Find research datasets worth reusing

opencc-by-4.0Aug 2019View details →

Graph topological features extracted from expression profiles of neuroblastoma patients

Introduction This dataset contains the data described in the paper titled "A deep neural network approach to predicting clinical outcomes of neuroblastoma patients." by Tranchevent, Azuaje and Rajapakse. More precisely, this dataset contains the topological features extracted from graphs built from publicly available expression data (see details below). This dataset does not contain the original expression data, which are available elsewhere. We thank the scientists who did generate and share these data (please see below the relevant links and publications).   Content File names start with the name of the publicly available dataset they are built on (among "Fischer", "Maris" and "Versteeg"). This name is followed by a tag representing whether they contain raw data ("raw", which means, in this case, the raw topological features) or TF formatted data ("TF", which stands for TensorFlow). This tag is then followed by a unique identifier representing a unique configuration. The configuration file "Global_configuration.tsv" contains details about these configurations such as which topological features are present and which clinical outcome is considered. The code associated to the same manuscript that uses these data is at <a href="https://gitlab.com/biomodlih/SingalunDeep">https://gitlab.com/biomodlih/SingalunDeep</a>. The procedure by which the raw data are transformed into the TensorFlow ready data is described in the paper.   File format All files are TSV files that correspond to matrices with samples as rows and features as columns (or clinical data as columns for clinical data files). The data files contain various sets of topological features that were extracted from the sample graphs (or Patient Similarity Networks - PSN). The clinical files contain relevant clinical outcomes. The raw data files only contain the topological data. For instance, the file "Fischer_raw_2d0000_data_tsv" contains 24 values for each sample corresponding to the 12 centralities computed for both the microarray (Fischer-M) and RNA-seq (Fischer-R) datasets. The TensorFlow ready files do not contain the sample identifiers in the first column. However, they contain two extra columns at the end. The first extra column is the sample weights (for the classifiers and because we very often have a dominant class). The second extra column is the class labels (binary), based on the clinical outcome of interest.   Dataset details The Fischer dataset is used to train, evaluate and validate the models, so the dataset is split into train / eval / valid files, which contains respectively 249, 125 and 124 rows (samples) of the original 498 samples. In contrast, the other two datasets (Maris and Versteeg) are smaller and are only used for validation (and therefore have no training or evaluation file). The Fischer dataset also has more data files because various configurations were tested (see manuscript). In contrast, the validation, using the Maris and Versteeg datasets is only done for a single configuration and there are therefore less files. For Fischer, a few configurations are listed in the global configuration file but there is no corresponding raw data. This is because these items are derived from concatenations of the original raw data (see global configuration file and manuscript for details).   References This dataset is associated with Tranchevent L., Azuaje F.. Rajapakse J.C., A deep neural network approach to predicting clinical outcomes of neuroblastoma patients. If you use these data in your research, please do not forget to also cite the researchers who have generated the original expression datasets. Fischer dataset: <ul> <li>Zhang W. et al., Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biology 16(1) (2015). doi:10.1186/s13059-015-0694-1</li> <li>Wang C. et al., The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat. Biotechnol. 32(9), 926–932. doi:10.1038/nbt.3001</li> </ul> Versteeg dataset: <ul> <li>Molenaar J.J. et al., Sequencing of neuroblastoma identifies chromothripsis and defects in neuritogenesis genes. Nature 483(7391), 589–593. doi:10.1038/nature10910</li> </ul> Maris dataset: <ul> <li>Wang Q. et al., Integrative genomics identifies distinct molecular classes of neuroblastoma and shows that multiple genes are targeted by regional alterations in DNA copy number. Cancer Res. 66(12), 6050–6062. doi:10.1158/0008-5472.CAN-05-4618</li> </ul>

opencc-by-4.0Oct 2019View details →

Common biochemical and topological properties of metabolic genes recurrently dysregulated in tumors

Although tumors exhibit numerous metabolic alterations, it’s unclear if common objectives and constraints underlie diverse metabolic changes. Here we interpret cancer gene expression, copy number variation, and survival data using a computational model, MetOncoFit. MetOncoFit evaluates142 metabolic features that can impact tumor fitness, including enzyme catalytic activity, pathway association, network topological attributes, and reaction flux. Meta-analysis of tumor databases using MetOncoFit revealed that metabolic enzymes with high catalytic activity were frequently up-regulated in many tumors and associated with poor survival. MetOncoFit also identified metabolites that were hot-spots of dysregulation. MetOncoFit illuminates how enzyme activity and metabolic network architecture influences tumorigenesis.

opencc-by-4.0Aug 2024View details →

Dataset for publication "Assessing the techno-economic benefits of LEMs for different grid topologies and prosumer shares"

This dataset contains all the results and scenarios for this paper. Each region has two files in which you can find all the scenarios for the market design with and without the local energy market. The folders within these files contain the results for one scenario for a given share of PV, EVs, and heat pumps (HPs). The results folders can also be used to rerun the scenarios. To do so, place the respective scenario in the tool's scenario folder. USE CASE: If you do not want to check all the files of the results or want to rerun the scenarios, use this version. If you want to download the relevant files for the analysis and figure creation of the paper, use the <a href="https://zenodo.org/records/13907329" target="_blank" rel="noopener">compact version</a>. TOOL: <a href="https://github.com/TUM-Doepfert/lemlab/tree/doepfert2024_lem">lemlab</a>

opencc-by-4.0Oct 2024View details →

Compact version: Dataset for publication "Assessing the techno-economic benefits of LEMs for different grid topologies and prosumer shares"

This is the compact version of the results. They contain only the relevant files for the analysis and figure creation of the paper. USE CASE: If you do not want to access every single file of the results or rerun the scenarios, you should use the compact version. TOOL: <a href="https://github.com/TUM-Doepfert/lemlab/tree/doepfert2024_lem">lemlab</a>

opencc-by-4.0Aug 2021View details →

Experimental data for Berry curvature dipole senses topological transition in a moiré superlattice

This experimental dataset was used in our study of "Berry curvature dipole senses topological transition in a moiré superlattice".

opencc-by-4.0Nov 2022View details →

Topologies and structures of the RND transporters MexB, MexF, and MexY of Pseudomonas aeruginosa.

The file contains the topologies and the structures of the RND transporters MexB, MexF, and MexY of Pseudomonas aeruginosa used to identify a common recognition topology in these transporters by means of computer simulations.

opencc-by-4.0Oct 2023View details →

Dataset for "Light-Induced Non-Thermal Phase Transition To The Topological Crystalline Insulator State In SnSe"

Dataset relative to the publication "Light-Induced Non-Thermal Phase Transition To The Topological Crystalline Insulator State In SnSe" Stefano Mocatti, Giovanni Marini, Matteo Calandra J. Phys. Chem. Lett. 2023, 14, XXX, 9329–9334 DOI: <a href="https://doi.org/10.1021/acs.jpclett.3c02450">10.1021/acs.jpclett.3c02450</a> The dataset contains the following folders Conv: Convergence tests for total energy, stress tensor and phonon frequencies Pseudo: All pseudopotentials used  Figures: All the data and script to reproduce the figures reported in the manuscript Fign: Simulation data referred to the nth figure of the manuscript The dataset contains also the preprint version of the manuscript (preprint.pdf) and the supporting information file (SI.pdf)

opencc-by-4.0Aug 2020View details →

Magnon Modes of Microstates and Microwave-Induced Avalanche in Kagome Artificial Spin Ice with Topological Defects

The attached folder contains the dataset for the manuscript entitled "Magnon Modes of Microstates and Microwave-Induced Avalanche in Kagome Artificial Spin Ice with Topological Defects".

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Anatomical partitioning has little influence in topologies from Bayesian phylogenetic analyses of morphological data

Morphological data is a fundamental source of evidence to reconstruct the Tree of Life, and Bayesian phylogenetic methods are increasingly being used for this task, along with, or instead of, traditional parsimony approaches. Bayesian phylogenetic analyses require the use of proper evolutionary models and their performance have been intensively studied in the past few years, with significant improvements to our knowledge regarding their performance. Notwithstanding, it was only recently that partitioned models for morphology received attention in studies of empirical data, but a systematic evaluation of its performances using simulations was never performed. Here we evaluate the influence of partitioned models defined by anatomical criterion in the precision and accuracy of consensus tree topologies, evaluating the possible negative effects of under and overpartitioning. For that, we analysed datasets simulated using parameters and properties of two empirical datasets, using Bayesian phylogenetic analyses in MrBayes. Additionally, we reanalysed 32 empirical datasets for diverse groups of vertebrates, applying unpartitioned and partitioned models. We found that in general, partitioning by anatomy has little to no influences in the performance of Bayesian phylogenetic methods in respect to the metrics studied here, with analyses under alternative partitioning schemes presenting very similar tree precision and accuracy. We discuss the possible reasons for the disagreement between the results obtained here and previous studies for empirical morphological data, and with empirical and simulation studies of molecular data, discussing the adequacy of anatomical partitioning relative to alternative methods to partition morphological datasets and how morphological and molecular partitioning are related.

opencc-zeroDec 2020View details →

opencc-by-4.0Apr 2012View details →

Topology and structure of Au144(SRNH3+)60 from "Atomistic Simulations of Functional Au144(SR)60 Gold Nanoparticles in Aqueous Environment"

Positively charged monolayer-protected gold nanoparticles (AuNPs) structure and topology files for GROMACS used in DOI: 10.1021/jp301094m.  The final structure of the simulation reported in DOI: 10.1021/jp301094m for the neutral case is provided. The gold nanoparticle contain a core of 144 Au atoms and 60 functionalized alkanethiol side groups (undecanyl chain, R = C11H22), each possessing a positively charged amonium terminal group. When using this structure do not forget to cite DOI: 10.1021/jp301094m.  NOTE1: Different versions for the topology files are provided of both AuNPs. All versions were used for the publication. The changes only affect the core surface and therefore had no influence in the reported properties. Still we recommentd using the latest version: AU144SRNH360_v3.itp. NOTE2: The original simulations used GROMACS 4.0.5. The files should work, however, as well up to GROMACS 4.6.7.

opencc-by-4.0Apr 2012View details →

Topology and structure of Au144(SRCOO-)60 from "Atomistic Simulations of Functional Au144(SR)60 Gold Nanoparticles in Aqueous Environment"

Negatively charged monolayer-protected gold nanoparticles (AuNPs) structure and topology files for GROMACS used in DOI: 10.1021/jp301094m.  The final structure of the simulation reported in DOI: 10.1021/jp301094m for the neutral case is provided. The gold nanoparticle contain a core of 144 Au atoms and 60 functionalized alkanethiol side groups (undecanyl chain, R = C11H22), each possessing a negatively charged carboxylic terminal group. When using this structure do not forget to cite DOI: 10.1021/jp301094m.  NOTE1: Different versions for the topology files are provided of both AuNPs. All versions were used for the publication. The changes only affect the core surface and therefore had no influence in the reported properties. Still we recommentd using the latest version: AU144SRCOO60_v2.itp. NOTE2: The original simulations used GROMACS 4.0.5. The files should work, however, as well up to GROMACS 4.6.7.

opencc-by-sa-4.0Feb 2016View details →

Communication Characterization and Optimization of Applications Using Topology-Aware Task Mapping on Large Supercomputers (Data)

Auxiliary materials for ICPE 2016 paper titled: "Communication Characterization and Optimization of Applications Using Topology-Aware Task Mapping on Large Supercomputers".

Spatial Evolve Algorithm Results for Erdos Renyi Topology Median Normalized Rank - MSc Dissertation

A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been the Erdős Rényi random network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size.

Spatial Evolve Algorithm Results for Random Topology Median Normalized Rank - MSc Dissertation

A data set containing the results of the spatial evolve lookup algorithm. The topologies used for the spatial tournaments has been random between, small world, random and complete. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size.

Spatial Evolve Algorithm Results for Watts Strogatz Topology Median Normalized Rank - MSc Dissertation

A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been the Watts Strogatz small world network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size.

Spatial Evolve Algorithm Results for Erdős Rényi Topology Median Normalized Rank - MSc Dissertation

A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been the Erdős Rényi random network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the strategies list, deterministic and non and on the sample size.

Spatial Evolve Algorithm Results for Complete Topology Median Normalized Rank - MSc Dissertation

A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been a complete network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size.

Spatial Evolve Algorithm Results for Random Topology Minimum Normalized Rank - MSc Dissertation

A data set containing the results of the spatial evolve lookup algorithm. The topologies used for the spatial tournaments has been random between, small world, random and complete. The objective function taken into account has been the minimum normalized rank. Two files are contained here based on the sample size. All 132 strategies of the Axelrod have been used.

opencc-zeroNov 2023View details →

dryad40/100

Data from: Topological transitions of the generalized Pancharatnam-Berry phase

Distinct from the dynamical phase, in a cyclic evolution, a system's state may acquire an additional component, a.k.a. geometric phase. Recently, it has been demonstrated that geometric phases can be induced by a sequence of generalized measurements implemented on a single qubit. Furthermore, it has been predicted that such geometric phases may exhibit a topological transition as a function of the measurement strength. We demonstrate and study this transition experimentally by employing an optical platform where the qubit is represented by the polarisation of light and the weak measurement is performed by means of coupling with the spatial degree of freedom. Our protocol can be interpreted in terms of environment-induced geometric phases, whose values are topologically determined by the environment-system coupling strength. Our results show that the two limits of geometric phase induced by either sequences of either weak or projective measurements are topologically distinct.