Find research datasets worth reusing

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging

Accompanying data to publication: Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging Excel files include hyperspectral indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments. Scripts used for producing plots in the publication and supplementary material are available on Renku; see the Software section. Hyperspectral data are submitted separately due to their size; see the Related works.

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - Phycocyanin

Accompanying data to publication: Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - Phycocyanin & Chlorophyll a

Dataset for the paper "Direct prediction of saturated neoclassical tearing modes in slab using an equilibrium approach"

Data and scripts related to the publication "Direct prediction of saturated neoclassical tearing modes in slab using an equilibrium approach".

Dataset about conference paper "Toward the Sea or the Land Cultural Identity and the Choice of Direction of Movement on the Water"

Dataset about conference paper "Toward the Sea or the Land Cultural Identity and the Choice of Direction of Movement on the Water". Including heritage sites, DEM, wind environment, least-cost corridor, and least-cost path.

opencc-by-4.0Sep 2024View details →

Direction of Arrival Estimation for Wildlife Protection - Soundfiles

Soundfiles collected during data collection related to the Licenciate thesis "Direction of Arrival Estimation for Wildlife Protection".

opencc-by-4.0Sep 2024View details →

Sequential Bayesian Inference of Finite-strain Visco-elastic Visco-plastic model parameters of 22-month aged PA12 bulk material printed along different directions

These are the data related to aged PA12 (22 months) following the methodology described in the following publication in which non-aged PA12 has been tested: title = "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator.", journal = "International Journal of Solids and Structures", year = "2023", volume = "283", pages = "112470", doi = "10.1016/j.ijsolstr.2023.112470", author = "Wu, Ling and Anglade, Cyrielle and Cobian, Lucia and Monclus, Miguel and Segurado, Javier and Karayagiz, Fatma and Freitas, Ubiratan and Noels Ludovic" Contrarily to the non-aged material, since high-strain-rate tests are not available, only 5 Maxwell's branches are considered herein. <h1>Description</h1> BI code and results of the inference of a pressure-dependent visco-elastic visco-plastic model developed in [NGU16] with a umat implementation in <a href="https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP">https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP</a> The sequential BI is described in [WU23] The experimental protocol is reported in [COB22,COB22b] but is herein applied on aged PA12 To run the BI you need the open source code <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> If you use these data or model, we would be grateful if you could cite the related papers: <ul> <li>[WU23] L. Wu, C. Anglade, L. Cobian, M. Monclus, J. Segurado, F. Karayagiz, U. Santos Freitas, L. Noels, Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator, International Journal of Solids and Structures (2023) 112470: <a href="https://doi.org/10.1016/j.ijsolstr.2023.112470" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1016/j.ijsolstr.2023.112470</a></li> <li>[COB22] L. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556: <a href="https://doi.org/10.1016/j.polymertesting.2022.107556" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1016/j.polymertesting.2022.107556</a> (in Open access)</li> <li>[COB22b] Data of “. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556” <a href="http://dx.doi.org/10.5281/zenodo.6136935" target="_blank" rel="nofollow noreferrer noopener">http://dx.doi.org/10.5281/zenodo.6136935</a> (in Open access)</li> <li>[NGU16] V. D. Nguyen, F. Lani, T. Pardoen, X. Morelle, L. Noels, A large strain hyperelastic viscoelastic-viscoplastic-damage constitutive model based on a multi-mechanism non-local damage continuum for amorphous glassy polymers. International Journal of Solids and Structures 96 (2016): 192-216; <a href="https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008" target="_blank" rel="nofollow noreferrer noopener">https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008</a>, Open access: <a href="https://orbi.uliege.be/handle/2268/197898" target="_blank" rel="nofollow noreferrer noopener">https://orbi.uliege.be/handle/2268/197898</a></li> </ul> <h1>Directories</h1> All the codes and experimental results are in three directories: <ol> <li>Experiment_PA12_AGED: experimental data of aged material, see the README.txt in each subdirectory for details</li> <li>BayesianVE: BI of the visco-elastic parameters 2.1. PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE range 2.1.1. Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVE_H.dat and Load_ExpVE_V.dat, which keep the experimental observations and loading conditions to perform the BI. 2.1.2. PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py 2.1.3. Load_ExpVE_H.dat and Load_ExpVE_V.dat created files with the observations and loading conditions to perform the BI 2.2. VE_V and VE_H: BI for viscoelastic properties of "V" specimen (VE_V) and "H" specimen (VE_H) 2.2.1. BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VE_....dat 2.2.2. WarmStart = True is used to restart an inference 2.2.3. MCMC_VE_....dat in the VE_V and VE_H directories are the BI results 2.3. CheckBayRes: to visualize predictions of a BI sample and experimental curves 2.3.1. MCMCRes.py is used to check the numerical predictions of a BI parameter sample (read last sample by default, V or H direction can be selected at line 7) 2.3.2. ResKGEmu.py plots the evolution of elastic properties with time 2.3.3. uses as input VE_V/MCMC_VE_....dat or VE_H/MCMC_VE_....dat 2.3.4. uses local ViscoElasticTest.py, line.geo, line. msh as interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code 2.3.5. uses local functions plotExp.py 2.4. ViscoElasticTest.py, line.geo, line.msh: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VE_V and VE_H to call the VEVP model</li> <li>BayesianVEVP: BI of the visco-elastic and visco-plastic parameters 3.1. PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE-VP ranges 3.1.1. Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat, which keep the experimental observations and loading conditions to perform BI at the viscoplastic stage. 3.1.2. PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py 3.1.3. Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat created files with the observations and loading conditions to perform the BI 3.2. VP_V and VP_H: BI for viscoelastic-viscoplastic properties of "V" specimen (VP_V) and "H" specimen (VP_H) 3.2.1. BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VP_....dat 3.2.2. WarmStart = True is used to restart an inference 3.2.3. It starts from the VE prosterior as prior, see point 2, and generates a MCMC_VP_?1Step.dat (? being H or V) 3.3. CheckBayRes: visualize predictions of a BI sample and experimental curves 3.3.1. MCMCRes.py is used to check the numerical predictions with 3 BI parameter samples (inclusing MAP, V or H direction can be selected at line 12) using the samples of BayesianVEVP/VP?/MCMC_VP_?1Step.dat (? being H or V) 3.3.2. plot_hist.py is used to plot histograms of all the inferred parameters using the samples of BayesianVEVP/VP?/MCMC_VP_?1Step.dat (? being H or V) 3.3.3. Plot_Prop.py plots joints histograms of the inferred parameters using the samples of BayesianVEVP/VP?/MCMC_VP_?_1Step.dat (? being H or V) 3.3.4. ResKGEmu.py plots the evolution of elastic properties with time 3.4. VEVPTest.py: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VP_V2Step and VP_H2Step to call the VEVP model</li> </ol> <h1>Figures (reference to the number in [WU23] but for aged PA12)</h1> <ul> <li>Fig. 5 (Selected observations): From directory BayesianVE/PlotExperimentalCurves/PrintDir_? (? being H or V), run python3 plotExp_T.py or plotExp_C.py</li> <li>Fig. 7: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V" and then with direct = "H" and with Var = [0,1,14,18,22,23,24,25]</li> <li>Fig. 8 (Predictions of 3 inference realisations): BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 9 (Predictions of 3 inference realisations): BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "H" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 14A: From directory BayesianVE/PlotExperimentalCurves/PrintDir_? (? being H or V), run python3 plotExp_T.py or plotExp_C.py</li> <li>Fig. 15B: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V", Var = [2,3,8,9,10,11,12,13] and [14,15,16,17,18,19,20,21]</li> <li>Fig. 16B: BayesainVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 17B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 18B: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "H", Var = [2,3,8,9,10,11,12,13] and [14,15,16,17,18,19,20,21]</li> <li>Fig. 19B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 20B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> </ul>   This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015.

opencc-by-4.0Oct 2024View details →

Data for "Direct observation of the neural computations underlying a single decision"

This repository contains the data required to reproduce all figures and results of the paper "Direct observation of the neural computations underlying a single decision" by Natalie A Steinemann*, Gabriel M Stine*, Eric M Trautmann, Ariel Zylberberg, Daniel M Wolpert & Michael N Shadlen (https://doi.org/10.7554/eLife.90859.1). The associated code is deposited on Github (https://github.com/Nat-Stein/LIP_single_trial_decision_variable).

opencc-by-4.0Oct 2024View details →

Datasets corresponding to "Direct evaluation of antiplatelet therapy in coronary artery disease by comprehensive image-based profiling of circulating platelets"

Datasets corresponding to "Direct evaluation of antiplatelet therapy in coronary artery disease by comprehensive image-based profiling of circulating platelets" 02_CNN_PhenotypeClassif.7z CNN Phenotype classification. Model was trained using AIDeveloper. using manually labelled data. Labelled Data is contained in folder "03_GatedData". The AIDeveloper session file in "02_Model\M10_Nitta6l_32pix_8class_meta.xlsx" shows, which files correspond to which subpopulation. The final model "M10_Nitta6l_32pix_8class_448.model" and corresponding .pb files are also located in that folder. codeforclassification.zip Code for applying model on unlabelled data. Test data is contained in 'sampledata.zip'

opencc-by-4.0Oct 2024View details →

Dataset for published paper: Mode Conversion Trimming in Asymmetric Directional Couplers Enabled by Silicon Ion Implantation

This dataset contains the measurement results described in the paper 'Mode Conversion Trimming in Asymmetric Directional Couplers Enabled by Silicon Ion Implantation'.

opencc-zeroMar 2020View details →

Optical traits perform equally well as directly-measured functional traits in explaining the impact of an invasive plant on litter decomposition

1. Functional traits can help elucidate and predict the impact of invasive plant species on ecosystem functioning. Yet, this approach requires comprehensive and labor-intensive trait collection campaigns, covering intraspecific trait variation of both the invader and native species in the invaded community. One potential way to overcome these logistic constraints is using hyperspectral remote sensing technology to efficiently quantify functional trait values. Although such spectrally derived or 'optical' traits are known to closely link to directly-measured functional traits, little research has explored how well these optical traits perform in assessing invader-induced ecosystem impact. 2. Here, we explored the trait-mediated impact of the invasive Rosa rugosa on litter decomposition and evaluated whether optical traits perform equally well as directly-measured traits in predicting litter decomposition variation. We collected data on species-specific functional traits, leaf hyperspectral reflectance and standardized 'tea bag index' litter decomposition across 25 invaded and 25 uninvaded coastal grassland plots. The selected traits were all potentially related to litter decomposition and covered the leaf economics spectrum, additional leaf structural components and competitive ability. Optical traits were quantified through a combination of a physical radiative transfer model inversion and vegetation indices calculations. 3. Invasion significantly increased the stabilization factor, i.e. the amount of resulting recalcitrant litter. Invader impact on litter decomposition could be entirely explained by changes it induced in the functional traits of the native community, rather than by the invader's traits itself. More specifically, the invader pushed the invaded community towards traits associated with high litter quality. Optical traits performed equally well as directly-measured traits in explaining the invasion impact on the stabilization factor (R2= 41.9% vs. 38.5%). Furthermore, the interpretation of the results based on optical traits resulted in a similar functional understanding of the invader impact. 4. Synthesis: Our results indicate the potential of hyperspectral data to explain changes in ecosystem functioning. The combination of radiative transfer models and vegetation indices allowed to extract all relevant trait information from the hyperspectral data. This framework thus presents a practical short-cut to assess relevant leaf traits, requiring only a limited amount of field trait measurements.

opencc-zeroMar 2020View details →

First explorations: ontogeny of central-place-foraging directions in two tropical seabirds

A widespread hypothesis for the ontogeny of behaviour and decision-making is the early-exploration-later-canalization hypothesis. It postulates that juveniles are more exploratory and adults more consistent in their behavior. In addition, it is often assumed that naïve juveniles could overcome the costs of individual experience building by copying more the decisions of others than adults (early-conformism-later-self-defining hypothesis). Here we compare the central-place-foraging movements of adults and post-fledging juveniles in their first flights around the colony before dispersal and migration, in two sympatric species of tropical seabirds: red-footed boobies and great frigatebirds. Using GPS records of individual movements, we analyzed the foraging directions of seabirds from the colony across successive trips. Juveniles of both species showed significant within-individual consistency in foraging direction but at lower levels than adults. Juveniles leaving the colony within the same time-window showed significant but low between-individual resemblance in foraging direction, at levels similar to adults. In both species, homing efficiency was lower in juveniles than in adults. Juvenile foraging directions were initially influenced by wind conditions, particularly in low-wing-loading frigatebirds. Wind conditions progressively lost influence on juvenile foraging directions during their first weeks of flights. In contrast within-individual consistency, between-individual resemblance and homing efficiency did not show signs of progression in juveniles. Our results support the early-exploration-later-canalization hypothesis but not the early-conformism-later-self-defining hypothesis. Relaxed constraints on self-feeding efficiency could favour high variability in post-fledging tropical seabirds. Our simple approach could be applied to further test these hypotheses by comparing strategies across a wide range of central-place foragers.

opencc-by-4.0Jun 2021View details →

Dataset accompanying: Directional biosonar beams allow echolocating harbour porpoises to actively discriminate and intercept closely-spaced targets

These datasets accompany the manuscript "Directional biosonar beams allow echolocating harbour porpoises to actively discriminate and intercept closely-spaced targets". Three XLSX spreadsheets contain data for trial-specific variables (Dataset A, n=120, used for trial duration, discrimination success, number of scans, buzz duration, range to target at buzz onset, range to target at discrimination), on-axis click candidates (Dataset B, n=1810, used for delta T, delta EL), and true on-axis clicks (Dataset C, n=906, used for SL and bearing calculations). Units of each variable are denoted in the column headers. Details on data collection, curation, and analysis can be found in the Malinka et al. publication ([insert link to DOI once available]). For comments/questions, please contact chloe.e.malinka [at] bio.au.dk

opencc-by-4.0Jun 2021View details →

Thalamic input to motor cortex facilitates goal-directed action initiation

Data set for: Takahashi N, Moberg S, Zolnik TA, Catanese J, Sachdev RNS, Larkum ME, Jaeger D (2021) Thalamic input to motor cortex facilitates goal-directed action initiation. Current Biology (DOI: <a href="https://doi.org/10.1016/j.cub.2021.06.089">https://doi.org/10.1016/j.cub.2021.06.089</a>)  The file named "Takahashi_Data&Code.zip" is a zipped version of a folder "Takahashi_Data&Code", which contains the data analyzed in the study along with the Matlab code used to generate the published figures. To access the data and the code, first unzip the file. Then add the folder with subfolders to the Matlab path. Before running the code (e.g., "PlotData_Fig1.m"), load the related Matlab data file (e.g., "Data_Pharmacology_Fig1.mat") in Workspace. Each code plots the results used in the corresponding figure.

opencc-zeroJun 2021View details →

Data from: Direct and indirect effects of landscape and field management intensity on carabids through trophic resources and weeds

Carabids are important biological control agents of weeds and other pests in agricultural fields. The carabid community is built upon direct and indirect ecological effects of landscape complexity, field management intensity and biotic components that in interaction make any prediction of community size and composition challenging. We analyse a large-scale sample of 60 European cereal fields using Structural Equation Modelling to quantify the direct effects of field management intensity and the surrounding landscape, and their indirect effect via biotic components, on carabid diversity. Our results highlight that direct and indirect effects of increasing landscape complexity, mediated by trophic resources, mainly affect carabids positively. Field management intensity only ever affects carabids through indirect effects that are generally negative, by suppressing standing weeds and weed seeds. Indirect effects on granivore carabid species depended on weed seed availability whereas omnivores depended on the availability of both weed seeds and animal prey. Synthesis and applications: A consideration of both the direct and indirect effects of landscape and field management is necessary for predicting carabid communities and with interactions. These effects, mediated via trophic resources, supports the diversity and abundance of carabid communities and their provision of ecosystem services. Our results show that promoting crop diversity and connectivity to semi-natural habitats will directly enhance carabid communities in farmland by manipulating their migration from source habitats. A reduction in field management intensity will preserve local standing weeds and weed seeds, and indirectly support carabid communities. These local and landscape modifications could contribute to improve the natural regulation of pests and weeds by carabids.

opencc-by-4.0Dec 2013View details →

FIG. 2 in Direct optimization, sensitivity analysis, and the evolution of the hymenopteran superfamilies

FIG. 2. (continued).

opencc-zeroJul 2021View details →

Warm range margin of boreal bryophytes and lichens not directly limited by temperatures

1. Species at their warm range margin are potentially threatened by higher temperatures, but may persist in microrefugia. Whether such microsites occur due to more suitable microclimate or due to lower biotic pressure from e.g. competitive species, is still not fully resolved. 2. We examined whether boreal bryophytes and lichens show signs of direct climate limitation, i.e. whether they perform better in cold and/or humid microclimates at their warm range margin. We transplanted a moss, a liverwort, and a lichen to 58 boreal forest sites with different microclimates at the species' southern range margin in central Sweden. Species were grown in garden soil patches to control effects of competitive exclusion and soil quality. We followed the transplanted species over three growing seasons (2016-2018) and modelled growth and vitality for each species as a function of sub-canopy temperature, soil moisture, air humidity, and forest type. In 2018, we also recorded cover of other plants having re-colonized the garden soil patches and modelled this potential future competition with the same environmental variables plus litter. 3. Species performance increased with warmer temperatures, which was often conditional on high soil moisture, and at sites with more conifers. Soil moisture had a positive effect, especially on the moss in the last year 2018, when the growing season was exceptionally hot and dry. The lichen was mostly affected by gastropod grazing. Recolonization of other plants was also faster at warmer and moister sites. The results indicate that competition, herbivory, shading leaf litter, and water scarcity might be more important than direct temperature for performance at the species' warm range margin. 4. Synthesis. In a transplant experiment with three boreal understory species we did not find signs of direct temperature limitation towards the south. Forest microrefugia, i.e. habitats where these species could persist regional warming, may instead be sites with fewer competitors and enemies, and with sufficient moisture and more conifers in the overstory.

opencc-by-4.0Jul 2021View details →

Genetic disruption of synthesis pathways of Arabidopsis secondary metabolites dramatically affects root-associated nematode populations directly and via modulation of microbial communities

Dataset of nematode, fungal and bacterial sequence reads of Arabidopsis roots. Dataset of fungal and bacterial sequence reads of Arabidopsis microbial suspension. DNA concentration of Arabidopsis root microbial suspension. Meloidogyne incognita J2 invasion into tomato roots. qPCR dataset of Meloidogyne hapla infection pressure into Arabidopsis roots.

opencc-by-4.0Jul 2021View details →

raw data for the article "Diamine Synthesis via the Nitrogen-Directed Azidation of sigma- and pi- C-C Bonds"

Raw NMR, MS and IR  data for the article "Diamine Synthesis via the Nitrogen-Directed Azidation of sigma- and pi- C-C Bonds" published in the Journal of the American Chemical Society, DOI:  <a href="https://doi.org/10.1021/jacs.1c06700">https://doi.org/10.1021/jacs.1c06700</a> The number of the folders correspond to compounds numbers in the article. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.