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Dataset results
764 results for “Reproducibility”
Demonstration of Scientific Workflow Reproducibility with Hyperflow Workflow Management System
<p>This package contains the Experiment Digital Object that allows to reproduce the workflow execution experiment.</p> <p>The content of the package:<br> - Information about the experimental workflow (below).<br> - Experiment execution traces in the form of a dataframe (csv file) with description of the format (below).<br> - Visualization of the execution (png file).<br> - Python script for analysis of the execution trace (generates the visualization). <br> - Instructions describing how to reproduce the experiment (below).</p>
Princeton Handbook for Reproducible Neuroimaging: Sample Data
<p>This archive contains a raw DICOM dataset acquired (with informed consent) using the <a href="https://github.com/ReproNim/reproin">ReproIn</a> naming convention on a Siemens Skyra 3T MRI scanner. The dataset includes a T1-weighted anatomical image, four functional runs with the “prettymouth” spoken story stimulus, and one functional run with a block design emotional faces task, as well as auxiliary scans (e.g., scout, soundcheck). The “prettymouth” story stimulus created by <a href="https://doi.org/10.1177%2F0956797616682029">Yeshurun et al., 2017</a> and is available as part of the <a href="https://openneuro.org/datasets/ds002345">Narratives</a> collection, and the emotional faces task is similar to <a href="https://doi.org/10.1016/j.nicl.2015.05.004">Chai et al., 2015</a>. These data are intended for use with the <a href="https://brainhack-princeton.github.io/handbook/">Princeton Handbook for Reproducible Neuroimaging</a>. The handbook provides guidelines for BIDS conversion and execution of BIDS apps (e.g., fMRIPrep, MRIQC). The brain data are contributed by author S.A.N. and are authorized for non-anonymized distribution.</p>
Reproducibility archive for MeDIP analyses of plasma DNA from brain tumour patients.
<p>This contains the starting points, intermediate objects, and the code used to produce them that were used to generate the figures in the associated paper. For execution, please extract the contents of both the data and the markdowns/scripts folder into the same folder. </p>
reproducible_research_data
<p>Data for the final project on reproducible research course at Unicamp (2020/1st)</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>
Reproducible Validation and Replication Studies in Nanoscale Physics (problem datasets for Rockstuhl et al. 2005 replication)
<p>Problem folders including all the input files necessary to reproduce the computations of the results related to Rockstuhl et al. 2005 on the paper: Reproducible Validation and Replication Studies in Nanoscale Physics</p>
EveOut: Reproducible Event Dataset for Studying and Analyzing the Complex Event-Outlet Relationship
<p>We present a dataset of 77,545 news events collected between January 2019 and May 2020. We selected the top five news outlets based on Alexa Global Rankings and retrieved all the events reported in English by these outlets using the Event Registry API. Our dataset can be used as a resource to analyze and learn the relationship between events and their selection by the chosen outlets. It is primarily intended to be used by researchers studying bias in event selection. However, it may also be used to study the geographical, temporal, categorical and several other aspects of the events. We demonstrate the value of the resource in developing novel applications in the digital humanities with motivating use cases such as the Outlet Prediction task given the event details and analysis of event-selection bias.</p>
Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)
<p>Replication dataset for experiments performed in MLSys 2021 submission: "Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)"</p> <p>(currently using a placeholder to acquire link for submission, will update with dataset)</p>
Dataset to reproduce MD simulations described in " Insights on the dynamics of the human zinc transporter ZnT8 by MD simulations"
<p>Second version includes full length 6xpf-based models and corresponding MD simulations.</p>
Data and code - Reproducibility improves exponentially over 63 years of research - Minocher et al. 2020
<p>Data and code to reproduce analyses in the publication - Minocher, et al. "Reproducibility improves exponentially over 63 years of social learning research". </p> <p>This repository is maintained at github https://github.com/rianaminocher/reproducibility-analysis.</p>
Reproducibility Package for TACAS'21 Paper Resilient Capacity-Aware Routing
<p>This package contains all the necessary information for the reproduction of the experimental results in the paper "Resilient Capacity-Aware Routing" accepted for TACAS'21. In particular we provide all the python scripts that we used, their dependencies and the shell scripts for running the experiments or its subset.</p>
Reproducible in-silico omics analyses - GSE37703: Differential analysis of HOXA1 in adult cells dataset
<p>GSE37703: Differential analysis of HOXA1 in adult cells at isoform resolution by RNA-Seq’ for quantification by Kallisto and differential abundance with Sleuth dataset used for the "Reproducible in-silico omics analyses across clouds and clusters" paper.</p>
Reproducible in-silico omics analyses - Supplementary Figure 3
<p>Supplementary Figure 3. Interleaved output of two RAxML Phylogenetic Trees of the same sequences estimated on Mac OSX (blue) and Amazon Linux (red). Differences in the branch lengths of resulting trees are shown in color. No such differences were observed when running a Nextflow dockerized version of the same command.</p>
Reproducible in-silico omics analyses - Main figure
<p>Figure 1: Nextflow produces stable analysis across different platforms. (a) Leishmania infantum clone JPCM5 genome annotation was predicted using a native and a dockerized (Debian Linux) version of the Companion eukaryotic annotation pipeline. The native and dockerized versions were run on Mac OSX and Amazon Linux platforms. The Venn diagram shows the existence of small, but significant discrepancies when comparing the genomic coordinates of predicted coding genes and non-coding RNAs, with some of these disparities including entire genes. (b) Results were deterministic on each platform, and totally identical readouts were measured when deploying the dockerized version. (c) A similar comparison carried out on a Kallisto/Sleuth pipeline when looking for differentially expressed genes (q-value <0.01) in an RNA-seq experiment collected from human lung fibroblasts reveals a comparable fluctuation between the Mac OSX and the Amazon Linux platform. Similarly, in this case both platforms produce identical readouts when deploying the dockerized version of the pipeline.<br> </p>
Reproducible in-silico omics analyses - Supplementary Figure 2
<p>Supplementary Figure 2. Kallisto Nextflow pipeline. The native Kallisto pipeline is converted to Nextflow and composed of three processes. The first two processes call Kallisto to index the transcriptome and then pseudo-map for RNA-seq quantification, and the third one for Sleuth to perform differential expression analysis.</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>
Raw data of the experiments in "Examining the Reproducibility of Using Dynamic Loop Scheduling Techniques in Scientific Applications" (REPPAR workshop at IPDPS 2017)
<p>Raw data of the experiments in "Examining the Reproducibility of Using Dynamic Loop Scheduling Techniques in Scientific Applications" (REPPAR workshop at IPDPS 2017)</p>
Raw data of the experiments in "Towards the Reproducibility of Using Dynamic Loop Scheduling Techniques in Scientific Applications" (ISPDC 2017)
<p>Raw data of the experiments in "Towards the Reproducibility of Using Dynamic Loop Scheduling Techniques in Scientific Applications" (ISPDC 2017)</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>
AUTOENCODIX raw data for reproducibility
<p>Raw data files for reproducibility of Experiments related to AUTOENCODIX publication and repository: https://github.com/jan-forest/autoencodix-reproducibility </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.