‐limited tundra vegetation, and on the shrubification of this habitat. Further, we show the powerful combination UAV multi‐sensor data and deep learning for efficient ecological tracking and monitoring of mound‐building ants and their spatial impact.

Replication Archive for "Teen Social Interactions and Well-being during the COVID-19 Pandemic"

This archive includes the Stata code to replicate all results in the referenced paper.

Source Code Archiving to the Rescue of Reproducible Deployment — Replication Package

Replication package for the paper: Ludovic Courtès, Timothy Sample, Simon Tournier, Stefano Zacchiroli. Source Code Archiving to the Rescue of Reproducible Deployment <a href="https://acm-rep.github.io/2024/">ACM REP'24</a>, June 18-20, 2024, Rennes, France <a href="https://doi.org/10.1145/3641525.3663622">https://doi.org/10.1145/3641525.3663622</a> <h2>Generating the paper</h2> The paper can be generated using the following command: <pre><code>guix time-machine -C channels.scm \ -- shell -C -m manifest.scm \ -- make </code></pre> This uses GNU Guix to run <code>make</code> in the exact same computational environment used when preparing the paper. The computational environment is described by two files. The <code>channels.scm</code> file specifies the exact version of the Guix package collection to use. The <code>manifest.scm</code> file selects a subset of those packages to include in the environment. It may be possible to generate the paper without Guix. To do so, you will need the following software (on top of a Unix-like environment): <ul> <li>GNU Make</li> <li>SQLite 3</li> <li>GNU AWK</li> <li>Rubber</li> <li>Graphviz</li> <li>TeXLive</li> </ul> <h2>Structure</h2> <ul> <li><code>data/</code> contains the data examined in the paper</li> <li><code>scripts/</code> contains dedicated code for the paper</li> <li><code>logs/</code> contains logs generated during certain computations</li> </ul> <h2>Preservation of Guix</h2> Some of the claims in the paper come from analyzing the Preservation of Guix (PoG) database as published on January 26, 2024. This database is the result of years of monitoring the extent to which the source code referenced by Guix packages is archived. This monitoring has been carried out by Timothy Sample who occasionally publishes reports on his personal website: <a href="https://ngyro.com/pog-reports/latest/">https://ngyro.com/pog-reports/latest/</a>. The database included in this package (<code>data/pog.sql</code>) was downloaded from <a href="https://ngyro.com/pog-reports/2024-01-26/pog.db">https://ngyro.com/pog-reports/2024-01-26/pog.db</a> and then exported to SQL format. In addition to the SQL file, the database schema is also included in this package as <code>data/schema.sql</code>. The database itself is largely the result of scripts, but also of manual adjustments (where necessary or convenient). The scripts are available at <a href="https://git.ngyro.com/preservation-of-guix/">https://git.ngyro.com/preservation-of-guix/</a>, which is preserved in the Software Heritage archive as well: <a href="https://archive.softwareheritage.org/swh:1:snp:efba3456a4aff0bc25b271e128aa8340ae2bc816;origin=https://git.ngyro.com/preservation-of-guix">https://archive.softwareheritage.org/swh:1:snp:efba3456a4aff0bc25b271e128aa8340ae2bc816;origin=https://git.ngyro.com/preservation-of-guix</a>. These scripts rely on the availability of source code in certain locations on the Internet, and therefore will not yield exactly the same result when run again. <h3>Analysis</h3> Here is an overview of how we use the PoG database in the paper. The exact way it is queried to produce graphs and tables for the paper is laid out in the Makefile. The <code>pog-types.sql</code> query gives the counts of each source type (e.g. “git” or “tar-gz”) for each commit covered by the database. The <code>pog-status.sql</code> query gives the archival status of the sources by commit. For each commit, it produces a count of how many sources are stored in the Software Heritage archive, missing from it, or unknown if stored or missing. The <code>pog-status-total.sql</code> query does the same thing but over all sources without sorting them into individual commits. The <code>disarchive-ratio.sql</code> query estimates the success rate of Disarchive disassembly. Finally, the <code>swhid-ratio.sql</code> query gives the proportion of sources for which the PoG database has an SWHID. <h3>Estimating missing sources</h3> The Preservation of Guix database only covers sources from a sample of commits to the Guix repository. This greatly simplifies the process of collecting the sources at the risk of missing a few. We estimate how many are missed by searching Guix’s Git history for Nix-style base-32 hashes. The result of this search is compared to the hashes in the PoG database. A naïve search of Git history results in an over estimate due to Guix’s branch development model. We find hashes that were never exposed to users of ‘guix pull’. To work around this, we also approximate the history of commits available to ‘guix pull’. We do this by scraping push events from the guix-commits mailing list archives (<code>data/guix-commits.mbox</code>). Unfortunately, those archives are not quite complete. Missing history is reconstructed in the <code>data/missing-links.txt</code> file. This estimate requires a copy of the Guix Git repository (not included in this package). The repository can be obtained from GNU at <a href="https://git.savannah.gnu.org/git/guix.git">https://git.savannah.gnu.org/git/guix.git</a> or from the Software Heritage archive: <a href="https://archive.softwareheritage.org/swh:1:snp:9d7b8dcf5625c17e42d51357848baa226b70e4bb;origin=https://git.savannah.gnu.org/git/guix.git">https://archive.softwareheritage.org/swh:1:snp:9d7b8dcf5625c17e42d51357848baa226b70e4bb;origin=https://git.savannah.gnu.org/git/guix.git</a>. Once obtained, its location must be specified in the Makefile. To generate the estimate, use: <pre><code>guix time-machine -C channels.scm \ -- shell -C -m manifest.scm \ -- make data/missing-sources.txt </code></pre> If not using Guix, you will need additional software beyond what is used to generate the paper: <ul> <li>GNU Guile</li> <li>GNU Bash</li> <li>GNU Mailutils</li> <li>GNU Parallel</li> </ul> <h2>Measuring link rot</h2> In order to measure link rot, we ran Guix Scheme scripts, i.e., scripts that exploit Guix as a Scheme library. The scripts depend on the state of world at the very specific moment when they ran. Hence, it is not possible to reproduce the exact same outputs. However, their tendency over the passing of time should be very similar. For running them, you need an installation of <a href="https://guix.gnu.org/manual/deve/en/html_node/Installation.html">Guix</a>. For instance, <pre><code>guix repl -q scripts/table-per-origin.scm </code></pre> When running these scripts for the paper, we tracked their output and saved it inside the <code>logs</code> directory.

opencc-by-4.0Feb 2020View details →

Рис. 1. Calyptra thalictri: 1 — Calyptra thalictri alexander ssp. n., гоΛотип; 2 — Calyptra thalictri alexander ssp. n., паратип; 3 — кΛаΑограмма Calyptra thalictri. Построена метоΑом максимаΛьного схоΑства, параметрическая моΑеΛь Тамура-Неи, 10 000 бутстрапрепΛикаций; 4 — биотоп Calyptra thalictri alexander ssp. n. Fig. 1. Calyptra thalictri: 1 — Calyptra thalictri alexander ssp. n., holotype; 2 — Calyptra thalictri alexander ssp. n., paratype; 3 — cladogram of Calyptra thalictri. Based on the maximum likelihood method, Tamura-Nei parametrical model, 10000 bootstrap replications; 4 — biotope of Calyptra thalictri alexander ssp. n. in A New Subspecies Of (Borkhausen, 1790) (Lepidoptera: Erebidae, Calpinae) From Kyrgyzstan

Рис. 1. Calyptra thalictri: 1 — Calyptra thalictri alexander ssp. n., гоΛотип; 2 — Calyptra thalictri alexander ssp. n., паратип; 3 — кΛаΑограмма Calyptra thalictri. Построена метоΑом максимаΛьного схоΑства, параметрическая моΑеΛь Тамура-Неи, 10 000 бутстрапрепΛикаций; 4 — биотоп Calyptra thalictri alexander ssp. n. Fig. 1. Calyptra thalictri: 1 — Calyptra thalictri alexander ssp. n., holotype; 2 — Calyptra thalictri alexander ssp. n., paratype; 3 — cladogram of Calyptra thalictri. Based on the maximum likelihood method, Tamura-Nei parametrical model, 10000 bootstrap replications; 4 — biotope of Calyptra thalictri alexander ssp. n.

Molecular dynamics simulation of SpoIVFB:Pro-SigmaK complex (replicate 4)

Replicate simulation 4/4 Found here are all files needed to reproduce or visualize the results of molecular dynamics simulation of the SpoIVFB intramembrane protease bound to the transcription factor Pro-sigmaK. The protein complex was embedded in a POPE_POPG_DAG_CL bilayer using CHARMM-GUI and simulated using OpenMM. The README file is a C-shell script that will run equilibration and 250ns of unrestrained simulation.  Individual output (.out) and trajectory (.dcd) files are provided for each checkpoint of the simulation. A combined trajectory containing 250 ns of unrestrained simulation is also provided (combined_250ns_traj.dcd). Together with the step5_input.psf file, this combined dcd file can be used with common software such as VMD to visualize the molecular dynamics trajectory.

Molecular dynamics simulation of SpoIVFB:Pro-SigmaK complex (replicate 3)

Replicate simulation 3/4 Found here are all files needed to reproduce or visualize the results of molecular dynamics simulation of the SpoIVFB intramembrane protease bound to the transcription factor Pro-sigmaK. The protein complex was embedded in a POPE_POPG_DAG_CL bilayer using CHARMM-GUI and simulated using OpenMM. The README file is a C-shell script that will run equilibration and 250ns of unrestrained simulation.  Individual output (.out) and trajectory (.dcd) files are provided for each checkpoint of the simulation. A combined trajectory containing 250 ns of unrestrained simulation is also provided (combined_250ns_traj.dcd). Together with the step5_input.psf file, this combined dcd file can be used with common software such as VMD to visualize the molecular dynamics trajectory.

Molecular dynamics simulation of SpoIVFB:Pro-SigmaK complex (replicate 2)

Replicate simulation 2/4 Found here are all files needed to reproduce or visualize the results of molecular dynamics simulation of the SpoIVFB intramembrane protease bound to the transcription factor Pro-sigmaK. The protein complex was embedded in a POPE_POPG_DAG_CL bilayer using CHARMM-GUI and simulated using OpenMM. The README file is a C-shell script that will run equilibration and 250ns of unrestrained simulation.  Individual output (.out) and trajectory (.dcd) files are provided for each checkpoint of the simulation. A combined trajectory containing 250 ns of unrestrained simulation is also provided (combined_250ns_traj.dcd). Together with the step5_input.psf file, this combined dcd file can be used with common software such as VMD to visualize the molecular dynamics trajectory.

Auxiliary data files for replication of "Augmenting the availability of historical GDP per capita estimates through machine learning"

This repository holds auxiliary data files needed for the replication "Augmenting the availability of historical GDP per capita estimates through machine learning". All further information and data is provided in the <a href="https://github.com/philmkoch/historicalGDPpc" target="_blank" rel="noopener">GitHub repository</a>. The data included in this auxiliary folder is based on the work by Laouenan et al. (https://www.nature.com/articles/s41597-022-01369-4).

Replication Package for "Catching Smells in the Act: A GitHub Actions Workflow Investigation" (SCAM 2024)

Welcome to our artifact! In here we provide additional information on how to retrace our steps performed during the research. We have split up our content into four sections based on the RQ's we have answered. Below you can find a quick summary of the contents of each folder, each folder also contains additional information regarding any data and scripts present. <ul> <li>RQ1 + 2: Contains excel files with the commits we have analyzed and the scripts we have used to automate this process.</li> <li>RQ3: Contains our smell detector and evaluation of the detector</li> <li>RQ4: Contains the data on our contribution study</li> </ul>

opencc-by-4.0Jan 2024View details →

Replication video: ePLANET in a nutshell

This video is part of the replication phase of ePLANET project and explains which are the key results and outputs of the project: from clustering governance for knowledge sharing to digitalisation of energy transition actions, ePLANET supports the Energy Transition in European public authorities by facilitating the adoption of coordinated energy transition actions. <div></div> <div> <div> <div></div> </div> </div>

opencc-by-4.0Jan 2024View details →

Replication video: ePLANET platform description

The video shows the functionalities of the ePLANET Platform focusing on the usability of the most interesting sections of the platform, trying to highlight the potential of the platform as a key tool for the clustering governance and the knowledge-sharing actions of the project. It combines a recording of the platform while performing different tasks, with a voice-over presentation in English.

carjgil/teacher-bias: Teacher Bias - Replication Package

*Project: Teacher's Bias in Assessments *Code: Replication of data cleaning and preparation and analyses *Authors: Carlos J. Gil-Hernández, Irene Pañeda-Fernández, Leire Salazar, and Jonatan Castaño-Muñoz *Last Update: 04/07/2024 *Software: STATA/MP 17 Here you can find the replication dofile in STATA format in the "code" folder and the raw and working datasets (including the codebook) of the teacher's bias in assessment experiment in the "data" folder: 1. "/replication files/code/datacleaning.do" contains all the data cleaning and preparation procedures from the raw anonymized Qualtrics data where we applied the survey experiment (see "data" folder .dta or .csv files named "raw_dataset_anonymized") to set a working dataset ready to be analyzed. 2. The folder "/replication files/data" contains the data files named "raw_dataset_anonymized" and "cleandataset" in .dta (data/STATA) or .csv (data/CSV) format on the raw and working data, respectively, to replicate the findings of the teacher's bias in assessments project or run your own analyses. If you do not have access to STATA software, you can check the variables labels of the "cleandataset" in the "data/codebook_cleandataset" Excel file. <blockquote> Data Citation: Gil-Hernández, Carlos J., Leire Salazar, Jonatan Castaño Muñoz, and Irene Pañeda-Fernandez. 2023. "Teacher's Bias Dataset: A Factorial Survey Experiment." European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/f14f5209-f032-4218-a89a-4643143809af </blockquote> 3. "datanalysis.do" reproduces all the tables and figures presented in the article and online appendix (if you want to reproduce the analyses from the pre-test pilot data, please get in contact with the corresponding author) using the data file named "cleandataset" in the "data" folder (in .dta or .csv format). The output from "datanalysis.do" will be printed in the "/replication files/output" subfolders for tables (main or appendix) or figures (main or appendix).

Replication data for: Bilateral flows and rates of international migration of scholars for 210 countries and areas for the period 1998-2020

<h3>Data and code for performing analyses and plotting figures for "Bilateral flows and rates of international migration of scholars for 210 countries and areas for the period 1998-2020"</h3> The code and data can also be found at https://github.com/MPIDR/Global-flows-and-rates-of-international-migration-of-scholars/ Abstract: A lack of comprehensive migration data is a major barrier for understanding the causes and consequences of migration processes, including for specific groups like high-skilled migrants. We leverage large-scale bibliometric data from Scopus and OpenAlex to trace the global movements of scholars. Based on our empirical validations, we develop pre-processing steps and offer best practices for the measurement and identification of migration events. We have prepared a publicly accessible dataset that shows a high level of correlation between the counts of scholars in Scopus and OpenAlex for most countries. Although OpenAlex has more extensive coverage of non-Western countries, the highest correlations with Scopus are observed in Western countries. We share aggregated yearly estimates of international migration rates and of bilateral flows for 210 countries and areas worldwide for the period 1998-2020 and describe the data structure and usage notes. We expect that the publicly shared dataset will enable researchers to further study the causes and the consequences of migration of scholars to forecast the future mobility of global academic talent.

Nanotiming: single-molecule based, telomere-to-telomere DNA replication timing profiling by nanopore sequencing

Dataset for the manuscript "Nanotiming: telomere-to-telomere DNA replication timing profiling by nanopore sequencing" by Theulot et al ,2024 (https://doi.org/10.1038/s41467-024-55520-3) related to the github repository (https://github.com/LacroixLaurent/NanoTiming) <ul> <li>WT_rep3.tar.gz contains fast5 file from an experiment where yeast BT1 strain was grown for one doubling time with 5µM BrdU then DNA was sequenced on R9.4.1 ONT flowcell</li> <li>mod_mapping.bam contains the bam file resulting from the BrdU base calling with megalodon (v2.2.9) using our BT1 reference genome and our BrdU aware model for base-calling</li> <li>WT_rep3_nanoT.bed.gz contains the reads coordinates from the mod_mappings file</li> <li>WT_rep3_nanoT_alldata.rds contains the BrdU profiles for each reads of the mod_mappings file, with the BrdU signal binned in 1kb non overlaping windows</li> <li>WT_rep3_nanoT.rds contains the genomic BrdU signal profiles by 1kb non overlaping windows</li> <li>TeloLengthDataNanoT.rds contains all the telomeric sequences extracted from the experiments reported in the Figure 4 and S19 to S23 of the manuscript with the associated filtering information and nanotiming signal.</li> </ul>

Data for "A replicable and modular benchmark for long-read transcript quantification methods"

This archive contains the input necessary to run the inital (TranSigner-protocol and IsoQuant-protocol) benchmarks associated with the <a href="https://github.com/COMBINE-lab/lr_quant_benchmarks" target="_blank" rel="noopener"><code>lr_quant_benchmarks repository</code></a>.  The archive can be decompressed with <code>tar</code> and <code>zstd</code> using the command <code>tar --use-compress-program=zstd -xf input.tar.zstd</code>.

Replication package for: "The Rise of Fiscal Capacity"

<div> <div> <div>The code in this replication package constructs the analysis and output files for the paper "The Rise of Fiscal Capacity: Administration and State Consolidation in the Holy Roman Empire" from various data sources using Stata and R. The provided code reproduces all tables and figures in the paper and in the appendix.</div> </div> </div>

opencc-by-4.0Dec 2017View details →

Taxon sampling and inferred community phylogenies: R replication code and data.

1 ) Code for simulating community phylogenies: community_simulations_creation.R [taxon].gene [taxon].phy [taxon].RAxML_bestTree.tre [taxon].Simulate.A.Community.pl [taxon].Simulate.B.Community.pl [taxon].Simulate.C.Community.pl [taxon].Simulate.D.Community.pl   2) R code for creating and comparing phylogenetic diversity metrics: simulated_metric_calculation_and_comparison.R empirical_metric_calculation_and_comparison.R   3) R code and data for statistical analyses: simulated_data_analysis.R empirical_data_analysis.R simulated_interval_individual_lme_data.csv simulated_summary_interval_individual_lme_data.csv empirical_interval_individual_lme_data.csv empirical_summary_interval_lme_data.csv

opencc-by-4.0Apr 2018View details →

R replication code and data for: Do modern hunter-gatherers live in marginal habitats?

Data and R replication code for testing the Marginal Habitat Hypothesis. The R code files contain all models and are organized by the figures they generate for the associated paper. The data are sourced from: 1) the Standard Cross Cultural Sample (SCCS). 2) NASA Moderate Resolution Imaging Spectroradiometer (MODIS) NPP data (MOD17A3 algorithm) from Numerical Terra Dynamic Simulation Group at the University of Montana. 3) Marine Ecoregions Of the World (MEOW): <a href="http://maps.tnc.org/files/metadata/MEOW.xml">http://maps.tnc.org/files/metadata/MEOW.xml</a> 4) Terrestrial Ecoregions Of the World (TEOW): <a href="http://maps.tnc.org/files/metadata/TerrEcos.xml">http://maps.tnc.org/files/metadata/TerrEcos.xml</a>

opencc-by-4.0Jun 2018View details →

Data for: A systematic review and meta-analysis of Drosophila short-term-memory genetics: robust reproducibility, but little independent replication

All the data, code, analyses, and figures used in the study entitled: "A systematic review and meta-analysis of Drosophila short-term-memory genetics: robust reproducibility, but little independent replication" (doi: https://doi.org/<a href="http://bb2sz3ek3z.search.serialssolutions.com/?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&__char_set=utf8&rft_id=info:doi/10.1101/247650&rfr_id=info:sid/libx&rft.genre=article">10.1101/247650</a>) Abstract Geneticists have long used olfactory conditioning techniques in Drosophila to identify the neurons and genes that mediate learning. While this method has characterized an abundance of memory-related genes, little is known about how these genes induce short-term memory (STM) via signaling pathways; characterizing these networks will be essential to developing mechanistic models of memory formation. Here, we investigated why elucidating the STM pathways has been relatively slow. One possibility is that the STM evidence base is weak due to publication of poorly reproducible results, as has been observed in other fields. We examined this hypothesis by performing a systematic review and subsequent meta-analysis of the STM genetics field. Using several metrics to quantify the variation between discovery articles and follow-up studies, we found that seven genes were highly replicated, showed no publication bias, and had generally high reproducibility. However, the remaining ~80% memory genes have not been replicated since their initial discovery. Although we observed only a few studies that investigated gene interactions, the reviewed genes could together account for >1000% memory. This large summed effect size indicates either that some of the gene findings are not reproducible, that many memory genes participate in shared pathways, or that current protocols lack the specificity needed to identify core plasticity memory genes. Mechanistic theories of memory and cognition will require the convergence of evidence from system, circuit, cellular, molecular, and genetic experiments. As this study demonstrates, systematic data synthesis is an essential tool for this integrated brain science.