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764 results for “Reproducibility”
Agreement of multiple night- and daytime filtering approaches of eddy covariance-derived net ecosystem CO2 exchange over a mountain forest. Reproducible workflow.
<p>Datasets and python scripts to reproduce results from the publication <em>Agreement of multiple night- and daytime filtering approaches of eddy covariance-derived net ecosystem CO2 exchange over a mountain forest.</em></p> <p>See README.txt for a description of the single files.</p>
Data and GrADS scripts needed to reproduce the figures in the article "Probabilistic forecasts of near-term climate change: verification for temperature and precipitation changes from years 1971-2000 to 2011-2020"
<p>Data and GrADS scripts needed to reproduce the figures in the article "Probabilistic forecasts of near-term climate change: verification for temperature and precipitation changes from years 1971-2000 to 2011-2020", submitted for publication in Climate Dynamics.</p> <p>Please see the file README for further details.</p> <p> </p>
Datasets and scripts to reproduce figures from the paper entitled "Impact of Chlorophyll Shading on the Peruvian Upwelling System" by Echevin et al.
<p>This dataset can be used to reproduce the 4 figures published in the paper entitled "Impact of Chlorophyll Shading on the Peruvian Upwelling System" by Echevin et al. The scripts must be run using ferret software as follows:</p> <p>> go script_figureX.jnl</p> <p>The netcdf files are described below:</p> <p>- model NSH and SH climatology (3D fields :x,y,time):</p> <p>Peru10km_SHADING_monthlyclim_Y16-Y25.nc</p> <p>Peru10km_NOSHADE_monthlyclim_Y16-Y25.nc</p> <p>- NSH and SH model along-shore averaged climatologies (3D fields: x,z,time):</p> <p>section_ave.mean.NOSH10km.8S13S.nc</p> <p>section_ave.mean.SH10km.8S13S.nc</p> <p>- model grid: Peru10km_grd.nc</p> <p>- 2-degrees-wide coastal mask for the grid: mask_Peru10km_w2deg.nc</p>
Reproducible Authorship Attribution Benchmark Tasks
<p>Reproducible Authorship Attribution Benchmark Tasks (RAABT) consists of five closed-set authorship identification experiments.</p> <p>Each task features fixed train and test sets. Four of the five tasks have a test set consisting of writing samples on fixed topics, guaranteeing that test set examples do not overlap with training set examples in terms of subject matter. Data for all tasks is available for download without any restrictions.</p> <p>The file README.md contains a full description of the data.</p>
How are software repositories mined? A systematic literature review of workflows, methodologies, reproducibility, and tools
<p>This is the excel spreadsheet dataset containing our analysis of papers performing mining software repositories research from the conferences ICSE, ESEC/FSE, and MSR from the years 2018 - 2020. The data is broken into columns and can be explained at a high-level as follows:</p> <p>Column Content</p> <p>1 The paper being analyzed</p> <p>2 Does the paper state the data they analyzed is available</p> <p>3 Does the paper perform some sort of data analysis or sampling using data others have compiled in the past</p> <p>4 Does the paper state a timestamp for when they begin their work</p> <p>5 Does the paper state the use of systems pre-built to help with MSR work</p> <p>6 - 18 Forms of sampling researchers may have employed to select their data</p> <p>19 What datasets (if any) were used in the analysis</p> <p>20 What tools (if any) were used in the analysis</p> <p>21 How they performed their data sampling workflow</p> <p>22 How they performed their data filtering workflow</p> <p>23 How they performed their data retrieval workflow</p> <p>24 Did they create any scripts in each of these workflows</p> <p>25 - 33 Did they publish a replication package and what is contained within</p> <p>34 Is the paper describing a tool for research or not</p> <p>35 Short description of the paper read</p> <p>36 A high-level category of the work performed in each paper</p>
Survey of Current Reproducibility Practices at IEEE Workshops and Conferences and in Journals
<p>The goal of the IEEE Reproducibility Practices Survey was to assess the reproducibility practices of IEEE journals, magazines, and conferences. IEEE Strategic Research (with Josephine Russo as the primary point of contact) executed the survey. As part of the survey, 422 IEEE conference organizers, magazine editor-in-chiefs, and journal editors-in-chiefs were surveyed using a self-administered, online questionnaire, yielding 132 responses for a response rate of 31%. A detailed report associated with this survey can be found at https://www.computer.org/volunteering/boards-and-committees/Open-Science-Reproducibility.</p>
Data for Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study
<p>Data for Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study</p>
Data to reproduce paper: Submesoscale effects on changes to export production under global warming
<p>Data to reproduce paper: Submesoscale effects on changes to export production under global warming</p> <p>Paper is being submitted to the AGU journal Global Biogeochemical Cycles. A pre-print copy of the submitted paper will be available on essoar.org</p> <p>Matlab data files contain data to reproduce all figures in the paper and supplement</p> <p>Zipped files contain the input files for ROMS (Regional Ocean Modeling System) to re-run the simulations, when combined with the netcdf files for initial and boundary conditions. </p> <p>Pdf and image files are copies of the paper figures; supplement2_CodeDataList has a table matching the code and data for each figure. </p> <p>Matlab software is at github.com/gjayb/submesoBioROMS/ and will be archived here on Zenodo as well.</p> <p>The Bipit code for the biological model within ROMS is archived at <a href="http://doi.org/10.5281/zenodo.5716181">doi.org/10.5281/zenodo.5716181</a>, within tpos-roms-v.1.0.0\ROMS\Nonlinear\Biology </p> <p>This work was primarily funded by the National Science Foundation (NSF) under grants OCE-1658550 and OCE-1658541. This material is based on work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under Cooperative Agreement No. 1852977. Computing and data storage resources, including the Cheyenne supercomputer (doi:10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. We thank all the scientists, software engineers, and administrators who contributed to the development of CESM. <br> DBW acknowledges additional support from the National Oceanic and Atmospheric Administration (NA18OAR4310408) and the National Aeronautics and Space Administration (80NSSC19K1116). GJB acknowledges additional support from a Janney grant awarded by the Johns Hopkins University Applied Physics Laboratory. </p>
TerraSenseTK - Towards Reproducible Machine-Learning and Remote Sensing Research
<p>Dataset used in TerraSenseTK - Towards Reproducible Machine-Learning and<br> Remote Sensing Research.</p> <p>Nutrient Estimation in Common wheat Case Study available in the notebook</p> <p>Documentation is available in <a href="https://terrasensetk.readthedocs.io/en/latest/">here</a>.</p>
Data sets for "Automated cell segmentation for reproducibility in bioimage analysis"
<p>This is the raw data sets used in "Automated cell segmentation for reproducibility in bioimage analysis", published in Synthetic Biology (Oxford Academic)</p>
Reproducing during Heat Waves: Influence of Juvenile and Adult Environment on Fecundity of a Pest Mite and its Predator
<p>Dataset of the results in the article:</p> <p>Reproducing during Heat Waves: Influence of Juvenile and Adult Environment on Fecundity of a Pest Mite and its Predator</p> <p>This research was funded by the Austrian Science Fund (FWF).</p>
Data to reproduce analysis in the WCDT metastatic prostate tumor subtypying paper
<p>Systemic targeted therapy in prostate cancer is primarily focused on ablating androgen signaling. Androgen deprivation therapy and second-generation AR-targeted therapy selectively favor the development of treatment-resistant subtypes of metastatic castration resistant prostate cancer (mCRPC), defined by whether the tumor expresses either AR or neuroendocrine markers. Among the subtypes of mCRPC, the molecular drivers of double-negative (AR-/NE-) mCRPC are poorly defined. In this study, we comprehensively characterize genomic and epigenomic features of treatment-emergent mCRPC subtypes in 210 tumors by integrating matched RNA sequencing, whole-genome sequencing, and whole-genome bisulfite sequencing. We show that AR-/NE- tumors exhibit a clinically and molecularly distinct phenotype. Patients with AR-/NE- mCRPC tumors have the shortest survival, and these tumors preferentially harbor amplification of the chromatin remodeler <em>CHD7</em> and loss of <em>PTEN</em>. We demonstrate that methylation changes in <em>CHD7</em> candidate enhancers are linked to elevated <em>CHD7</em> expression in AR-/NE+ tumors. Moreover, we use genome-wide methylation analysis to nominate the Krüppel-like factor gene <em>KLF5</em> as a driver of the AR-/NE- phenotype and link its activity to loss of the tumor supressor <em>RB1</em>. These observations reveal the aggressiveness of the AR-/NE- tumors and elucidate genomic and epigenomic drivers of mCRPC subtypes, which may facilitate the identification of novel therapeutic targets in this highly aggressive disease.</p>
Data from: Reproducing in hot water: experimental heatwaves deteriorate multiple reproductive traits in a freshwater ectotherm
<p>Heatwaves are occurring at an increasing frequency and intensity under ongoing climate change. With many reproductive traits – including mating behaviour and gamete traits– being sensitive even to small stressors, including short temperature changes, the impact of heatwaves on reproduction and sexual selection processes is likely to be vast. Also, understanding whether the sexes may differentially respond to these extreme events is crucial to understand the impact on fecundity and the consequence at the population level. Nonetheless, our knowledge of the effects of heatwaves on these key aspects of an animal life is still limited. Here, we expose recently mated male and female guppies (Poecilia reticulata) to an experimental heatwave (32°C, 6°C above the control, for 5 days) to determine its effects on several traits, including sexual behaviour, condition, ornamentation, and fertility. Using this design, in contrast to most other experimental set ups, we had the possibility to attribute the effects of the heatwave to males' and females' reproductive traits independently. Overall, our results indicate that heatwaves can drastically affect key reproductive traits and unravel sex-specific responses. In males, there was no effect of the heatwave on survival, but both pre- and post-copulatory reproductive traits were affected. After the heatwave, we detected a decrease in orange colouration (the most important ornament on which female choice is based) and the overall level of sexual activity, and a shift in the preferred mating tactic towards forced copulation attempts. The latter suggest implications in sexual conflict dynamics, as forced copulations override female mate choice. Also, after the heatwave, males had more sperm but of lower quality, and, in addition, an increased variance in sperm number. Overall, heatwaves may result in a compromised ability to secure mating and fertilization. In females, the heatwave significantly affected survival, with increased mortality in the short term, and impaired fecundity, with many females from the heatwave treatment not reproducing at all. The negative effects of heatwaves on key reproductive traits unravelled by our study could have major implications for population dynamics and persistence. It highlights the need for further studies on these extreme events on reproduction, to improve our understanding of the impacts of climate change.</p>
Ability of the ash dieback pathogen to reproduce and to induce damage on its host are controlled by different environmental parameters
<p>Ash dieback, induced by an invasive ascomycete, <em>Hymenoscyphus</em> <em>fraxineus</em>, has emerged in the late 1990s as a severe disease threatening ash populations in Europe. Future prospects for ash are improved by the existence of individuals with natural genetic resistance or tolerance to the disease and by limited disease impact in many environmental conditions where ash is common. Nevertheless, it was suggested that, even in those conditions, ash trees are infected and enable pathogen transmission. We studied the influence of climate and local environment on the ability of <em>H. fraxineus</em> to infect, be transmitted and cause damage on its host. We showed that healthy carriers, i.e. individuals showing no dieback but carrying <em>H. fraxineus</em>, exist and may play a significant role in ash dieback epidemiology. The environment strongly influenced <em>H. fraxineus</em> with different parameters being important depending on the life cycle stage. The ability of <em>H. fraxineus</em> to establish on ash leaves and to reproduce on the leaf debris in the litter (rachises) mainly depended on total precipitation in July-August and was not influenced by local tree cover. By contrast, damage to the host, and in particular shoot mortality was significantly reduced by high summer temperature in July-August and by high autumn average temperature. As a consequence, in many situations, ash trees are infected and enable H. fraxineus transmission while showing limited or even no damage. We also observed a decreasing trend of severity (leaf necrosis and shoot mortality probability) with the time of disease presence in a plot that could be significant for the future of ash dieback.</p>
Data Reproducibility Tree
<p>Decision Tree for dataset reproducibility presented in the paper <em>A Decision Tree to Shepherd Scientist through<br> the Data Reproducibility</em></p>
Enormously large tippers observed in southwest China. Can realistic 3-D EM modeling reproduce them?
<p>This dataset contains the observed tipper data and 3-D EM modeling results associated to the manuscript: Enormously large tippers observed in southwest China. Can realistic 3-D EM modeling reproduce them?</p>
ISMRM 2022/23 Reproducibility Team Challenge: Replication results for MReplIcators team
<p>This repository contains the <em><strong>MR</strong></em>epl<em><strong>I</strong></em>cators team’s replication attempt results from the 2022-23 International Society of Magnetic Resonance in Medicine (ISMRM) Challenge ‘Repeat It With Me: Reproducibility Team Challenge’ (<a href="https://challenge.ismrm.org/">ISMRM Challenge – ISMRM's Challenge Forums</a>).</p> <p>The challenge aims to “raise awareness of the importance of reproducible science through fostering collaboration between labs by reproducing ISMRM abstracts”.</p> <p>The Reproducer sub-team was formed by Ebony R. Gunwhy (ERG) and Jemima Pilgrim-Morris (JPM) from POLARIS (<a href="https://www.sheffield.ac.uk/polaris">POLARIS (Pulmonary, Lung and Respiratory Imaging Sheffield) | POLARIS | The University of Sheffield</a>) at The University of Sheffield and the team worked on reproducing abstract #1700 "Sensitivity Analysis of the Bloch Equations" (ISMRM 2022) by Author sub-team Nick Scholand and Martin Uecker from The Institute of Medical Engineering at Graz University of Technology, Austria and Institute for Diagnostic and Interventional Radiology University Medical Center, Göttingen (1).</p> <p>In this archive, a zipped results folder contains the independent results and notes from each member of the replicator sub-team (in ‘results/JPM’ and ‘results/ERG’, respectively). A more detailed overview of the abstract and replication (motivation, approach, challenges, and outcomes) can also be found in ‘Abstract replication details.pdf’. A shorter summary is provided here:</p> <p><em><strong>A successful replication ‘attempt’ was defined as…</strong></em></p> <ul> <li> <p>Installation of provided software</p> </li> <li> <p>Creation of simulations with the BART (2) simulation interface</p> </li> </ul> <p><em><strong>A fully successful replication was defined as…</strong></em></p> <ul> <li> <p>Successful recreation of the figures provided in the abstract</p> </li> <li> <p>Examining goodness-of-fit by matching the provided ground truth dataset to some tolerances (normalised root mean square error; NRMSE)</p> </li> <li> <p>Using quantitative measures of reproducibility (e.g., coefficient of variation) between original data computed by author sub-team and data computed by replicator sub-team</p> </li> <li> <p>Application of technique to further sequences and tissue/sequence parameters</p> </li> </ul> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p><em><strong>Contact between Replicator and Author sub-teams</strong></em></p> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p>An initial meeting was conducted to discuss how we should organise our participation as a team in this challenge. Some starting information was exchanged, in addition to the original abstract:</p> <p><strong>(A)</strong> Publicly published information about the studies:</p> <p>(i) arXiv preprint of more detailed manuscript (2)</p> <p>(ii) GitHub repository associated with the arXiv preprint, containing steps for reproducing the work relating to Figure 2 of the abstract (<a href="https://github.com/mrirecon/bloch-moba/tree/main/02_sens_analysis">https://github.com/mrirecon/bloch-moba/tree/main/02_sens_analysis</a>)</p> <p>(iii) GitHub repositories with tutorials on using the BART reconstruction toolbox (<a href="https://github.com/mrirecon/bart-workshop">https://github.com/mrirecon/bart-workshop</a>)</p> <p><strong>(B)</strong> The author sub-team also created an additional GitHub repository made publicly available, containing interactive Google Colab and Binder notebooks introducing BART and its simulation framework, and providing a more step-by-step guide to allow for an exact replication of the work in the abstract (<a href="https://github.com/mrirecon/ismrm-2022-sensitivity-analysis-bloch-eq">https://github.com/mrirecon/ismrm-2022-sensitivity-analysis-bloch-eq</a>). The author sub-team also uploaded the ground truth dataset (their own simulation results, located within ‘02_irbssfp/ref/’ and ‘03_unprep_irbssfp/ref/) from the abstract to this repository.</p> <p>In the first instance, the Replicator sub-team attempted a replication using only the publicly available information outlined in (A). Each member of the Replicator sub-team created their own GitHub forks of the repository listed in (A)(ii) to provide a starting point from which to document their replication from. These can be found at <a href="https://github.com/JemimaPM/bloch-moba-jpm">https://github.com/JemimaPM/bloch-moba-jp</a>m (JPM) and <a href="https://github.com/EbonyGunwhy/bloch-moba-erg/tree/MReplIcators">https://github.com/EbonyGunwhy/bloch-moba-erg/tree/MReplIcators</a> (ERG) and contain a commit history of all replication steps taken.</p> <p>The additional repository described in (B) was only used to download the ground truth dataset for quantitatively assessing the replication results. This limitation was imposed to allow the Replicator sub-team autonomy in reproducing the work and to aid in gaining a more thorough understanding of the methodology involved. It was agreed that if required, the replicator sub-team would take help from the extra repository described in (B) and provide feedback to the authors afterwards summarising what degree of information was useful for replicating the work. Further interaction was limited to creating issues on GitHub and to quick queries via email, as needed.</p> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p><em><strong>Expected outcomes</strong></em></p> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p>The original study was based on numerical experiments. Therefore, the results were expected to be directly reproducible, however it was expected that possible differences may occur due to numerical noise. The major reason for this is the single floating-point arithmetic used in the provided BART software. Its presence can be tested by reproducing the study results on various machines multiple times. Numerical noise is expected to change between different hardware. Therefore, both members of the Replicator sub-team attempted to independently replicate the results on their own hardware. The used hardware in this challenge is specified below.</p> <p><em>Replicators:</em></p> <p>ERG: Intel(R) Core(TM) i5-1145G7 CPU @ 2.60GHz (4 cores, 8 logical processors)</p> <p>JPM: Intel(R) Core(TM) i7-4790 CPU @ 3.60GHz (4 cores, 8 logical processors)</p> <p><em>Authors:</em></p> <p>Intel(R) Core(TM) i7-8565U CPU @ 1.80GHz (4 cores, 8 logical processors)</p> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p><em><strong>Replication results</strong></em></p> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p>Prior to starting the replication attempt, we agreed on the NRMSE tolerances below which we would define the replication as successful. These were NRMSE = 0.005 for the DQ derivative and NRMSE = 0.0001 for the SA derivative.</p> <p>Due to time constraints, only a direct replication was performed. This was deemed successful as both replicators were able to reproduce the results within these tolerances. As expected, differences occurred between hardware, with 0% NMRSE achieved in the replication attempt of JPM. In addition, the reproduced figures were qualitatively compared to the abstract figures and these were found to agree.</p> <p>Further notes and results from the Replicator sub-team attempts can be found within the ‘results’ folder for each respective replictor.</p> <p> </p> <p><strong><em>===========================================</em></strong></p> <p><em><strong>Carbon footprint</strong></em></p> <p>This algorithm runs in 0.08 mins on 4 CPUs and on:</p> <ul> <li> <p>Intel i5-10400F: draws 572.04 Wh. Based in the United Kingdom, this has a carbon footprint of 132.21 g CO2e, which is equivalent to 0.14 tree-months (<a href="http://calculator.green-algorithms.org//?runTime_hour=12&runTime_min=0&appVersion=v2.2&locationContinent=Europe&locationCountry=United%20Kingdom&locationRegion=GB&coreType=CPU&numberCPUs=4&CPUmodel=Core%20i5-10400F&memory=12&platformType=personalComputer&PSFradio=Yes&PSF=1">Green Algorithms i5-10400F (green-algorithms.org)</a>)</p> </li> <li> <p>Intel i7-4930K: draws 1.10 kWh. Based in the United Kingdom, this has a carbon footprint of 253.13 g CO2e, which is equivalent to 0.28 tree-months (<a href="http://calculator.green-algorithms.org//?runTime_hour=12&runTime_min=0&appVersion=v2.2&locationContinent=Europe&locationCountry=United%20Kingdom&locationRegion=GB&coreType=CPU&numberCPUs=4&CPUmodel=Core%20i7-4930K&memory=12&platformType=personalComputer&PSFradio=Yes&PSF=1">Green Algorithms i7-4930K (green-algorithms.org)</a>)</p> </li> </ul> <p>(calculated using green-algorithms.org v2.2 [1]. Note: at time of writing, the Green Algorithms calculator did not include our specific CPUs, therefore here we have calculated with similar processors. A request has been made on the Green Algorithms GitHub repository for our processors to be added in future).</p> <p><em><strong>===========================================</strong></em></p> <p><em><strong>References</strong></em></p> <p><em>1. Scholand N, & Uecker M. Sensitivity Analysis of the Bloch Equations [abstract]. In: Proceedings of the 31st Annual Meeting of ISMRM, London, 2022. Abstract nr 1700.</em></p> <p><em>2. Uecker M, Virtue P , Ong F, Murphy MJ, Alley MT, Vasanawala SS, Lustig M. Software Toolbox and Programming Library for Compressed Sensing and Parallel Imaging [abstract]. ISMRM Workshop on Data Sampling and Image Reconstruction, Sedona, 2013.</em></p> <p><em>3. Scholand N, Wang X, Roeloffs V, Rosenzweig S, Uecker M. Quantitative Magnetic Resonance Imaging by Nonlinear Inversion of the Bloch Equations. arXiv:2209.08027 [Preprint]. 2022. doi: <a href="https://doi.org/10.48550/arXiv.2209.08027">10.48550/arXiv.2209.08027</a></em></p> <p><em>4. Lannelongue L, Grealey J, Inouye M. Green Algorithms: Quantifying the Carbon Footprint of Computation. Adv. Sci. 2021;8(12):2100707. doi: <a href="https://doi.org/10.1002/advs.202100707">10.1002/advs.202100707</a></em></p> <p> </p> <p> </p>
Female great tits (Parus major) reproduce earlier when paired with a male they prefer
<p><span>Mate choice is a key component of reproductive biology. Females often prefer certain males but do females modulate their reproductive investment depending on whether they are mated with their preferred partner? We investigated this question in great tits (<em>Parus</em> <em>major</em>) where we subjected 36 females to a six-choice mate preference test. Male morphological traits and the female's own characteristics did not influence the preference females expressed. We did find that females spent significantly more time near more exploratory males. We then paired them with one of the males in aviaries and subsequently monitored their reproductive investment (through measurement of plasma 17β-oestradiol concentrations, first egg date, clutch size, and egg size). Females that were mated with a male for which they had a strong preference laid their first clutch significantly earlier in the season than females paired with a male they less preferred. Our results show that mate preference influences reproductive investment in great tits, thereby linking mate choice to bird reproductive decisions. </span></p>
Rat gadoxetate MRI signal dataset for the IMI-WP2-TRISTAN Reproducibility study
<p>Dataset to accompany the manuscript "Assessment of hepatic transporter function in rats using dynamic gadoxetate-enhanced MRI: A reproducibility study" by Ebony R. Gunwhy, Catherine D. G. Hines, Claudia Green, Iina Laitinen, Sirisha Tadimalla, Paul D. Hockings, Gunnar Schütz, J. Gerry Kenna, Steven Sourbron, and John C. Waterton (referred to as the 'Reproducibility' study).</p> <p><strong>data.zip</strong></p> <p>After extracting the contents within the data.zip folder, the MRI signal data acquired in this study are provided in csv format and can be found inside the <code>data/Reproducibility/01_signals</code> subfolder. Each csv file contains the liver and spleen region of interest (ROI) time curve data for a single rat from a specific substudy and day, after administration of the test compound of interest.</p> <p>The corresponding filename for each file is formatted as a string containing study descriptors (metadata) separated by underscores, i.e., filename = <code>substudy_</code><code>compound_site_RatNumber_dayNumber_dataType</code>, e.g., <code>1_Rifampicin_D_Rat5_2_Signals</code>.</p> <p><strong>results.zip</strong></p> <p>The results and figures relating to this manuscript were created using the outputs contained in <code>results/Reproducibility/2023-04-17</code>.</p> <p><code>01_model_ouputs</code> contains all outputs generated as a result of the tracer kinetic model fitting. Within this, plotted signal time curves for each acquistion per rat may be found in <code>figures/per_rat</code>, while average deltaR1 curves per substudy may be found in <code>figures/per_substudy</code>. All estimated parameter variables from the fitting are stored in <code>all_parameters.csv</code>. The folder <code>relaxation_rates_and_signals</code> contains the fitted MRI signal data in a similar format to the original csv data in <code>data/01_signals</code>. Additional columns have been included showing the MRI signals converted to R1. <code>fit_errors.txt</code> contains an ID list for any computational fitting errors found during quality control of the tracer kinetic modelling.</p> <p><code>02_analyses</code> contains results of the statistical analysis performed on the tracer kinetic modelling output. These are summarised in tabular csv format within the <code>repeatability/ </code>and <code>reproducibility/</code> folders, while graphical distributions are provided within the <code>figures/</code> folder. Summary results for benchmark values and from the mixed ANOVA are also provided in <code>benchmarks.csv </code>and<code> mixed_anova.csv</code>, respectively. Please see the manuscript for further details on these results.</p>
Reproducibility Package for Predictive Limitations of Physics-Informed Neural Networks in Vortex Shedding
<p>This archive contains the repro-pack for the paper at <a href="http://github.com/barbagroup/jcs_paper_pinn">https://github.com/barbagroup/jcs_paper_pinn</a>.</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.