Find research datasets worth reusing

opencc-by-4.0Sep 2016View details →

Acquired data necessary to perform the control algorithm introduced in the scientific paper: "Multilevel control of an anthropomorphic prosthetic hand for grasp and slip prevention" (Advances in Mechanical Engineering, 2016, vol. 8, pp. 1-13)

Acquired data necessary to perform the control algorithm introduced in this paper. a) Figure 6: Calibration data for the three FSRs placed on the prosthetic hand and covered with silicon caps. b) Figure 9: Data for the cost during the learning of two grasping tasks of an egg: bi-digital grasp and tri-digital grasp. c) Figure 10 and Figure 11: Data for the experimental results with the plastic cup and with the highlighter shown in the paper.

opencc-by-4.0Mar 2023View details →

Two metabolomics data sets (mouse kidney, mouse plasma), generated for the publication Bignon et al., 2023: "Multiomics reveals multilevel control of renal and systemic metabolism by the renal tubular circadian clock".

Publication: Bignon Y, Wigger L, Ansermet C, Weger BD, Lagarrigue S, Centeno G, Durussel F, Götz L, Ibberson M, Pradervand S, Quadroni M, Weger M, Amati F, Gachon F, Firsov D. Multiomics reveals multilevel control of renal and systemic metabolism by the renal tubular circadian clock. J Clin Invest. 2023 Mar 2:e167133. doi: 10.1172/JCI167133. Epub ahead of print. PMID: 36862511.   Abstract: Circadian rhythmicity in renal function suggests rhythmic adaptations in renal metabolism. To decipher the role of the circadian clock in renal metabolism, we studied diurnal changes in renal metabolic pathways using integrated transcriptomic, proteomic, and metabolomic analysis performed on control mice and mice with inducible deletion of the circadian clock regulator Bmal1 in the renal tubule (cKOt). With this unique resource, we demonstrated that ~30% RNAs, ~20% proteins and ~20% metabolites are rhythmic in kidneys of control mice. Several key metabolic pathways including NAD+ biosynthesis, fatty acid transport, carnitine shuttle,and b-oxidation displayed impairments in kidneys of cKOt, resulting in a perturbed mitochondrial activity. Carnitine reabsorption from the primary urine was one of the most impacted processes with a ~50% reduction in plasma carnitine levels and a parallel systemic decrease in tissues carnitine content. This suggests that the circadian clock in the renal tubule controls both kidney and systemic physiology.   This record contains two separate mass-spectrometry metabolomics data sets associated with this study: <ol> <li>Metabolic profile of renal tubules, MS/MS data, Metabolon, Morrisville, NC (N=60)</li> <li>Metabolic profile of blood plasma, MS/MS data, Biocrates, Innsbruck, Austria (N=60)</li> </ol> For each data set, original data as received from the platforms and processed data as used in the data analysis are provided. Preprocessing of kidney data included removal of metabolites with more than 80% missing data values, median normalization, imputation and glog2 transformation. Preprocessing of plasma data included filtering of metabolites with any missing data and log2 transformation. Details of data processing are available in the STAR*methods of the publication.   Data sets in other repositories associated with the same study: Additional data sets (transcriptomics, proteomics) pertaining to the same study have been deposited in public repositories: <ul> <li>Gene Expression Omnibus (NCBI GEO), GSE216252</li> <li>PRIDE Archive (EMBL-EBI), PXD036803</li> </ul>

opencc-by-4.0Feb 2023View details →

Data of publication: "Collective atom-cavity coupling and nonlinear dynamics with atoms with multilevel ground states"

The uploaded files contain the raw data of the measurements and simulations presented in <a href="https://doi.org/10.1103/PhysRevA.107.023714">https://doi.org/10.1103/PhysRevA.107.023714</a>

opencc-by-4.0Sep 2023View details →

Multilevel atlas comparisons reveal divergent evolution of the primate brain

Nifti files of 20 mammalian atlases modified into a Common Multilevel Segmentation. (See Figure 1 in Multilevel atlas comparisons reveal divergent evolution of the primate brain; https://www.pnas.org/doi/full/10.1073/pnas.2202491119#sec-3) These nifti files are based on the brain atlases from 18 mammalian species, that were published between the years 2013 and 2021 (see list). The Python script to re-segment the "original" atlases into the modified version (that is shared here) is also available: see Modify_atlases.py Each species folder contains 5 nifti files: 1 for each level of segmentation and 1 for the brain segmentation. The other txt files are the volumetric output extracted using AFNI on each nifti file. Please read the Readme.txt file to credit and cite accordingly all the authors.

opencc-by-4.0Oct 2023View details →

Functional organization of the mouse lemur Primate Microcebus murinus : from multilevel validation to comparison with humans

Brain network organization in the mouse lemur (Microcebus murinus) Primate. (comparison with humans) Archives contain: - Dictionary learning analysis in mouse lemurs and humans showing networks identified in these two species. - Cerebral templates from mouse lemurs and humans (MNI template). They can be used to localize networks. - A functional atlas of the mouse lemur brain issued from resting fMRI. Resting-state functional MR images were recorded from 14 mouse lemurs at 11.7 Tesla (2 time point per animal). - An atlas from human brain (issued from <a href="http://www.gin.cnrs.fr/fr/outils/aal-aal2/">http://www.gin.cnrs.fr/fr/outils/aal-aal...</a>) that can be used to attribute human cerebral networks. - Templates, atlases and networks can be easily observed together using ITK-SNAP (<a href="http://www.itksnap.org/">http://www.itksnap.org/</a>). if used for publication please cite:  Resting state functional atlas and cerebral networks in mouse lemur primates at 11.7 Tesla Clément M Garin, Nachiket A Nadkarni, Brigitte Landeau, Gaël Chételat, Jean-Luc Picq, Salma Bougacha, Marc Dhenain Feb 2021 NeuroImage 226, 117589 DOI: 10.1016/J.NEUROIMAGE.2020.117589 <a href="https://www.sciencedirect.com/science/article/pii/S1053811920310740">https://www.sciencedirect.com/science/article/pii/S1053811920310740</a>

openother-openMar 2020View details →

Repository: Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade-linked multilevel life cycle assessment

This repository contains the input data, codes and results of the model developed in the paper "Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade-linked multilevel life cycle assessment" published in the Journal of Industrial Ecology (2020) by Alexandre Milovanoff, I. Daniel Posen, Heather L. MacLean.

opencc-by-4.0Mar 2022View details →

Accompanying simulated data for "Go multivariate: a Monte Carlo study of a multilevel hidden Markov model with categorical data of varying complexity"

The multilevel hidden Markov model (MHMM) is a promising vehicle to investigate latent dynamics over time in social and behavioral processes. By including continuous individual random effects, the model accommodates variability between individuals, providing individual-specific trajectories and facilitating the study of individual differences. However, the performance of the MHMM has not been sufficiently explored. Currently, there are no practical guidelines on the sample size needed to obtain reliable estimates related to categorical data characteristics We performed an extensive simulation to assess the effect of the number of dependent variables (1-4), the number of individuals (5-90), and the number of observations per individual (100-1600) on the estimation performance of group-level parameters and between-individual variability on a Bayesian MHMM with categorical data of various levels of complexity. We found that using multivariate data generally alleviates the sample size needed and improves the stability of the results. Regarding the estimation of group-level parameters, the number of individuals and observations largely compensate for each other. Meanwhile, only the former drives the estimation of between-individual variability. We conclude with guidelines on the sample size necessary based on the complexity of the data and the study objectives of the practitioners. This repository contains data generated for the manuscript: "Go multivariate: a Monte Carlo study of a multilevel hidden Markov model with categorical data of varying complexity". It comprehends: (1) model outputs (maximum a posteriori estimates) for each repetition (n=100) of each scenario (n=324) of the main simulation, (2) complete model outputs (including estimates for 4000 MCMC iterations) for two chains of each repetition (n=3) of each scenario (n=324). Please note that the empirical data used in the manuscript is not available as part of this repository. A subsample of the data used in the empirical example are openly available as an example data set in the R package <a href="https://cran.r-project.org/web/packages/mHMMbayes/index.html">mHMMbayes on CRAN</a>. The full data set is available on request from the authors.

opencc-zeroOct 2022View details →

Multilevel selection on social network traits differs between sexes in experimental populations of forked fungus beetles

Both individual and group behavior can influence individual fitness, but multilevel selection is rarely quantified on social behaviors. Social networks provide a unique opportunity to study multilevel selection on social behaviors, as they describe complex social traits and patterns of interaction at both the individual and group levels. In this study, we used contextual analysis to measure the consequences of both individual network position and group network structure on individual fitness in experimental populations of forked fungus beetles (Bolitotherus cornutus) with two different resource distributions. We found that males with high individual connectivity (strength) and centrality (betweenness) had higher mating success. However, group network structure did not influence their mating success. Conversely, we found that individual network position had no effect on female reproductive success but that females in populations with many social interactions experienced lower reproductive success. The strength of individual-level selection in males and group-level selection in females intensified when resources were clumped together, showing that habitat structure influences multilevel selection. Individual and emergent group social behavior both influence variation in components of individual fitness but impact male mating success and female reproductive success differently, setting up intersexual conflicts over patterns of social interactions at multiple levels.

opencc-by-4.0Mar 2023View details →

Accompanying empirical data for Kirchherr et al., 2023, "Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex"

This repository contains data accompanying: Kirchherr et al., 2023, "Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex". Data collection methods: Two adult female rhesus macaques (Macaca mulatta) trained on a reaching, and grasping, and placing task served as the subjects. The animal handling as well as surgical and experimental procedures complied with European guideline (2010/63/UE) and authorized by the French Ministry for Higher Education and Research (project # 2016112713202878) in force on the care and use of laboratory animals, and were approved by the ethics committee CELYNE (comité d’éthique Lyonnais pour les neurosciences expérimentale, C2EA 42). After initial training, we performed a sterile surgery to implant six floating multielectrode arrays (FMA, Microprobes for Life Science, Gaithersburg, MD, USA) in the right (monkey 1) or left (monkey 2) cortical hemisphere. Each array was comprised of 32 platinum/iridium electrodes (impedance 0.5 MΩ at 1 kHz) with lengths ranging from 1 to 6 mm, and with an inter-electrode spacing of 400 μm. One electrode array was implanted in the primary motor cortex (M1), two were implanted in the ventral premotor cortex (F5), one in the dorsal premotor cortex (F2), and two in the prefrontal cortex (45a and 46/12r), as estimated according to a previous magnetic resonance imaging scan. For the purposes of this study, we analyzed data from the M1 array of each monkey. The wideband neural signal (bandpass filtered at 0.1 to 7500 kHz) was recorded at 30 kS/s, and amplified and digitized (16-bit; 0.192 μV resolution) with an Intan Tech-based (Intan Technologies, Los Angeles, CA, USA) open source acquisition system (Open Ephys; Siegle et al. 2017). This system uses a 256-channel Intan RHD2000 series acquisition board and 32-channel headstages (RHD2132). Spike detection was performed offline using Trisdesclous (Garcia & Pouzat,2015). The common reference was removed to reduce ambient noise. Spikes were then detected from each electrode using a threshold of 2 times the median absolute deviation (MAD), and analyzed as multi-unit activity (MUA) in 10 ms bins. All electrodes in which at least one well-isolated spike waveform was detected were selected for the following analyses. We thus used a sample of 21 electrodes out of 32 for monkey 1, and 25 out of 32 electrodes for monkey 2. Custom made detection panels were used to record the moments when the monkey’s hand released the handle, the hand contacted the target object, and when the object was placed in the groove. An Omniplex 16-channel recording system (Plexon, Dallas, TX, USA) was used to simultaneously record these behavioral events. Trials were discarded if the response time (time between the go signal and handle release) was less than 100 or greater than 1500 ms, the reach duration (time between handle release and object contact) was less than 100 or greater than 1000 ms, or the placing duration (time between object contact and placing the object in the groove) was less than 100 or greater than 1200 ms, leaving 19 - 68 trials per day for monkey 1 (M = 43.9, SD = 15.46, N = 439; left: M = 14.8, SD = 5.74; center: M = 14.4, SD = 5.15; right: M = 14.7, SD = 7.73), and 23 - 49 per day for monkey 2 (M = 38.3, SD = 9.87, N = 383; left: M = 14.2, SD = 3.91; center: M = 10.8, SD = 3.55; right: M = 13.3, SD = 3.37). Abstract: Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analyzing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity in many neurons, but also because of changes in the recorded signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analyzing such data in terms of discrete, latent states, but previous approaches have either not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modeled condition specific differences. We present a multilevel Bayesian HMM which addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation, and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping, and placing task. We show that the model identifies latent neural population states which are tightly linked to behavioral events, despite the model being trained without any information about event timing. We show that these events represent specific spatiotemporal patterns of neural population activity and that their relationship to behavior is consistent over days of recording. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations.

opencc-by-4.0Dec 2022View details →

Epitaxial La0.5Sr0.5MnO3 bipolar memristive devices with tunable and stable multilevel states

Data files (.txt) of the data presented in the publication DOI 10.1002/admi.202202496 File names are referred to as the reference of the Figure and number in the main manuscript and keywords of the figure. Regarding the series in a Figure, in the case there is very little data, the series are in a single file, arranged in columns. Otherwise, the series with large amounts of data have been split into single files named according to the series (legend). All the files include two headers (Name and Units) for each column. The files containing data from multiple series contain an extra header to precise the data series.

opencc-zeroJun 2023View details →

Multilevel analysis between Physcomitrium patens and Mortierella explores potential long-standing interaction among land plants and fungi

<a name="_Hlk83717523"></a>The model moss species Physcomitrium patens has long been used for studying divergence and evolution of land plants spanning from bryophytes to angiosperms. In addition to its phylogenetic relationships, the limited number of differential tissues, and comparable morphology to the earliest embryophytes make it an ideal candidate for modeling plant terrestrialization 500 million years ago. Based on how plants and fungi interact today, it is predicted that early interactions may have aided in overcoming the barriers present for initial plant colonization on land. This may have manifested similar to present day, where fungi enabled easier uptake of nitrogen, phosphorous, micronutrients, and water retention in exchange for a reliable carbon source. However, identifiable fungal symbionts in P. patens, despite mutualistic interaction widespread among all present day embryophyte families, have remained elusive. To test modern representatives of early land fungal lineages, two Mortierella species (Linnemannia elongata and Benniella eriona), with strains lacking and containing endobacterial symbionts, were grown in coculture with P. patens. We illustrate the interaction between P. patens and Mortierella through high-throughput phenomics, microscopy, RNA-sequencing, differential expression profiling, gene ontology enrichment, and comparisons among 99 other P. patens transcriptomic studies. Our study provides insights into the earliest plant-fungal interactions may have looked like and ways P. patens and Mortierella communicate today.

publicOct 2022View details →

Multilevel selection on social network traits differs between sexes in experimental populations of forked fungus beetles

Open the record for dataset details and reuse information.

publicDec 2023View details →

Multilevel analysis between Physcomitrium patens and Mortierella explores potential long-standing interaction among land plants and fungi

Open the record for dataset details and reuse information.

dryad36/100

Data from: Functional traits and community composition: a comparison among community-weighted means, weighted correlations, and multilevel models

1. Of the several approaches that are used to analyze functional trait-environment relationships, the most popular is community-weighted mean regressions (CWMr) in which species trait values are averaged at the site level and then regressed against environmental variables. Other approaches include model-based methods and weighted correlations of different metrics of trait-environment associations, the best known of which is the fourth-corner correlation method. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using four different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios, implying that the significant results for the data could be spurious. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but had lower power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should always be avoided. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using five different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but suffered from low power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should be avoided.

opencc-zeroDec 2017View details →

opencc-by-4.0Feb 2017View details →

A Benchmark Set for Multilevel Hypergraph Partitioning Algorithms

DESCRIPTION ------------------------------------------------------------------------------------------------------- This archive contains a large benchmark set for hypergraph partitioning algorithms. All hypergraphs are unweighted (i.e., have unit edge and vertex weights) and use the hMetis hypergraph input file format [1]. BENCHMARK SETS ------------------------------------------------------------------------------------------------------- Hypergraphs are derived from the following benchmark sets: - The ISPD98 Circuit Benchmark Suite [2] - The DAC 2012 Routability-Driven Placement Contest [3] - The international SAT Competition 2014 [4] - The University of Florida Sparse Matrix Collection (UF-SPM) [5] The benchmark set contains all ISPD98 and DAC2012 instances. Furthermore, it contains 92 randomly selected instances from the application track of the SAT Competition 2014. The Sparse Matrix Collection is organized into 172 groups and each group contains matrices of different application areas. From each group, we chose one matrix for each application area that has between 10 000 and 10.000.000 columns. In case multiple matrices fulfill our criteria, we randomly selected one. In total, we include 192 matrices. HYPERGRAPH REPRESENTATION ------------------------------------------------------------------------------------------------------- VLSI instances [2,3] are transformed into hypergraphs by converting the netlist into a set of hyperedges. Sparse Matrices are translated into hypergraphs using the row-net model [6], i.e. each row is treated as a net and each column as a vertex. SAT instances are converted into three different hypergraph representations: In the literal model, each boolean literal is mapped to one vertex and each clause constitutes a net [7]. In the primal model each variable is represented by a vertex and each clause is represented by a net, whereas in the dual model the opposite is the case [8]. FILE NAMES ------------------------------------------------------------------------------------------------------- The origin of each hypergraph (and for SAT instances the hypergraph model) is encoded into the file names as follows: - Sparse Matrices : *.mtx.hgr - DAC2012 : dac2012_superblue*.hgr - ISPD98 : ISPD98_ibm*.hgr - SAT-14 primal : sat14_*.cnf.primal.hgr - SAT-14 dual : sat14_*.cnf.dual.hgr - SAT-14 literal : sat14_*.cnf.hgr REFERENCES ------------------------------------------------------------------------------------------------------- [1] http://glaros.dtc.umn.edu/gkhome/fetch/sw/hmetis/manual.pdf [2] C. J. Alpert. The ISPD98 Circuit Benchmark Suite. In Proc. of the 1998 Int. Symp. on Physical Design, pages 80–85, New York, 1998. ACM. [3] N. Viswanathan, C. Alpert, C. Sze, Z. Li, and Y/ Wei. The dac 2012 routability-driven placement contest and benchmark suite. In Proceedings of the 49th Annual Design Automation Conference, DAC ’12, pages 774–782 [4] A. Belov, D. Diepold, M. Heule, and M. Järvisalo. The SAT Competition 2014. http://www.satcompetition.org/2014/, 2014. [5] T. A. Davis and Y. Hu. The University of Florida Sparse Matrix Collection. ACM Trans. Math. Softw.,38(1):1:1–1:25, 2011. [6] Ü. V. Catalyürek and C. Aykanat. Hypergraph-partitioning-based decomposition for parallel sparse-matrix vector multiplication. IEEE Transactions on Parallel and Distributed Systems, 10(7):673–693, Jul 1999. [7] D. A. Papa and I. L. Markov. Hypergraph Partitioning and Clustering. In T. F. Gonzalez, editor, Handbook of Approximation Algorithms and Metaheuristics. Chapman and Hall/CRC, 2007. [8] Zoltan Mann and Pal Papp. Formula partitioning revisited. In Daniel Le Berre, editor, POS-14. Fifth Pragmatics of SAT workshop, volume 27 of EPiC Series in Computing, pages 41–56. EasyChair, 2014.

opencc-by-4.0Feb 2024View details →

Data and geometries for "Understanding X-ray absorption in liquid water using triple excitations in multilevel coupled cluster theory"

Geometries and raw and processed data for the paper "Understanding X-ray absorption in liquid water using triple excitations in multilevel coupled cluster theory" This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Program (grant agreement no. 101020016 and 860553), the Research Council of Norway through FRINATEK (project no. 275506), the Swedish Research Council (grant agreement no. 2021-04521), the Independent Research Fund Denmark--Natural Sciences, DFF-RP2 (grant no. 7014-00258B) Computing resources from UNINETT Sigma2—the National Infrastructure for High Performance Computing and Data Storage in Norway (project no. NN2962k), from DeIC—Danish Infrastructure Cooperation (grant no. DeiC-DTU-N3-2023027), and from the Swiss National Supercomputing Centre  (project ID uzh1).

openother-openNov 2021View details →

MichalGath/Wayfinding Behavior in MultiLevel Buildings Dataset

This dataset captures the nuanced dynamics of wayfinding behavior within systematically manipulated virtual buildings, designed to enhance visibility between floors. It encompasses observations of human participants, shortest path agents, and cognitive agents navigating through six predefined tasks. Key features include: <ul> <li>Wayfinding behavior of 149 participants</li> <li>Wayfinding behavior of shortest path agents</li> <li>Wayfinding behavior of cognitive agents</li> </ul>