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48 results for “Reproducibility Of Result”
Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing (code, data and scripts to reproduce paper results)
<p>This repository contains the data, code, and scripts required to reproduce the results of the paper "Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing" by Daniele De Sensi, Salvatore Di Girolamo and Torsten Hoefler, presented at the 2019 International Conference for High Performance Computing, Networking, Storage, and Analysis. </p> <p>This repository does not contains the code of the library used to automatically tune the routing algorithm, which can be found at http://doi.org/10.5281/zenodo.3372785</p>
Data to reproduce the results presented in Lake et al. 2021. Journal of Soils and Sediments, https://doi.org/10.1007/s11368-021-03107-6 ("High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting")
<p>This repository contains data on (1) the absorbance data and (2) the measured concentrations, to reproduce computational results as presented in:<br> "High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting".</p> <p> <br> 1. Absorbance data (200-730 nm wavelengths):</p> <p> * Average absorbance compensated for measured concentrations (average absorbance value per concentration)<br> * Average absorbance compensated for theoretical concentrations (average absorbance value per concentration)<br> * Average raw absorbance measured (average absorbance value per concentration)<br> * Raw absorbance measured (all absorbance values for all concentrations)</p> <p> Data in all 3 files is indicated per soil sample / mixture, with corresponding fraction(s) of soil sample(s) and corresponding (theoretical) input concentration.<br> <br> 2. Measured concentration data:</p> <p> * Measured concentration (average concentrations, tested for all experiments and for all theoretical input concentrations)</p> <p> </p>
Including Data Management in Research Culture Increases the Reproducibility of Scientific Results
<p><strong>General Information:</strong></p> <p>This dataset contains artifacts related to Riedel et al. (2022) (https://dx.doi.org/10.18420/inf2022_114). Here, we investigate the reproducibility of 108 research papers published between 2017 and 2021 by members of the Collaborative Research Center 1294 – Data Assimilation. To that end, we relate to a previous study by Stagge et al. (2019) that relies on a questionnaire that we extended. </p> <p>The publication by Stagge et al. (2019) is available here: https://doi.org/10.5281/zenodo.2562268<br> The dataset by Stagge et al. (2019) is available here: https://doi.org/10.1038/sdata.2019.30</p> <p>This dataset contains the questionnaire that we used to evaluate the reproducibility of scientific publications, a csv file containing the questionnaire’s answers, and a Jupyter notebook script to evaluate the given data.</p> <p><strong>Run the code:</strong></p> <p>To run the code, you must install Anaconda [1] and then open the jupyter notebook. All necessary libraries are listed in "requirement.txt". </p> <p>Alternatively, you can import the .ipyab file in the colab [2] and run it. </p> <p><br> [1]. https://www.anaconda.com/<br> [2]. https://research.google.com/colaboratory/<br> </p>
Data to reproduce the results: Statistical power of spatial earthquake forecast tests
<p>We provide data needed to reproduce the figures from the publication titled "Statistical power of spatial earthquake forecast tests".</p>
Data and scripts to reproduce the results shown in "Stability of attractor local dimension estimates in non-Axiom A dynamical systems"
<p>Here we make available all the codes and datasets to reproduce the results of the paper "Stability of attractor local dimension estimates in non-Axiom A dynamical systems" by Flavio Pons, Gabriele Messori and Davide Faranda.</p> <p>The pre-print of the article is available at https://hal.science/hal-04051659/document.</p> <p>Any question/comment can be sent to flavio.pons@gmail.com.</p> <p>License for the codes and simulation/analysis results (*.Rda files): the code is shared under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license, see https://creativecommons.org/licenses/by-nc-sa/4.0/</p> <p>License and terms of use for the ERA5 data (z500_daily_euro.nc): the ERA5 500 hPa geopotential was downloaded from https://climexp.knmi.nl/start.cgi</p>
Datasets, reproducible codes, and results for evaluating differential expression analysis methods on population-level RNA-seq data
<p>This upload contains the necessary R codes and data to reproduce the FDR and Power results described in our correspondence "Neglecting normalization impact in semi-synthetic RNA-seq data simulation generates artificial false positives" to Li Y, Ge X, Peng F, Li W, Li JJ, Exaggerated false positives by popular differential expression methods when analyzing human population samples, <em>Genome Biology</em> 23, 79, 2022, DOI: 10.1186/s13059-022-02648-4.</p>
R code and radiocarbon dates to reproduce figures and results of our paper "Population dynamics ..."
<p>R Codes and dataset to reproduce figures and results of the paper <strong><em>Population dynamics during the Neolithic transition and the onset of megalithism in Portugal according to summed probability distribution of radiocarbon determinations</em>. </strong></p>
Data to reproduce the results presented in Sehgal et al. 2022. Water Resources Research, https://doi.org/10.1029/2021WR030624 ("Inferring suspended sediment carbon content and particle size at high-frequency from the optical response of a submerged spectrometer")
<p>This repository consists data to reproduce results as presented in: "Inferring suspended sediment carbon content and particle size at high-frequency from the optical response of a submerged spectrometer", Water Resorces Research. Kindly refer to the readme.text file to navigate through the dataset.</p> <p> </p> <p> </p>
STATA code to reproduce results in the manuscript "Low birth weight risk during COVID-19: Evidence from a nationwide study in India"
<p>This STATA code will reproduce results in the manuscript "Low birth weight risk during COVID-19: Evidence from a nationwide study in India" The users will have to register and access the data from www.dhsprogram.com to run the analysis code. </p>
Data to reproduce FAME results
<p>Synthetic WGBS data set with ground truth and processed EPIC bead data set to reproduce results obtained for FAME and other methods appearing in [coming soon].</p> <p>The original WGBS data complementing the EPIC bead calls are published by Pidsley et al. and should be downloaded using the IDs provided in the Online Appendix [coming soon].</p>
Data to reproduce the results presented in Lake et al. 2024. Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2024.131930. ("High-frequency spatial sediment source fingerprinting using in situ absorbance data")
<p>This repository contains data on the used absorbance data, measured at the field site, as described in Lake et al., 2024 (<span>h</span><span>t</span><span>t</span><span>p</span><span>s</span><span>:</span><span>/</span><span>/</span><span>d</span><span>o</span><span>i</span><span>.</span><span>o</span><span>r</span><span>g</span><span>/</span><span>1</span><span>0</span><span>.</span><span>1</span><span>0</span><span>1</span><span>6</span><span>/</span><span>j</span><span>.</span><span>j</span><span>h</span><span>y</span><span>d</span><span>r</span><span>o</span><span>l</span><span>.</span><span>2</span><span>0</span><span>2</span><span>4</span><span>.</span><span>1</span><span>3</span><span>1</span><span>9</span><span>3</span><span>0).</span> Furthermore, data on the turbidity, used calibration curves and R code to prepare the input data for the MixSIAR model are included in the data repository.</p>
Dataset Literature Review Digital Forensic and Reproducibility of Result
<p>Data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci "<em>digital forensic" dan</em> "<em>reproducibility of result"</em></p>
Dataset Literature Review Digital Forensic AND Reproducibility of Results
<p>data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "Digital Forensic" dan "Reproducibility of Results"</p>
Data and program codes to reproduce the results presented in "Crustal structure beneath Central Kamchatka inferred from ambient noise tomography"
<p>This archive contains the SURF_TOMO code for the surface-wave tomography (<em>Koulakov et al., 2016</em>) and all the necessary files and instructions for reproducing the results presented in the paper “Crustal structure beneath Central Kamchatka inferred from ambient noise tomography” by Igor Egorushkin, Ivan Koulakov, Andrey Jakovlev, Hsin-Hua Huang, Eugeny I. Gordeev, Ilyas Abkadyrov, and Danila Chebrov.</p>
Reproducible Validation and Replication Studies in Nanoscale Physics (repro results plots - Ellis et al., 2016)
<p>This archive contains the Jupyter notebooks needed to reproduce the figures of the paper that are related to the Validation results and replication of Ellis et al. 2016. For further information direct to the README.md file.</p>
Non-random network connectivity comes in pairs: Code & generated data to reproduce results and figures of the article
<p>Complete research code and generated data for the article to reproduce the figures and computations referenced.</p> <p>Please visit https://non-random-connectivity-comes-in-pairs.github.io/ for documentation of the code.</p>
Data availability to ensure the reproducibility of the results of Rivaes et al. (2017) in the journal HESSD.
<p>These data is provided to ensure the reproducibility of the results presented in the publication authored by Rivaes et al. (2017) in the Journal of Hydrology and Earth System Sciences Discussion.</p>
Dataset and LOTOS program code to reproduce the main results of seismic tomography for West Aegean region (Turkey)
<p>This file contains the data to reproduce the results presented in the article: </p><p>Petrov I., Bushenkova N., Gulten, P., (2023). Intracontinental extension settings in the structure of the Aegean region (Turkey): local seismic tomography study., <i>Journal of Geodynamics.</i></p><p>This file includes:</p><p>1. The full version of the LOTOS code for the seismic tomography (Koulakov, 2009);</p><p>2. Folder with the dataset including arrival times of the P and S waves and the location of network from local seismicity in Aegean region of Tukey and it`s surroundings;</p><p>3. README.PDF file with the description of how to reproduce the tomography models and datatests based on data presented in the article. </p><p>Koulakov, I., 2009, LOTOS code for local earthquake tomographic inversion: Benchmarks for testing tomographic algorithms: Bulletin of the Seismological Society of America, v. 99, p. 194–214, https://doi.org/10.1785/0120080013.</p>
Data and program codes to reproduce the results of seismic tomography for Okmok
<p>This file contains the files to reproduce the results presented in the article: Kasatkina, E., Koulakov I., Grapenthin, R., Izbekov, P., Larsen, J., Al Alifi, N., and Qaysi, S.I. (2022). Multiple shallow magma sources beneath the Okmok caldera as inferred from local earthquake tomography, <em>Journal of Geophysical Research, Solid Earth</em>.</p> <p>This file includes:</p> <p>1. The full folder with the LOTOS code for the passive-source seismic tomography (Koulakov, 2009, BSSA). </p> <p>2. Folder with the dataset including arrival times of the P and S waves from local seismicity in the area of the Okmok Caldera in Aleutian Islands.</p> <p>3. README_OKMOK.PDF file with the description of the workflow on how to reproduce the tomography models based on experimental and synthetic data presented in the article. </p> <p>Koulakov, I., 2009, LOTOS code for local earthquake tomographic inversion: Benchmarks for testing tomographic algorithms: Bulletin of the Seismological Society of America, v. 99, p. 194–214, https://doi.org/10.1785/0120080013.</p>
Data and program codes to reproduce the results of local earthquake seismic tomography for Tenerife Island
<p>This file contains the files to reproduce the results presented in the article: <strong>Local earthquake seismic tomography reveals the link between the crustal structure and volcanism in Tenerife (Canary Islands) </strong>by Ivan Koulakov, Luca D'Auria, Janire Prudencio, Iván Cabrera-Pérez, Nemesio M. Pérez, Jesús M. Ibáñez, <em>Journal of Geophysical Research, Solid Earth</em>.</p> <p>This file includes:</p> <p>1. The full folder with the LOTOS code for the passive-source seismic tomography (Koulakov, 2009, BSSA). </p> <p>2. Folder with the dataset including arrival times of the P and S waves from local seismicity in the area of the Tenerife Island, Canary Archipelago.</p> <p>3. README_TENERIFE.PDF file with the description of the workflow on how to reproduce the tomography models based on experimental and synthetic data presented in the article. </p> <p>Koulakov, I., 2009, LOTOS code for local earthquake tomographic inversion: Benchmarks for testing tomographic algorithms: Bulletin of the Seismological Society of America, v. 99, p. 194–214, https://doi.org/10.1785/0120080013.</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.