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764 results for “Reproducible”
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>
Reproducibility package for: AllSynth: Transiently Correct Network Update Synthesis Accounting for Operator Preferences
<p>This is a reproducibility package for the paper "AllSynth: Transiently Correct Network Update Synthesis Accounting for Operator Preferences" to appear in TASE'22.</p>
Data and Analysis Script for Cluster2022 Submission pap254: "Painless Transposition of Reproducible Distributed Environments with NixOS Compose"
<p>Data and Analysis Script for Cluster2022 Submission pap254: "Painless Transposition of Reproducible Distributed Environments with NixOS Compose"</p> <p>The experiments repository is available at: <a href="https://gitlab.inria.fr/nixos-compose/articles/cluster2022">https://gitlab.inria.fr/nixos-compose/articles/cluster2022</a></p>
On the computational reproducibility of molecular phylogenies
<p>Reports on the lack of computational reproducibility of molecular phylogenies, produced in multiple runs of the same program or by different programs, cast a long shadow on downstream research using these phylogenies and on efforts to build the tree of life. We show that this irreproducibility does not decrease the accuracy of the reconstructed evolutionary relationships. Also, we find inferred molecular phylogenies to have log-likelihoods comparable to or better than the true phylogeny. Therefore, the lack of computational reproducibility of molecular phylogenies is not a problem for evolutionary studies.</p>
Documentation and code for reproducing analyses presented in: vcferr: Development, Validation, and Application of a SNP Genotyping Error Simulation Framework
<p>Documentation and code for reproducing analyses presented in: vcferr: Development, Validation, and Application of a SNP Genotyping Error Simulation Framework. Please see the <strong>README.pdf</strong> for step-by-step instructions for reproducing the entire analysis described in the paper.</p>
A sequential two-step priming scheme reproduces diversity in synaptic strength and short-term plasticity
<p>Please consult the uploaded word file '<a href="https://zenodo.org/api/files/274d8784-bd00-47da-b7e7-7350c0aa3659/figure_4_public.docx">figure_4_public.docx</a>' for a description.</p>
Julia for Reproducibility Blogpost dataset
<p>Dataset for the Julia for Reproducibility tutorial (unpublished so far).</p> <p>30 CSV files representing classifications problems:<br> - numerical columns "xi" for predictors and column "y" (0, 1) for target<br> - variable number of columns and rows<br> - more rows than columns</p> <p>1 generate.jl file to generate this dataset.</p>
Data to: Carotenoids-based reddish pelvic spines in non-reproducing female and male sticklebacks (Gasterosteus aculeatus) – signalling social dominance?
<p>Conspicuous ornaments are often considered a result of evolution by sexual selection. According to the social selection hypothesis, such conspicuous traits may also evolve as badges of status associated with increased boldness or aggression towards conspecifics in conflicts about ecological resources. This study tested predictions from the social selection hypothesis to explain evolution of conspicuous red colour of the pelvic spines of the three-spine stickleback (Gasterosteus aculeatus). Wild non-reproducing sticklebacks were presented to pairs of dummies which differed at their pelvic spines, having either (i) normal sized grey or red pelvic spines, or (ii) normal sized grey or large red pelvic spines. The experimental tank was illuminated by white or green light, since green light impedes the sticklebacks' ability to detect red colour. The dummies moved slowly around in circles at each end of the experimental tank. We quantified the parameters (i) which of the two dummies was visited first, (ii) time taken before the first visit to a dummy, (iii) distribution of the focal sticklebacks in the two zones close to each of the two dummies and in the neutral zone of the tank, (iv) close to which of the two dummies did the focal fish eat its first food-piece, and (v) time spent until the first piece of food was eaten. This was carried out for 22 females and 29 males sticklebacks. The results suggested no effect of the colour or size of the dummies' pelvic spines, on none of the five behavioural parameters. Moreover, neither the colour of the pelvic spines of the focal sticklebacks themselves (as opposed to redness of the dummies' spines) or their body length was associated with behaviour towards the dummies. Thus, this study did not support predictions from the social selection hypothesis to explain evolution of red pelvic spines in sticklebacks.</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>
Datasets to accompany twitter reproducible methods document
<p>Datasets to accompany twitter reproducible methods document</p>
Data to reproduce the results presented in Lake et al. 2022. Hydrological processes, https://doi.org/10.1002/hyp.14726 ("Using particle size distributions to fingerprint suspended sediment sources – evaluation at laboratory and catchment scales")
<p>This repository contains data on particle size distribution data obtained from the laboratory and field experiments as described in Lake et al., 2022. </p> <p>The data contains the input files as needed for the modelling:</p> <p>- In the excel files the particle size distribution data for the target SS</p> <p>- In the text file the particle size distribution data from the sources.</p> <p> </p> <p>Furthermore, the data contains the resulting output files.</p> <p> </p>
Reproducible Self-Publication Pitch 2017-08-31
<p>The <strong>Rep</strong>roducible <strong>Se</strong>lf <strong>P</strong>ublishing toolkit demonstrates how to dynamically include publication-quality data analysis output in most common science communication formats. Data analysis is defined in one and only one place, and styling is applied at the document or output element level. Data <em>and</em> code dependencies are provided or specified, so that both the toolkit itself - as well as your own derivatives - can be reproduced locally and autonomosly by your colleagues, reviewers, students, and everybody else.</p>
Data to reproduce "CIM-CitySim coupling" on the EPFL campus
<p>The data to reproduce results from the paper " Multi-scale modelling to evaluate building energy consumption at the neighbourhood scale " with DOI : 10.1371/journal.pone.0183437 can be found in this repository.</p> <p>We provide the useful data to rerun the CitySim simulation and provide results also from the experiments conducted as well as data collected on the EPFL campus.</p> <ol> <li>Climate file for Ecublens issued from Meteonorm (Ecublens.cli)</li> <li>Climate file for Ecublens used as input for CIM (remove the header from the Ecublens.cli file)</li> <li>Ground surface temperature for the EPFL campus (epfl_surf_temp.dat)</li> <li>Geometrical characteristics for EPFL campus (epfl_geo_char.dat)</li> <li>Executable file for CIM (canopy.dbx)</li> <li>Climate file for Ecublens issued from CIM (Ecublens_cim.cli)</li> <li>Measured data at 2m above ground and from the LESO-rooftop for year 2015 (u_2m.txt, u_12m.txt, temp_2m.txt, temp_12m.txt, winddir_12m.txt).</li> <li>Simulated data from CIM for the year 2015 at 2m and 12m (u_2m-cim.txt, u_12m-cim.txt, temp_2m-cim.txt, temp_12m-cim.txt, winddir_12m-cim.txt).</li> </ol> <p>If you require anything else please contact the corresponding author.</p>
Adaptive Caching for Operation-based Versioning of Models (Reproducibility Package)
Open the record for dataset details and reuse information.
MA-BBOB - Reproducibility and Additional Data
<h1>Reproducibility files for the paper:</h1> <blockquote> <h1>MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts</h1> </blockquote> <p> </p> <div>This document details the reproduction steps for the paper "MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts"</div> <p> </p> <div>This readme is structured as follows:</div> <div>- We first showcase a code-snippet highlighting the way in which the MA-BBOB generator can be used as a stand-alone feature</div> <div>- Afterwards, we walk through the steps required for reproducing the results presented in the paper</div> <div>- Next, we have a brief description of each file included in this repository</div> <div>- Finally, we provide some background on the main dependencies used in this project</div> <h2>Accessing the MA-BBOB generator</h2> <div> </div> <div>Since it is integrated into the IOHexperimenter, accessing the MA-BBOB functions can be done easily from Python. As a default, the generator uses the function creation procedure described in section 3 of the paper to sample new functions. To ensure the functions are reusable, the 'instance id' is used to seed the generation procedure, so using the same id multiple times leads to the same function.</div> <div> </div> <div><code>import ioh</code></div> <div><code>f = ioh.problem.ManyAffine(1, n_variables = 5)</code></div> <p> </p> <div>However, the code used in the remainder of the repository uses specific settings of weights, instances and optima to compare different settings. These are specified instead of the instance ID as follows:</div> <p> </p> <div><code>xopt = np.random.uniform(size=(2), low=-5, high=5)</code></div> <div><code>weights = np.ones(24)/24</code></div> <div><code>iids = list(np.repeat(1,24))</code></div> <div><code>f2 = ioh.problem.ManyAffine(xopt = list(xopt), weights = list(weights), instances = iids, n_variables = 2)</code></div> <h2>Reproducing the paper's results</h2> <p> </p> <div>The core file for reproducing the results from this project is the notebook 'Visualization.ipynb'. This notebook is interrupted at times to run scripts and collect data, which is explained both in the notebook as well as in the sections below. The data collection is related to Section 4, since the results of Section 3 are entirely contained in the notebook, and Section 5 uses available data from <a href="../records/7826036">this Zenodo</a> (data included here as well for convenience).</div> <p> </p> <h3>Determine the settings used</h3> <div>Within the notebook, the section 'Setup data collection' is used to generate the used weights, instance number and optima location for all pairwise experiments. Each of these is stored in its corresponding csv-file.</div> <div>These files fully specify the pairwise instances of the MA-BBOB suite we use throughout the paper. These files are used in the scripts for the following parts (ELA + Performace)</div> <p> </p> <h3>Calculating ELA features</h3> <div>The ELA-feature computation is based on pflacco as described in the dependencies.</div> <div>The script: 'ela_calculation.py' runs the computation and stores the results as csv files (Note: the 'dirname' parameter should be changed before running this script). The file loops over all selected BBOB and MA-BBOB instances and gets the following sets of ELA features:</div> <div>* meta data</div> <div>* distribution</div> <div>* level set</div> <div>* principal component analysis</div> <div>* linear model</div> <div>* nbc</div> <div>* dispersion</div> <div>* information_content</div> <div>For more information on these feature sets, please see</div> <blockquote> <div>"Mersmann et al. (2011), “Exploratory Landscape Analysis”, in Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 829—836. ACM (http://dx.doi.org/10.1145/2001576.2001690)"</div> </blockquote> <p> </p> <h3>Performance data</h3> <p> </p> <div><strong>Collect performance data</strong></div> <div>The performance data collection script is 'collect_performance.py', which makes use of nevergrad and IOHprofiler to benchmark the selected algorithms. Note that the 'rootname' parameter should be modified when running this script.</div> <p> </p> <div>The data from running this script is available in the zenodo repository as 'data.zip'.</div> <p> </p> <div><strong>Process performance data</strong></div> <div>The previous script generates IOHanalyzer-compatible data. This can be processed via the script 'auc_calculation.py', resulting in the file 'auc.csv' if the 'dirname' parameter is set to the one used in the previous script.</div> <p> </p> <h3>Visualizations</h3> <div>All remaining analyses and visualizations are part of the notebook. Note that the corresponding directory and filenames should be updated according to the ones used in the respective scripts. Parts of the notebook make use of data from other repositories, which is included in this repository as well for convenience.</div> <p>Please note that to fully run the notebook, the zip-files 'ELA' and 'ELA_BBOB' need to be unzipped in the working directory of the notebook. </p> <h2>Included Files</h2> <div>This repository contains the following files:</div> <div>- All_2d_info2.csv and All_5d_info2.csv: these files contain ELA and performance data which is used in section 5 (and originates from this repository: <a href="../records/7826036">https://zenodo.org/records/7826036</a>)</div> <div>- auc.csv: contains the performance data used in section 4</div> <div>- auc_calculation.py: the script which is used to process the IOH-generated files into the corresponding performance metric</div> <div>- auc_w_info.csv: an intermediate file created in the notebook which combines instance-information and the performance data from auc.csv</div> <div>- collect_performance.py: The script used to generate the performance data (IOH-based files) used in section 4</div> <div>- data_example.zip: a subset of the performance data as generated by 'collect_performance.py'</div> <div>- dt_ela.csv: the ELA features used in section 4, calculated in the visualization notebook by concatenating files from the ELA.zip and ELA_BBOB.zip</div> <div>- ELA.zip and ELA_BBOB.zip: ELA features collected by 'ela_calculation.py'</div> <div>- ela_calculation.py: script used to generate all landscape features used for section 4</div> <div>- ela_normalized.csv: normalized version of dt_ela, generated in the visualization notebook</div> <div>- iids, opt_locs, weights.csv: information about the used instances in section 5</div> <div>- Visualization.ipynb: the main reproducibility file which documents the process of using all files above to result in the figures and analysis presented in the paper</div> <p> </p> <h2>Dependencies</h2> <div>This project relies on a few different libraries to achieve the presented results. Note that each of these packages (except IOHanalyzer) is available directly from pip, with the used version mentioned in the requirements file. These are as follows:</div> <p> </p> <h3>IOHexperimenter</h3> <div>Part of the <a href="https://iohprofiler.github.io/">IOHprofiler</a> environment, <a href="https://iohprofiler.github.io/IOHexp/">IOHexperimenter</a> provides an interface between optimization algorithms and problems, and adds detailed logging functionality to this pipeline.</div> <div>We use the python-version of IOHexperimenter, available on <a href="https://pypi.org/project/ioh/">pip as 'ioh'</a> (we used version 0.3.14). From this package, we use the logging component, as well as the interface to the <a href="https://bee22.com/resources/bbob%20functions.pdf">BBOB problem suite</a> and the MA-BBOB problem generator.</div> <p> </p> <h3>PFlacco</h3> <div>To analyze the function's low-level properties, we make use of Exploratory Landscape Analysis (ELA), which gives access to a wide range of features. To calculate these, we use the python-based <a href="https://github.com/Reiyan/pflacco">pflacco</a> library (version 1.2.2, note that this requires python 3.8 or higher). </div> <div>We make use of only the features which don't require sampling additional points from the function, from the 'classical_ela_features' module.</div> <p> </p> <h3>Nevergrad</h3> <div>To access a variety of optimization algorithms, we make use of the <a href="https://github.com/facebookresearch/nevergrad">Nevergrad </a>(version 0.4.3.post8).</div> <div>We make use of the following algorithms from Nevergrad's optimizers module: 'DifferentialEvolution', 'DiagonalCMA', 'RCobyla'</div> <p> </p> <h3>Modular CMA-ES + DE</h3> <div>In addition to the Nevergrad algorithms, we make use of two modular algorithm frameworks in our portfolio. The first is <a href="https://github.com/IOHprofiler/ModularCMAES">Modular CMA-ES</a>, 'modcma' on pip (version 1.0.2). The second is <a href="https://github.com/Dvermetten/ModDE">Modular DE</a>, 'modde' on pip (version 0.0.1).</div> <p> </p> <h3>IOHanalzyer</h3> <div>While not used directly in any of the presented figures in the paper, we did make use of the <a href="https://github.com/IOHprofiler/IOHanalyzer">IOHanalyzer</a> for data exploration throughout the analysis. This is an R-based library for analyzing and visualizing optimization algorithm performance. We use version 0.1.8.4.</div> <p> </p> <div> </div>
Reproducible Evaluation of Open-Source Tools for Prostate Segmentation on Public Datasets
<p>Segmentation of the prostate and surrounding regions is important for a variety of clinical and research applications. Our goal is to evaluate the generalizability of publicly available state-of-the-art AI models on publicly available datasets. To compare the AI generated segmentations to the available manually annotated ground-truth, quantitative measures such as Dice Coefficient and Hausdorff distance, along with shape radiomics features, were analyzed. Our study also aims to show how cloud-based tools can be used to analyze, store, and visualize evaluation results.<strong> </strong></p> <p>Three open-source pre-trained AI prostate segmentation tools were evaluated against expert annotations, on three publicly available MRI prostate collections, available in NCI Imaging Data Commons[1]. Two pre-trained models originate from the nnU-Net framework[2], the last pre-trained model originates from Prostate158 paper[4]. ProstateX[5], QIN-Prostate-Repeatability[6] and PROSTATE-MRI-US-Biopsy[7]. Expert annotations of the the whole prostate gland, peripheral zone (PZ) and transition zone (TZ) of the prostate are available for ProstateX collection, whole prostate gland and PZ for QIN-Prostate-Repeatability collection, and whole prostate gland for PROSTATE-MRI-US-Biopsy collection.</p> <p>We rely on the DICOM standard to encode our segmentation and radiomics results. The DICOM standard aims to achieve interoperability and FAIR[10] principles. Encoding our results in DICOM representation allows us to leverage DICOM-reliant tools, such as Google Cloud Computing tools for storage,computation, analysis and visualization. Open-source DICOM-based visualization tools such as OHIF[8] viewer can also be used to look qualitatively at the AI and expert annotations and the referenced images.. DICOM Segmentation objects are used to encode the AI models predictions, using dcmqi[11], DICOM Structured Reports on the other hand are used to encode radiomics features[3] extracted from the AI and expert annotations, using dcmqi and highdicom[12]. </p> <p>This dataset is organized in three parts: </p> <p>AI_SEGMENTATIONS_DICOM.zip, AI_STRUCTURED_REPORTS_DICOM.zip and EXPERT_SRUCTURED_REPORTS_DICOM..zip. All zip files contain DICOM objects only, sorted based on DICOM attributes, following this pattern:</p> <p>PatientID/<br> └───Modality-%StudyInstanceUID/<br> └───%SeriesInstanceUID-%SeriesDescription.dcm.</p> <p>AI_SEGMENTATIONS_DICOM.zip contains all the pre-trained AI models evaluated segmentation results, encoded as DICOM Segmentation objects. AI_STRUCTURED_REPORTS_DICOM..zip contains firstorder and shape radiomics features extracted for the AI segmentation results, such as Segmentation Volume, encoded as DICOM Structured Reports. EXPERT_SRUCTURED_REPORTS_DICOM.zip contains firstorder and shape radiomics features extracted for the expert annotations (for ProstateX, QIN-Prostate-Repeatability and PROSTATE-MRI-US-Biopsy collections) stored a DICOM Structured Reports objects.</p> <p>Code repository containing evaluation cloud-based notebooks and results/metadata .csv tables is available here:<br><a href="https://github.com/ImagingDataCommons/idc-prostate-mri-analysis">https://github.com/ImagingDataCommons/idc-prostate-mri-analysis</a></p> <h2>Additional Notes</h2> <p><strong> </strong>This project has been funded in whole or in part with Federal funds from the NCI, NIH, under task order no. HHSN26110071 under contract no. HHSN261201500003l.<br>https://portal.imaging.datacommons.cancer.gov/</p> <p>nnU-Net: <a href="https://github.com/MIC-DKFZ/nnUNet">https://github.com/MIC-DKFZ/nnUNet</a> <br>Prostate158: <a href="https://github.com/Project-MONAI/model-zoo/tree/dev/models/prostate_mri_anatomy">https://github.com/Project-MONAI/model-zoo/tree/dev/models/prostate_mri_anatomy</a><br>Pyradiomics: <a href="https://github.com/AIM-Harvard/pyradiomics">https://github.com/AIM-Harvard/pyradiomics</a> <br>Highdicom: <a href="https://github.com/herrmannlab/highdicom">https://github.com/herrmannlab/highdicom</a> <br>DCMQI: <a href="https://github.com/QIICR/dcmqi">https://github.com/QIICR/dcmqi</a> <br>Github repo: <a href="https://github.com/ImagingDataCommons/idc-prostate-mri-analysis">https://github.com/ImagingDataCommons/idc-prostate-mri-analysis</a></p> <h2>Related information</h2> <p>ProstateX - <a href="https://doi.org/10.7937/K9TCIA.2017.MURS5CL">https://doi.org/10.7937/K9TCIA.2017.MURS5CL </a><br><br>QIN-Prostate-Repeatability - <a href="https://doi.org/10.7937/K9/TCIA.2018.MR1CKGND">https://doi.org/10.7937/K9/TCIA.2018.MR1CKGND</a><br><br>PROSTATE-MRI-US-BIOPSY - <a href="https://doi.org/10.7937/TCIA.2020.A61IOC1A">https://doi.org/10.7937/TCIA.2020.A61IOC1A</a></p> <h2>References </h2> <p>[1] Fedorov A, Longabaugh WJ, Pot D, Clunie DA, Pieper S, Aerts HJ, Homeyer A, Lewis R, Akbarzadeh A, Bontempi D, Clifford W. NCI imaging data commons. Cancer research. 2021 Aug 8;81(16):4188.</p> <p>[2] Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods. 2021 Feb;18(2):203-11.</p> <p>[3] Van Griethuysen JJ, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RG, Fillion-Robin JC, Pieper S, Aerts HJ. Computational radiomics system to decode the radiographic phenotype. Cancer research. 2017 Nov 1;77(21):e104-7.</p> <p>[4] Adams, Lisa C., Marcus R. Makowski, Günther Engel, Maximilian Rattunde, Felix Busch, Patrick Asbach, Stefan M. Niehues, et al. 2022. “Prostate158 - An Expert-Annotated 3T MRI Dataset and Algorithm for Prostate Cancer Detection.” Computers in Biology and Medicine 148 (September): 105817.</p> <p>[5] Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2020.A61IOC1A</p> <p>[6] Fedorov, A; Schwier, M; Clunie, D; Herz, C; Pieper, S; Kikinis, R; Tempany, C; Fennessy, F. (2018). Data From QIN-PROSTATE-Repeatability. The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2018.MR1CKGND</p> <p>[7] Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2020.A61IOC1A</p> <p>[8] Open Health Imaging Foundation Viewer: An Extensible Open-Source Framework for Building Web-Based Imaging Applications to Support Cancer Research. Erik Ziegler, Trinity Urban, Danny Brown, James Petts, Steve D. Pieper, Rob Lewis, Chris Hafey, and Gordon J. Harris</p> <p>[9] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. Journal of digital imaging. 2013 Dec;26(6):1045-57.</p> <p>[10] Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data. 2016 Mar 15;3(1):1-9.</p> <p>[11] Herz C, Fillion-Robin JC, Onken M, Riesmeier J, Lasso A, Pinter C, Fichtinger G, Pieper S, Clunie D, Kikinis R, Fedorov A. DCMQI: an open source library for standardized communication of quantitative image analysis results using DICOM. Cancer research. 2017 Nov 1;77(21):e87-90.</p> <p>[12] Bridge CP, Gorman C, Pieper S, Doyle SW, Lennerz JK, Kalpathy-Cramer J, Clunie DA, Fedorov AY, Herrmann MD. Highdicom: A python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology. Journal of Digital Imaging. 2022 Aug 22:1-9.</p> <p> </p>
Dataset to reproduce the figures of article "Information dynamics of in silico EEG Brain Waves"
<p>Data and code to reproduce results figures.</p>
Data and programmes to reproduce figures from Reichert et al. (2024)
<p>Figure 1:</p> <p>Use Figure_1.ipynb to plot Figure 1 from the manuscript of Reichert et al. (2024).</p> <p>Figure 2:</p> <p>Use Figure_2.ipynb to plot Figure 2 from the manuscript of Reichert et al. (2024).</p> <p>Figure 3:</p> <p>Use Figure_3.pro (IDL) to compute the wavelet transform of the test signals and save the results in Figure_3.nc. Finally, use Figure_3.ipynb to plot Figure 3 from the manuscript of Reichert et al. (2024).</p> <p>Figure 4:</p> <p>Use Figure_4.pro (IDL) to compute the wavelength ratio and save the results in Figure_4.nc. Finally, use Figure_4.ipynb to plot Figure 4 from the manuscript of Reichert et al. (2024).</p> <p>Figure 5:</p> <p>Use Figure_5.pro (IDL) to compute the wavelet transform of the test signals and save the results in Figure_5.nc. Finally, use Figure_5.ipynb to plot Figure 5 from the manuscript of Reichert et al. (2024).</p> <p>Figure 6:</p> <p>Use Figure_6.pro (IDL) to compute the wavlength ratio and save the results in Figure_6.nc. Finally, use Figure_6.ipynb to plot Figure 6 from the manuscript of Reichert et al. (2024).</p> <p>Figure 7:</p> <p>Use Figure_7.pro (IDL) to compute the wavelet transform of the test signals and save the results in Figure_7.nc. Finally, use Figure_7.ipynb to plot Figure 7 from the manuscript of Reichert et al. (2024).</p> <p>Figure 8:</p> <p>First, use Figure_8.pro (IDL) to read in the CORAL temperature data (20180521-2139_T60Z900.nc), extract one profile and derive temperature perturbations, temperature background, wave amplitude and stratification. Also, read in spectrally truncated (T21) ERA5 data (era5_riogrande_201805_T21Z500.sav) to compute the mid-frequency maximum vertical wavelength from winds and stratification. All derived quantities are saved in Figure_8.nc and are again read in with Figure_8.ipynb in order to create the actual Figure as seen in the manuscript of Reichert et al. (2024).</p> <p>Figure 9:</p> <p>Use Figure_9.pro (IDL) to do Monte Carlo simulations and determine the significance levels for the wavelet power spectrum and save the results in Figure_9.nc. Finally use Figure_9.ipynb to plot Figure 9 from the manuscript of Reichert et al. (2024).</p> <p>Figure 10:</p> <p>Use Figure_10.ipynb to plot Figure 10 from the manuscript of Reichert et al. (2024).</p>
Dataset about Reproducibility in Software Engineering Research: A Systematic Mapping Study
<p>This artifact contains the results collected in one Systematic Mapping Study (SMS), about Reproducibility in Software Engineering Research. The results are associated with the selected studies and with the research questions considered.</p>
Data and code for "Trace elements increase reproducibility of microbial growth"
<p>Data and code for the paper <em><a href="https://doi.org/10.1101/2024.07.15.603609">Trace elements increase reproducibility of microbial growth</a>.</em></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.