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764 results for “Reproducibility”
A reproducible model for magnetosensitivity: earthworms in transparent soil reduce their cumulative movement in extremely weak magnetic field
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A Metric for Optimism: John Ioannidis on Reproducibility, Preregistration, and Data Sharing
<p><strong>Episode Summary:</strong></p> <p>In this episode we are discussing data sharing and Open Science. Our interview guest will be Stanford University Professor of Medicine: John Ioannidis who has now come to the Berlin Institute of Health as an Einstein BIH Visiting Fellow at the BIH QUEST Center to establish the Meta-Research Innovation Center Berlin (METRIC-Berlin), the European “sister” of the Meta-Research Innovation Center at Stanford (METRICS). We will cover his research and opinions on data sharing, reproducibility, and how to improve research.</p> <p><strong>Links: </strong></p> <p><a href="https://profiles.stanford.edu/john-ioannidis">John Ioannidis</a></p> <p><a href="https://www.bihealth.org/en/research/quest-center/mission-approaches/">BIH Quest Centre</a></p> <p><a href="https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124">Why Most Published Research Findings Are False</a></p> <p><strong>Quotes:</strong></p> <p>'I think that scientists, by themselves, are recognizing that it is important to share [data] and in many fields, like in Genetics, they realize that unless they share they cannot really go very far'</p> <p>'Clearly over the years we have seen more scientific sharing of data'</p>
Investigating the Reproducibility of NPM Packages
<p>Open-source Dataset</p> <p><br> This dataset contains the NPM packages that we built using our tool-chain. It consists of the diffoscope outputs, the versions built by our tool-chain, and the pre-built packages present on the npmjs registry.</p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Valdez ASCII
<p>Multiscale elevation models centered on Valdez, Alaska, USA</p> <p>Resolutions: 3.3, 7.5, 15, 30, 90, 250, 500, 1,000, and 2,000 meters, 1500 x 1,500 height samples each</p> <p>File format: Esri ASCII grid</p> <p>When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Massanutten Mountain GeoTIFF
<p>An elevation model of Massanutten Mountain, Virginia, USA</p> <p>Landform features: folded ridges, hogback, water gap, meander</p> <p>Resolution: 10 meter, 3,900 x 3,900 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Gore Range GeoTIFF
<p>Multiscale elevation models centered on Gore Range, Colorado, USA</p> <p>Resolutions: 1, 5, 15, 30, 90, 250, 500, 1,000, 2,000, 2,500, and 5,000 meters, 1500 x 1,500 height samples each</p> <p>File format: GeoTIFF</p> <p>When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Sandhills GeoTIFF
<p>An elevation model of Sandhills, Nebraska, USA</p> <p>Landform features: stabilized dune field</p> <p>Resolution: 10 meter, 4,500 x 4,500 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Jackson Hole (riverbed) GeoTIFF
<p>An elevation model of Jackson Hole, Wyoming, USA</p> <p>Landform features: braided river, fluvial terrace</p> <p>Resolution: 2 meter, 4,200 x 4,200height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Sandhills ASCII
<p>An elevation model of Sandhills, Nebraska, USA</p> <p>Landform features: stabilized dune field</p> <p>Resolution: 10 meter, 4,500 x 4,500 height samples</p> <p>File format: Esri ASCII grid</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>Version 1.0.1 removes empty space characters from the file header, which prevented the file from being opened by some software.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Gore Range ASCII
<p>Multiscale elevation models centered on Gore Range, Colorado, USA</p> <p>Resolutions: 1, 5, 15, 30, 90, 250, 500, 1,000, 2,000, 2,500, and 5,000 meters, 1500 x 1,500 height samples each</p> <p>File format: Esri ASCII grid</p> <p>Version 1.1.0 does not contain GeoTIFF files that were included by mistake in version 1.0.0.</p> <p>When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Crater Lake ASCII
<p>An elevation model of Crater Lake, Oregon, USA</p> <p>Landform features: caldera, cinder cone, lava flow</p> <p>Resolution: 3.33 meter, 5,200 x 5,200 height samples</p> <p>File format: Esri ASCII grid</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>Version 1.0.1 removes empty space characters from the file header, which prevented the file from being opened by some software.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Jackson Hole (riverbed) ASCII
<p>An elevation model of Jackson Hole, Wyoming, USA</p> <p>Landform features: braided river, fluvial terrace</p> <p>Resolution: 2 meter, 4,200 x 4,200 height samples</p> <p>File format: Esri ASCII grid</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>Version 1.0.1 removes empty space characters from the file header, which prevented the file from being opened by some software.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Great Sand Dunes ASCII
<p>An elevation model of Great Sand Dunes, Colorado, USA</p> <p>Landform features: active dune field, sand sheet, sabkha</p> <p>Resolution: 3.3 meter, 5,300 x 5,300 height samples</p> <p>File format: Esri ASCII grid</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>Version 1.0.1 removes empty space characters from the file header, which prevented the file from being opened by some software.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
GHTraffic: A Dataset for Reproducible Research in Service-Oriented Computing
<p>This is the latest version of the GHTraffic project. The main aim is to model a variety of transaction sequences to reflect more complex service behaviour.</p> <p>It has two editions: Small (S) and Large (L) where the records were created by selecting the same repositories as the original Small and Large datasets. The newest S dataset contains records from <a href="https://github.com/google/guava">google/guava</a> repository. The L dataset contains records from eight repositories (i.e., <a href="https://github.com/twbs/bootstrap">twbs/bootstrap</a>, <a href="https://github.com/symfony/symfony">symfony/symfony</a>, <a href="https://github.com/docker/docker">docker/docker</a>, <a href="https://github.com/Homebrew/homebrew">Homebrew/homebrew</a>, <a href="https://github.com/rust-lang/rust">rust-lang/rust</a>, <a href="https://github.com/kubernetes/kubernetes">kubernetes/kubernetes</a>, <a href="https://github.com/rails/rails">rails/rails</a>, and <a href="https://github.com/angular/angular.js">angular/angular.js</a>). </p> <p>The entire data generation process is quite similar to the original GHTraffic design. But it incorporates minor changes to the process of synthetic data generation where it uses a random date after successfully posting a resource to make up the request and response for all of the HTTP methods. It also adds yet another subset of unsuccessful transactions by stipulating requests before resource creation is successful.</p> <p>This results in a far more dynamic series of transactions to named resources.</p> <p>Scripts used for datasets construction are accessible from the <a href="https://bitbucket.org/tbhagya/ghtraffic-version-2.0.0">repository</a>.</p>
Replicability and Reproducibility of a Schema Evolution Study in Embedded Databases
<p>Archives containing datasets, scripts, and instructions for reproducing a study.</p>
ISMRM Reproducible Research Study Group: Data for the paper "CG-SENSE revisited: Results from the first ISMRM reproducibility challenge"
<p>Challange data (brain/heart) and supplementary data (cardiac/rawdata_sprial) for the paper "CG-SENSE revisited: Results from the first ISMRM reproducibility challenge".</p>
data set related to article Echo-time dependency of quantitative susceptibility mapping reproducibility at different magnetic field strengths
<p>This record contains raw date related to article Echo-time dependency of quantitative susceptibility mapping reproducibility at different magnetic field strengths</p>
Making plasma science more open, collaborative, and reproducible
<p>The reproducibility crisis of modern science is the inability of scientists to reproduce roughly half of the results published in scientific journals. This crisis has affected a broad range of fields, such as psychology, chemistry, and oncology. While physicists tend to have high confidence that physics research is reproducible, no comprehensive studies have been performed to support or refute this claim. Nevertheless, the scientific, cultural, and institutional practices that contribute to the reproducibility crisis in other fields are also present in plasma science. This tutorial will describe how to implement best practices for scientific reproducibility into plasma research. The talk will begin by outlining sources of irreproducibility, such as cognitive biases, improper use of statistics, publication bias, closed access policies for data and software, and the reward system for modern academia. The talk will then describe remedies for these problems such as open access data policies; open metadata standards; open source software; training on proper use of statistics; pre-registration of research methodologies; independent methodological and statistical support; and valuing the reproducibility of research in tenure, hiring, and funding decisions.</p>
R code, spatial and tabular data to fully reproduce STEPS simulations of population change for common brushtail possum, grassland melomys and northern brown bandicoot in northern Australia
<ol> <li> <p>The development of effective fire management for biodiversity conservation is a global challenge. The highly dynamic nature of fire, the difficulty in replicating 'real-world' fire experiments, and the need to understand population changes at large spatiotemporal scales make computer simulations particularly useful for identifying optimal fire management regimes for biodiversity conservation. </p> </li> <li> <p>We aimed to develop a flexible modelling approach with which to investigate how the spatiotemporal application of fire (i.e. management scenarios) influences savanna biodiversity. We used existing data from a landscape-scale fire experiment to develop population simulations for the common brushtail possum (<i>Trichosurus vulpecula</i>), grassland melomys (<i>Melomys burtoni</i>) and northern brown bandicoot (<i>Isoodon macrourus</i>) across the Kapalga area of Kakadu National Park in northern Australia. We simulated how populations were expected to change between 1995 and 2015 in response to the fire patterns observed at Kapalga over this period, and under a hypothetical management scenario of extensive prescribed burning.</p> </li> <li> <p>Our models predicted a substantial decline in all three species in response to the observed fire regime at Kapalga, suggesting that the fire patterns observed at Kapalga, with the associated mechanisms and interactions with other ecological processes, were not conducive with the persistence of native mammal populations. </p> </li> <li> <p>Our prescribed burning scenario had little effect on the predicted population trajectory of the common brushtail possum and grassland melomys, but markedly improved the population trajectory of the northern brown bandicoot. These inconsistencies highlight the need for a nuanced approach to fire management across northern Australian savannas, that is tailored to local conditions and management objectives. </p> </li> <li> <p>Synthesis and applications. The modelling approach outlined here, provides a basis for identifying fire patterns that are beneficial for conserving biodiversity, thereby increasing our capacity to establish clear targets for prescribed fire management. Importantly, this approach is flexible and can be easily adapted to other taxa and fire-prone ecosystems.</p> </li> </ol>
Files for reproducing results in Octo-Tiger: a new, 3D hydrodynamic code for stellar mergers that uses HPX parallelisation
<p>This archive contains config files for reproducing results in the paper "Octo-Tiger: a new, 3D hydrodynamic code for stellar mergers that uses HPX parallelisation"</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.