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Figure 8 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 8 The final composite, sieved water mite sample ready for the picking process.
StreamFlow run of digital pathology tissue/tumor prediction workflow
<p>This dataset is an <a href="https://www.researchobject.org/ro-crate/">RO-Crate</a> representation of an execution of the tissue/tumor prediction workflow for digital pathology from <a href="https://github.com/crs4/deephealth-pipelines/tree/c54840df08742e3aa454394e0e74d15fbd640f07">crs4/deephealth-pipelines</a>. It follows the <a href="https://w3id.org/ro/wfrun/provenance/0.1">Provenance Run Crate</a> profile. The workflow has been run with <a href="http://streamflow.di.unito.it">StreamFlow</a>, using the following commands to produce an RO-Crate bundle:</p> <pre><code class="language-bash"># Run the pipeline streamflow run \ --name ml-predict-pipeline-streamflow \ streamflow.yml # Generate the RO-Crate bundle streamflow prov \ --add-file src=README.md,dst=/README.md,about="{\"@id\":\"./\"}",encodingFormat=text/markdown \ --add-property \./.license=https://spdx.org/licenses/MIT \ --add-property \./.name="DeepHealth Pipeline" \ --add-property \./.description="Run of digital pathology tissue/tumor prediction workflow" \ --file streamflow.yml \ ml-predict-pipeline-streamflow</code></pre> <p>The input dataset is <a href="https://openslide.cs.cmu.edu/download/openslide-testdata/Mirax/Mirax2-Fluorescence-2.zip">Mirax2-Fluorescence-2</a> by Yves Sucaet, from the <a href="https://openslide.cs.cmu.edu/download/openslide-testdata/Mirax/">MIRAX test data</a>.</p>
Ptatin2D : dataset for run simulations presented in the article Slab pull driven South China Sea opening implies a Mesozoic Proto South China Sea
<p>Overview of pTatin2D</p> <p>pTatin2D is a software package designed for studying long time-scale processes relevant to geodynamics. The original motivation for this development was to provide the community with an open-source toolkit capable of studying high-resolution, two-dimensional models of lithospheric deformation.</p> <p>Functionality</p> <ul> <li>mixed finite elements (Q2-P1_disc) for the Stokes problem</li> <li>material points for tracking Lagrangian state and history variables</li> <li>energy equation solved with Q1 elements + SUPG</li> <li>ALE formulations (with a variety of remeshing solutions)</li> <li>extensible rheology components (currently supports: iso-viscous, Frank-Kamenetskii, Arrhenius, power-law, von Mises, Drucker Prager)</li> <li>full support for spatio-temporal Dirichlet and non-zero Neumann boundary conditions</li> <li>coupling with landscape evolution models</li> <li>full support for Newton and Picard non-linear solvers</li> <li>massively parallel implementation</li> </ul> <p>pTatin2D leverages functionality from <a href="http://www.mcs.anl.gov/petsc">PETSc</a></p> <p>Requirements</p> <ul> <li>PETSc (only support for version 3.2)</li> </ul>
Replication data for Temperature variability and long-run economic development
<p><strong>Description</strong></p> <p>This dataset contains the processed data used for the statistical analysis in Linsenmeier, M. (2023): Temperature variability and long-term economic development, published in the Journal of Environmental Economics and Management.</p> <p>The main data on nightlights stem from the satellites of the Visible Infrared Imaging Radiometer Suite (VIIRS). The data are downloaded as annual composites (vcm) of version V1 (Elvidge et al., 2017). For robustness tests, also annual composites of version V2 are used (Elvidge et al., 2021). Additional data on nightlights are taken from the U.S. Air Force Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) of Version 4. Data on the global distribution of crop land and pasture land (Ramankutty et al., 2008) are taken from NASA (Ramankutty et al., 2010). Data on population are from the Gridded Population of the World (GPW) dataset version 4.0 (CIESIN, 2018). Data on elevation are from the Global Land One-kilometer Base Elevation (GLOBE) dataset in version 1 provided by the National Oceanic and Atmospheric Administration (NOAA) (Hastings et al., 1999). Data on terrain ruggedness are from a global dataset with a resolution of 1 km (Shaver et al., 2018). Data on distances from the nearest coast are from NASA. Distance from inland water bodies are from the GloboLakes dataset (Carrea et al., 2015). Finally, data on weather are from ERA5 reanalysis (Hersbach et al. 2018). All datasets are spatially aggregated to the grid cells of the ERA5 reanalysis.</p> <p><strong>Acknowledgements</strong></p> <p>The data contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p>Image and data processing by Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines. DMSP data collected by US Air Force Weather Agency.</p> <p><strong>Bibliography</strong></p> <ul> <li>Carrea, L., Embury, O., andMerchant, C.(2015). GloboLakes: High-resolution global limnology data. Center for Environmental Data Analysis. http://catalogue.ceda.ac.uk/uuid/06cef537c5b14a2e871a333b9bc0b482.</li> <li>CIESIN (2018). Gridded Population of the World, Version 4 (GPWv4): Population Count, Revision 11.</li> <li>Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., and Ghosh, T. (2017). VIIRS night-time lights. International Journal of Remote Sensing, 38(21):5860–5879.</li> <li>Elvidge, C.D, Zhizhin, M., Ghosh T., Hsu FC, Taneja J. Annual time series of global VIIRS nighttime lights derived from monthly averages:2012 to 2019. Remote Sensing 2021, 13(5), p.922, doi:10.3390/rs13050922.</li> <li>Hastings, D. A., Dunbar, P. K., Elphingstone, G. M., Bootz, M., Murakami, H., Maruyama, H., Masaharu, H., Holland, P., Payne, J., Bryant, N. A., et al. (1999). The global land one-kilometer base elevation (GLOBE) digital elevation model, version 1.0. National Oceanic and Atmospheric Administration, National Geophysical Data Center, 325.</li> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed in January 2021), 10.24381/cds.adbb2d47</li> <li>Ramankutty, N., Evan, A., Monfreda, C., and Foley, J. (2010). Global Agricultural Lands: Pastures, 2000. Global Agricultural Lands Dataset.</li> <li>Ramankutty, N., Evan, A. T., Monfreda, C., and Foley, J. A. (2008). Farming the Planet: 1. Geographic Distribution of Global Agricultural Lands in the Year 2000: Global Agricultural Lands in 2000. Global Biogeochemical Cycles, 22(1).</li> <li>Shaver, A., Carter, D. B., and Shawa, T. W. (2018). Terrain ruggedness and land cover: Improved data for most research designs. Terrain Ruggedness and Land Cover Dataset.</li> </ul>
MSFragger open searches of proteome shotgun and phosphoproteomic runs PXD013868 (Mergner et al., 2020)
<p>MSFragger open searches of phosphoproteomics and shotgun proteome runs of large-scale tissue atlas in Arabidopis (Mergner et al., 2020). Part of the Plant PTM Viewer 2.0 update paper.</p>
Results for plan4EU runs in Case STudy 4 of the Open ENTRANCE project
<p>This dataset contains scenarios results for Case Study 4 of the openENTRANCE project.</p> <p>Visit the openENTRANCE Scenario Explorer at <a href="https://data.ene.iiasa.ac.at/openentrance">https://data.ece.iiasa.ac.at/openentrance</a> for more information.</p>
MAgPIE model runs csv for plotting: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections
<p>This .zip file contains the data used to create the figures for the paper. It includes .csv files and .nc files for maps. This version includes additional files like the mapping between countries and MAgPIE're economic regions.</p>
Data to run Hy2DL code
<p>Data necessary to run the Hy2DL code (https://doi.org/10.5281/zenodo.8289020). One must download the data, unzip the file, and paste it into the 'data' folder of the Hy2DL repository. </p>
lemmi16s instances and runs using GTDB 20230913
<p>Configuration files necessary to reproduce the content of all the benchmarks published on <a href="https://lemmi16s.ezlab.org">https://lemmi16s.ezlab.org</a> on 2023/09/13, using version 1.0.0 of the platform and GTDB.</p>
lemmi16s instances and runs using SILVA 20230913
<p>Configuration files necessary to reproduce the content of all the benchmarks published on <a href="https://lemmi16s.ezlab.org">https://lemmi16s.ezlab.org</a> on 2023/09/13, using version 1.0.0 of the platform and SILVA.</p>
Data from: Navigating Polycrisis: long-run socio-cultural factors shape response to changing climate
<p>These data are part of a data portal that accompanies the special issue 'Climate change adaptation needs a science of culture,' published in Philosophical Transactions of the Royal Society B in 2023. To access the data portal, please visit <a href="https://doi.org/10.5061/dryad.bnzs7h4h4"><strong>10.5061/dryad.bnzs7h4h4</strong></a>. We have systematically collected information about the nature and consequences of societal crisis in over 150 cases covering different regions in the world in the preindustrial period. In this contribution, we discuss some critical insights arising from initial analysis of this material, which has informed the basis of our work.</p>
Data for running WHAM! v1.0
<p>These files support running of the Wildfire Human Agency Model (WHAM!) v1.0.</p><p>Code to run the model is available at: https://zenodo.org/doi/10.5281/zenodo.8319310</p>
Results and log of LLM-KG-Bench runs described in article "Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?", Frey et al. 2023
<p>Results and log of LLM-KG-Bench runs described in article ""Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?", Frey et al. 2023, to appear in proceedings for workshop DL4KG@ISWC 2023.</p> <p>For data on task FactExtractStatic please contact authors.</p>
Artifact Demonstration Video for Negotiating Autonomy in Cyber-Physical Systems at Run Time
<p>This artifact demonstration video shows a novel approach to dynamically negotiate autonomy in Cyber-Physical Systems (CPS). As CPS become increasingly integrated into our daily lives, sharing autonomy between human operators and autonomous agents within these systems becomes a critical issue. Business case developers also face the design and integration of CPS. Our research presents a novel architecture and methodology that seamlessly enables this sharing of autonomy. We aim to encourage business case developers to consider autonomic CPS. The video provides an insightful overview of our approach and highlights its practicality. It illustrates how our system enables business case developers to back CPS with a mechanism to negotiate and share autonomy at runtime. Our artifact demonstration video is not only a good opportunity to visually grasp the concept of negotiating autonomy in CPS, but also to experience the practical implementation of this approach. It serves as a valuable resource for researchers, engineers, and anyone interested in understanding the future of sharing autonomy in the context of cyber-physical systems.</p>
PepNet, PointNovo, and DeepNovo running results on PXD014877 and PXD019483 and DIA dataset
<p>PepNet, PointNovo, and DeepNovo running results on PXD014877 and PXD019483 and DIA dataset</p><p>For publication: Accurate De Novo Peptide Sequencing Using Fully Convolutional Neural Networks </p>
A Study of Weekly Tisotumab Vedotin for Patients With Platinum-Resistant Ovarian Cancer With Safety Run-in (innovaTV 208)
ClinicalTrials.gov study NCT03657043. IPD Sharing: Not stated. Countries: 6. Publications: 1.
Custom Molded Foot Orthoses Effect on Foot Kinematics and Lower Extremity Electromyography During Walking and Running.
ClinicalTrials.gov study NCT02143947. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Running Retraining to Minimize Braking Forces
ClinicalTrials.gov study NCT03302975. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Evaluation of a Modified Running-specific Prosthetic Foot
ClinicalTrials.gov study NCT02440711. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Combined tDCS and Pneumatic Compression for Recovery After a 10K Run
ClinicalTrials.gov study NCT07333404. IPD Sharing: NO. Countries: 1. Publications: 2.
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.