Find research datasets worth reusing

opencc-by-4.0Dec 2022View details →

Data and materials for "The Consequences of Data Dispersion in Genomics: A Comparative Analysis of Data Sources for Precision Medicine" manuscript"

<p>Data and sripts for the "The Consequences of Data Dispersion in Genomics: A Comparative Analysis of Data Sources for Precision Medicine" manuscript" manuscript, sent to BMC Bioinformatics</p>

Small-angle Scattering Data Analysis Round Robin dataset: original for participants.

<p>These are four datasets that were made available to the participants of the Small-angle Scattering data analysis round robin. The intent was to find out how comparable results from different researchers are, who analyse exactly the same processed, corrected dataset. </p> <p>In this repository, there are:<br> 1) a PDF document with more details for the study,<br> 2) the datasets for people to try and fit<br> 3) an Excel spreadsheet to document the results.</p> <p>Datasets 1 and 2 were modified from: Deumer, Jerome, & Gollwitzer, Christian. (2022). npSize_SAXS_data_PTB (Version 5) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5886834</p> <p> </p> <p>Datasets 3 and 4 were collected in-house on the MOUSE instrument, detailed <a href="https://dx.doi.org/10.1088/1748-0221/16/06/P06034">here</a></p>

Phantom imaging data and analysis macros for the article "Monochromatic computed tomography using laboratory-scale setup"

<p>The raw and processed data and analysis macros of the article <em>A.-P.</em> <em>Honkanen et S. J. Huotari, Monochromatic computed tomography using laboratory-scale setup, Scientific Reports (2023), doi:<a href="http://doi.org/10.1038/s41598-023-27409-6">10.1038/s41598-023-27409-6</a></em></p> <p>The data set consists of the raw and reconstructed computed tomography projection data taken of an PMMA phantom embedded with three different chemical species of selenium taken with a monochromatic X-ray imaging setup based on a laboratory-scale Johann-type crystal X-ray spectrometer. In addition to the imaging data, the set contains also the Jupyter Notebooks used to process and analyse the data. The details of the instrument and the analysis are presented in the article.</p> <p>The dataset is licensed under Creative Commons Attribution 4.0 International License <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>

Small-angle Scattering Data Analysis Round Robin: anonymized results, figures and Jupyter notebook

<p>The intent of this round robin was to find out how comparable results from different researchers are, who analyse exactly the same processed, corrected dataset.</p> <p>This zip file contains the anonymized results and the jupyter notebook used to do the data processing, analysis and visualisation. Additionally, TEM images of the samples are included. </p>

opencc-by-4.0Dec 2021View details →

Data analysis of LiP-MS data for high-throughput applications

<p>Proteins regulate biological processes by changing their structure or abundance to accomplish a specific function. In response to any perturbation or stimulus, protein structure may be altered by a variety of molecular events, such as post translational modifications, protein-protein interactions, aggregation, allostery, or binding to other molecules. The ability to probe these structural changes in thousands of proteins simultaneously in cells or tissues can provide valuable information about the functional state of a variety of biological processes and pathways. Here we present an updated protocol for LiP-MS, a proteomics technique combining limited proteolysis with mass spectrometry, to detect protein structural alterations in complex backgrounds and on a proteome-wide scale (Cappelletti et al., 2021; Piazza et al., 2020; Schopper et al., 2017). We describe advances in the throughput and robustness of the LiP-MS workflow and implementation of data-independent acquisition (DIA) based mass spectrometry, which together achieve high reproducibility and sensitivity, even on large sample sizes.  In addition, we introduce MSstatsLiP, an R package dedicated to the analysis of LiP-MS data for the identification of structurally altered peptides and differentially abundant proteins. Altogether, the newly proposed improvements expand the adaptability of the method and allow for its wide use in systematic functional proteomic studies and translational applications. </p>

Immersive haptic simulation for training nurses in emergency medical procedures - Data collected and statistical analysis

<p>Data collected during the evaluation presented in "Haptic simulation for emergency procedures in nursing training" paper.</p> <table> <caption>HR ALL</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>2.857</td> <td>29</td> <td>0.008</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-8.089</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>7.567</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-2.962</td> <td>29</td> <td>0.006</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em>  Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR FIRST MANN</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>1.665</td> <td>14</td> <td>0.118</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-7.104</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>6.498</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-1.461</td> <td>14</td> <td>0.166</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em>  Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR FIRST VR</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>2.341</td> <td>14</td> <td>0.035</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-4.612</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>4.482</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-2.688</td> <td>14</td> <td>0.018</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em>  Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR BETWEEN GROUPS</caption> <thead> <tr> <th> </th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-1.958</td> <td>28</td> <td>0.060</td> </tr> <tr> <td>Mann post HR</td> <td>-1.902</td> <td>28</td> <td>0.068</td> </tr> <tr> <td>VR pre HR</td> <td>-4.013</td> <td>28</td> <td>< .001</td> </tr> <tr> <td>VR post HR</td> <td>-2.344</td> <td>28</td> <td>0.026</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em>  Independent samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>Physiological T-Test results for the participants who started the experiment performing the procedure in the mannequin.</caption> <thead> <tr> <th>First variable</th> <th>μ</th> <th>σ</th> <th>Second variable</th> <th>μ</th> <th>σ</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>SBP pre-mannequin</td> <td>128.333</td> <td>10.715</td> <td>SBP pre-simulator</td> <td>134.533</td> <td>11.819</td> <td>-1.870</td> <td>14</td> <td>0.083</td> </tr> <tr> <td>SBP post-mannequin</td> <td>125.600</td> <td>11.648</td> <td>SBP post-simulator</td> <td>131.467</td> <td>14.643</td> <td>-2.094</td> <td>14</td> <td>0.055</td> </tr> <tr> <td>DBP pre-mannequin</td> <td>80.133</td> <td>5.527</td> <td>DBP pre-simulator</td> <td>81.533</td> <td>9.039</td> <td>-0.623</td> <td>14</td> <td>0.544</td> </tr> <tr> <td>DBP post-mannequin</td> <td>78.667</td> <td>6.956</td> <td>DBP post-simulator</td> <td>81.400</td> <td>8.475</td> <td>-2.073</td> <td>14</td> <td>0.057</td> </tr> <tr> <td>HR pre-mannequin</td> <td>92.133</td> <td>14.837</td> <td>HR pre-simulator</td> <td>75.733</td> <td>9.9625</td> <td>6.498</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>HR post-mannequin</td> <td>87.400</td> <td>9.132</td> <td>HR post-simulator</td> <td>91.400</td> <td>14.217</td> <td>-1.461</td> <td>29</td> <td>0.166</td> </tr> </tbody> </table> <p>SBP = Systolic blood pressure. DBP = Diastolic blood pressure. HR = Heart Rate.</p> <table> <caption>Physiological T-Test results for the participants who started the experiment performing the procedure in the ParaVR simulator.</caption> <thead> <tr> <th>First variable</th> <th>μ</th> <th>σ</th> <th>Second variable</th> <th>μ</th> <th>σ</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>SBP pre-mannequin</td> <td>119.067</td> <td>12.898</td> <td>SBP pre-simulator</td> <td>130.600</td> <td>12.188</td> <td>-3.799</td> <td>14</td> <td>0.002</td> </tr> <tr> <td>SBP post-mannequin</td> <td>117.533</td> <td>13.410</td> <td>SBP post-simulator</td> <td>128.200</td> <td>13.385</td> <td>-4.022</td> <td>14</td> <td>0.001</td> </tr> <tr> <td>DBP pre-mannequin</td> <td>76.533</td> <td>8.943</td> <td>DBP pre-simulator</td> <td>80.200</td> <td>6.899</td> <td>-1.815</td> <td>14</td> <td>0.091</td> </tr> <tr> <td>DBP post-mannequin</td> <td>74.333</td> <td>8.541</td> <td>DBP post-simulator</td> <td>79.133</td> <td>7.864</td> <td>-2.003</td> <td>14</td> <td>0.065</td> </tr> <tr> <td>HR pre-mannequin</td> <td>102.067</td> <td>12.876</td> <td>HR pre-simulator</td> <td>91.533</td> <td>11.825</td> <td>4.482</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>HR post-mannequin</td> <td>95.867</td> <td>14.623</td> <td>HR post-simulator</td> <td>103.667</td> <td>14.450</td> <td>-2.688</td> <td>29</td> <td>0.018</td> </tr> </tbody> </table>

Data package for Poromechanical analysis of oil well cements in CO2-rich environments

<p>Data package for the results obtained in Poromechanical analysis of oil well cements in CO2-rich environments.</p> <p>Juan Cruz Barría, Mohammadreza Bagheri, Diego Manzanal, Seyed M. Shariatipour, Jean-Michel Pereira,<br> Poromechanical analysis of oil well cements in CO2-rich environments,<br> International Journal of Greenhouse Gas Control, Volume 119, 2022, 103734, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2022.103734.</p>

Data analysis of Maamela et al. 2023 The effect of temperature and dietary energy content on female maturation and egg nutritional content in Atlantic salmon

<p>This folder includes the data and R scripts used in the data analysis of the Maamela et al. 2023 paper in Journal of Fish Biology.</p>

CLIMOVE- Climate induced Displacement Data Analysis

<p>CLIMOVE addresses the challenging socio-legal avenues for the European Union (EU) to respond the climate migration from a gender perspective. The objectives of the project will increase the most innovative and diversified social scientific skills to explore, recognize and understand the complex experience of migrant women in the context of climate change and of EU migration crisis: female migration still often clashes with censorship, patriarchal laws or lack of diversity or equality in countries of origin and even destination. As part of this project, a report has been produced analyzing and processing available official data on climate change-induced displacement. A part of this analysis is presented here.</p>

RDF version of the supplementary data from Shin, Hyun Kil and Seo et al. Meta-analysis of Daphnia magna nanotoxicity experiments in accordance with test guidelines. Environ. Sci.: Nano (2018)

<p>This is an RDF version of the dataset published by Shin, Hyun Kil and Seo et al. as a supplement of the study Meta-analysis of Daphnia magna nanotoxicity experiments in accordance with test guidelines. Environ. Sci.: Nano (2018).</p> <p>The original dataset is available online: <a href="https://ui.staging.kit.cloud.douglasconnect.com/dataexplorer?dataset=ab2bc1ee-99dc-4ddf-b1f9-9fdeb8a0f48c%3A1&q=%7B%7D">https://ui.staging.kit.cloud.douglasconnect.com/dataexplorer?dataset=ab2bc1ee-99dc-4ddf-b1f9-9fdeb8a0f48c%3A1&q=%7B%7D</a></p> <p>The original publication DOI: <a href="http://dx.doi.org/10.1039/C7EN01127J">http://dx.doi.org/10.1039/C7EN01127J</a></p> <p>GitHub repository of the datasets converted to RDF along with RML mappings: <a href="https://github.com/ammar257ammar/RDFied-datasets">https://github.com/ammar257ammar/RDFied-datasets</a></p>

Micro-wear analysis data from Shubayqa 1 and 6, Jordan

<p>These are the databases from a micro-wear analysis carried out on the chipped stone assemblages from Shubayqa 1 and 6, Jordan.</p> <p> </p>

Example Data, Analysis and Results Files for MAUD-batch-analysis

<p>Example data, example analysis and example results files for using with the <a href="https://github.com/LightForm-group/MAUD-batch-analysis">MAUD-batch-analysis</a> package. The MAUD-batch analysis package can also be downloaded from Zenodo as <a href="https://doi.org/10.5281/zenodo.7603382">v1.0.0</a>.</p> <p>To run an example analysis with MAUD batch mode, download these data, analysis and results files and unzip them into the respective folders in the MAUD-batch-analysis package.</p>

Data_ Integrating torrefaction of pulp industry sludge with anaerobic digestion to produce bioenergy and biochemicals: Techno-economic and environmental feasibility analysis

<p>In order to improve the economic competitiveness of bioenergy carriers and biochemicals, they must be produced<br> from low-cost or no-cost feedstock and at a reduced operational expenses. In that regard, this study<br> focused on understanding the techno-economic feasibility of using pulp sludge as a low-cost alternative feedstock<br> to forestry biomass in torrefaction. Economic feasibility of further integrating pulp sludge torrefaction with<br> anaerobic digestion to produce energy, biomethane and volatile fatty acids (VFA) was also studied. The operational<br> expenses were around 8.2 and 2.04 M€ and the minimum selling price of torrefied pellets was 407 and<br> 189 €/t for forestry biomass torrefaction and pulp sludge torrefaction respectively. In case of integrated approaches,<br> VFA production showed higher economic feasibility with a torrefied pellets selling price of 163 €/t<br> compared with biomethane production (213 €/t). The biomethane and VFA selling price can be reduced by 90<br> and 64% compared with current market price at torrefied pellets selling price of 260 €/t. Sensitivity analysis<br> revealed that, moisture content of the sludge is the main influencing parameter on the overall economic feasibility<br> of the pulp sludge torrefaction. The environmental analysis showed that pulp sludge torrefaction has higher<br> emissions compared with forestry biomass torrefaction.</p>

Data used in the analysis of Baltis Vallis, Venus

<p>The files included in this data repository were used in the analysis and figures of Conrad and Nimmo (2023) to determine the characteristic wavelengths of the Baltis Vallis canale.</p> <p>Baltis Vallis is a ~7000 km long channel on the surface of Venus that was altered over most of that length.</p> <p>The included files are in comma separated values format with the first line giving the column descriptions.</p> <p>File Descriptions:<br> FordVenus720shape.txt = Spherical Harmonics Coefficients derived from Ford and Pettengill (1992)'s topography<br> BaltisVallis_LLT.csv = Baltis Vallis longitude, latitude, topography sampled from ArcGIS<br> BaltisVallis_Metrics_North.txt = % downhill and conformity factor as a function of maximum spherical harmonic degree. North source case<br> BaltisVallis_Metrics_South.txt = % downhill and conformity factor as a function of maximum spherical harmonic degree. South source case<br> Synthetic_Metric_Deviation.txt = Standard deviation of % downhill and conformity factor for our 1000 synthetic profiles.<br> BaltisVallis_Power_Spectrum.txt = Topographic power spectra of the topography presented in BaltisVallis_LLT.csv</p> <p>BV_Synthetic_Gen.py = generates the synthetic topographic spherical harmonic coefficients</p> <p>BV_Topo_Conf.py and Venus_Synthetic_Topo_Conf.py = determines metric values for Baltis Vallis and synthetic Baltis Valli respectively.</p> <p>BV_TopoPower.py = from the BaltisVallis_LLT.csv file calculate the topographic power spectra and plot it.</p>

opencc-zeroFeb 2023View details →

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Data from: Assessing the mechanisms and impacts of shrub invasion in forests: A meta-analysis

<ol> <li>The encroachment of invasive shrubs in forest understories can have detrimental effects on native plant recruitment. As a result, removal of invasive species is a common practice although long-lasting success is rare. In order to effectively conserve and manage invaded forests, it is crucial to understand the mechanisms that drive shrub invasion, i.e., high propagule pressure, low native resistance, and exploitation of empty niches.</li> <li>To gain a deeper understanding of the invasion process in forest ecosystems we conducted a meta-analysis of the work done in this topic. We collected data on invasive species and native community performance and on the abiotic conditions of forest understories under low and high levels of shrub invasion. We analyzed data from 124 articles that yielded 377 unique observations.</li> <li>Our results revealed that while invader performance did not vary by the mechanism of invasion, the impact on the native community was significantly detrimental when invasion occurred via low biotic resistance, and only marginally significant via propagule pressure. Invasive species performance was associated with increases in light availability, but not with other resources (soil water, or nutrients). When assessing impact on native performance as a function of invasive performance, results were again only significant under the low biotic resistance mechanism. Lastly, impacts were stronger when invasion took place by a single invader.</li> <li> <em>Synthesis and applications</em>: Taken together, these results suggest that restoration efforts should focus on (i) increasing the presence of strong native competitors or functionally diverse native communities, (ii) decreasing sources of invasive shrub propagules while keeping the canopies closed when invasion occurs via high propagule pressure, (iii) avoiding management techniques that degrade or diminish canopy cover, and (iv) prioritizing management of forest understories dominated by particularly impactful invasive shrubs.</li> </ol>

opengpl-2.0-or-laterFeb 2023View details →

Codes and data related to the article: Renard et al. Floods and Heavy Precipitation at the Global Scale: 100-year Analysis and 180-year Reconstruction. Journal of Geophysical Research - Atmospheres.

<p>This package contains R codes and data related to the article:</p> <p>B. Renard, D. McInerney, S. Westra, M. Leonard, D. Kavetski, M. Thyer and J.-P. Vidal. Floods and Heavy Precipitation at the Global Scale: 100-year Analysis and 180-year Reconstruction. <em>Journal of Geophysical Research - Atmospheres</em>. DOI: <a href="https://doi.org/10.1029/2022JD037908">10.1029/2022JD037908</a></p> <p><strong>Analyses</strong></p> <p>This folder contains the R scripts used to set up models, analyse results and prepare figures. See README file for details.</p> <p><strong>ShinyApp</strong></p> <p>This folder contains an interactive Shiny App to explore the data and the results from the article.</p> <p>An online version can be found at <a href="https://hydroapps.recover.inrae.fr/HEGS-paper">https://hydroapps.recover.inrae.fr/HEGS-paper</a></p> <p> </p>

Derived data supporting the analysis of surface albedo changes from Mars 2020 observations: Probabilistic distribution of the Amplitude Spectral Densities of Supercam microphone recordings and Monte-Carlo dust devil simulations.

<p>These files contain derived data used in the analysis submitted for publication in Journal of Geophysical Research: Planets, entitled "Dust Lifting Through Surface Albedo Changes at Jezero Crater, Mars" by Vicente-Retortillo et al. The article was initially submitted on November 14, 2022, and the revised version on March 1, 2023.</p> <p>Files include the derived data and information needed to generate Figures 4 (Microphone_Data.mat and Plot_ASD_from_Microphone_Data) and 6 (remaining files) of the article.</p>

opencc-zeroMar 2023View details →

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Data for: Simulation and social network analysis provide insight into the acquisition of tool behavior in hybrid macaques

<p>The pathways through which primates acquire skills are a central focus of cultural evolution studies. The roles of social and genetic inheritance processes in skill acquisition are often confounded by environmental factors. Hybrid macaques from Koram Island, Thailand provide an opportunity to examine the roles of inheritance and social learning to skill acquisition within a single ecological setting. These hybrids are a cross between tool-using Burmese long-tailed (<em>Macaca</em> <em>fascicularis</em> <em>aurea</em>) and non-tool-using common long-tailed macaques (<em>Macaca</em> <em>fascicularis</em> <em>fascicularis</em>). This population provides an opportunity to explore the roles of social learning and inheritance processes while being able to exclude underlying ecological factors. Here, we investigate the roles of social learning and inheritance in tool use prevalence within this population using social network analysis and simulation. Agent-based modeling (ABM) is used to generate expectations for how social/asocial learning and inheritance structure the patterning in a social network. The results of the simulation show that various transmission mechanisms can be differentiated based on associations between individuals in a social network. The results provide an investigative framework for discussing tool-use transmission pathways in the Koram social network. By combining ABM, network analysis, and behavioral data from the field we can investigate the roles social learning and inheritance play in tool acquisition in wild primates. </p>

opencc-by-4.0Apr 2022View details →

Scripts of data selection and analysis: role of community size in driving spatial variation in riverine fish metacommunities around the world

<p>Here we describe how we obtained and analyzed data for the manuscript: High compositional dissimilarity among small communities is decoupled from environmental variation, accepted for publication in Oikos. (10.1111/oik.09802). A preprint is also available: https://doi.org/10.32942/osf.io/vngse</p> <p>We investigated the role of community size in mediate the strength of ecological drift and environmental selection in driving community spatial variation in metacommunities. </p>