Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
54
datasets available to search
ShareScore release 0.9.0
Dataset results
54 results for “Heterogeneous Network”
Raw data of the heterogeneous Hegselmann-Krause model on network ensembles
<p># Raw data of the heterogeneous Hegselmann-Krause model on network ensembles<br> This is the raw data underlying the results of the article *«On the effects of over-compromising: heterogeneity and network effects on a bounded confidence opinion dynamics model.»*.</p> <p>For each measured combination of the parameters, there is one gzipped file. The parameters are:</p> <p> - Lower and upper bounds of the confidence interval, [ε_l, ε_u].<br> - Topology: the different types of networks and the average degree with which the networks are generated.<br> - System size<br> - Number of realizations for the parameter combination<br> <br> The single files follow a naming scheme of `data_HK_uni[{eps_l},{eps_u}]_topo={topology}_N={N}_trajrecord=0_{m}real.dat.gz`, where:</p> <p> - `{eps_l},{eps_u}` are the values of the lower and upper bounds of the confidence interval.<br> - `{topology}` contains the type of network and the average degree. The possibilities are `BA_k=10`, `ER_c=10`, `sl1`, `sl2`, and `sl3`.<br> - `N` is the system size. The sizes are powers of two.<br> - `trajrecord=0` signals the fact that file contains only the final state.<br> - `{m}` is the number of realizations.</p> <p># Data format<br> Each file contains the final state of each realization back to back. Each final state is encoded as three lines:</p> <p> - The convergence time is a single integer with a line prefix '\# iterations:'<br> - The positions of all clusters in opinion space with a line prefix '\# ' (unsorted)<br> - The number of agents in each of the clusters without a line prefix</p> <p># Folders structure<br> The files are organized as follows:</p> <p> - **`phase_plots.tar`**: contains the data for the different phase plots (full exploration of the [ε_l, ε_u] space) with `N=16384` and `m=100` realizations.<br> - **`ER`** contains the data for Erdos Renyi with mean degree of 10 (c=10)<br> - **`BA`** contains the data for Barabasi Albert with a mean degree of 10 (k=10)<br> - **`SL`** contains the data for Square lattice with first, second and third nearest neighbors (k=4, 8, 12)<br> - **`swipes.tar`** contains the data for the finite size effects study at fixed ε_l with `m=1000` realizations.<br> - **`ER`** contains the data for Erdos Renyi with mean degree of 10 (c=10) with ε_l = 0.05<br> - **`BA`** contains the data for Barabasi Albert with a mean degree of 10 (k=10) with ε_l = 0.05<br> - **`SL`** contains the data for Square lattice with third nearest neighbors (k=12) with ε_l =0.03<br> - the different videos referenced in the main text and the SM follow various naming schemes:<br> - **`scatter3D_el_eu_Smax_uni_{topology}_N=16384.mp4`**: 360° rotation of the 3D visualisation of the data leading the average phase plots.<br> - **`scatter2D_el={eps_l}_eu_Smax_extremism_{topology}_SizeEffect.mp4`**: evolution of the scatter plot leading the finite size study as a function of N.<br> - **`scatter3D_el={eps_l}_eu_Smax_extremism_{topology}_SizeEffect.mp4`**: same as before, but in 3D where the Z-axis is the extremism.<br> - **`scatter_x0_xt_{topology}_N={N}_{realization_type}.mp4`**: time evolution of the scatter plot of the opinion at time `t` versus initial opinion, color-coded with the extremism. {realization_type} can be mild, skewed or U-turn.<br> - **`traj_2D_SL_k=12_N=16384_{realization_type}.mp4`**: because of the spatial embedding, the time evolution of those realizations on the Square Lattice can be visualized in 2D.</p> <p># Python example for reading the format<br> An example script, which visualizes <S\> vs ε_u graph for the largest size of the ER case, with a function to read this format is given in `example.py`.</p>
Prospective Pilot Study of Bone Marrow and Peripheral Blood Samples From AML Patients to Characterize the Biologic Heterogeneity of the Disease Using Single Cell Network Profiling (SCNP)
ClinicalTrials.gov study NCT01203033. IPD Sharing: NO. Countries: 1. Publications: 0.
Deeper insight into Pancreatic Ductal Adenocarcinoma (PDAC) survival heterogeneity using integrative analyses of individual and global transcriptome based networks
GEO Series GSE150043. Homo sapiens. 19 samples. Type: Expression profiling by high throughput sequencing.
Modular neuroinflammatory network discovery from large-scale phenotypic screening in genetically heterogeneous mice
GEO Series GSE294998. . 0 samples. Type: Expression profiling by high throughput sequencing.
DDA-GTN: large-scale drug repurposing on drug-gene-disease heterogenous association networks using graph transformers
Open the record for dataset details and reuse information.
Heterogeneous DNA methylation changes in acute myeloid leukemia associate with cancer-specific gene expression signatures and disruption of the hematopoietic regulatory network
GEO Series GSE52590. Homo sapiens. 20 samples. Type: Methylation profiling by high throughput sequencing.
Network-based systems pharmacology identifies heterogeneity in LCK and BCL2 signaling and differential vulnerability of T-cell acute lymphoblastic leukemia to targeted therapy
GEO Series GSE158457. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Mutant EZH2 alters the epigenetic network and increases epigenetic heterogeneity in B cell lymphoma
GEO Series GSE238258. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Single-cell transcriptomics of precursor lung adenocarcinoma reveals intratumoral heterogeneity and shifting immune network
GEO Series GSE164789. Homo sapiens. 78 samples. Type: Expression profiling by high throughput sequencing.
Single-cell RNA sequencing to reveal non-parenchymal cell heterogeneity and immune network of acetaminophen-induced liver injury in mice
GEO Series GSE228305. Mus musculus. 3 samples. Type: Expression profiling by high throughput sequencing.
Mutant EZH2 alters the epigenetic network and increases epigenetic heterogeneity in B cell lymphoma (CUT&Run)
GEO Series GSE272889. Homo sapiens. 7 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Quantitative analyses of structure-function heterogeneity of individual mitochondrial components reveal an early role of small-mitochondrial-networks in priming neoplastic transformation
GEO Series GSE285728. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
DDA-GTN: large-scale drug repurposing on drug-gene-disease heterogenous association networks using graph transformers
Open the record for dataset details and reuse information.
DDA-GTN: large-scale drug repurposing on drug-gene-disease heterogenous association networks using graph transformers
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.