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.
151
datasets available to search
ShareScore release 0.9.0
Dataset results
151 results for “network scaling”
Data from: The influence of spatial sampling scales on ant-plant interaction network architecture
Open the record for dataset details and reuse information.
Data from: Ancient drainage networks mediated a large-scale genetic introgression in the East Asian freshwater snails
Open the record for dataset details and reuse information.
Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens
Open the record for dataset details and reuse information.
Ecological network complexity scales with area
Open the record for dataset details and reuse information.
Codes and datasets associated with the paper "Day-ahead Wind Power Predictions at Regional Scales: Post-processing Operational Weather Forecasts with a Hybrid Neural Network"
<p>The jupyter notebooks and datasets associated with the EEM20 forecasts are available here. More details will be provided shortly. </p> <p>Please check the EEM20 website (<a href="https://eem20.eu/forecasting-competition/">https://eem20.eu/forecasting-competition/</a>) for the details of the forecasting competition. </p>
Data from: Large-scale functional networks identified from resting-state EEG using spatial ICA
Several methods have been applied to EEG or MEG signals to detect functional networks. In recent works using MEG/EEG and fMRI data, temporal ICA analysis has been used to extract spatial maps of resting-state networks with or without an atlas-based parcellation of the cortex. Since the links between the fMRI signal and the electromagnetic signals are not fully established, and to avoid any bias, we examined whether EEG alone was able to derive the spatial distribution and temporal characteristics of functional networks. To do so, we propose a two-step original method: 1) An individual multi-frequency data analysis including EEG-based source localisation and spatial independent component analysis, which allowed us to characterize the resting-state networks. 2) A group-level analysis involving a hierarchical clustering procedure to identify reproducible large-scale networks across the population. Compared with large-scale resting-state networks obtained with fMRI, the proposed EEG-based analysis revealed smaller independent networks thanks to the high temporal resolution of EEG, hence hierarchical organization of networks. The comparison showed a substantial overlap between EEG and fMRI networks in motor, premotor, sensory, frontal, and parietal areas. However, there were mismatches between EEG-based and fMRI-based networks in temporal areas, presumably resulting from a poor sensitivity of fMRI in these regions or artefacts in the EEG signals. The proposed method opens the way for studying the high temporal dynamics of networks at the source level thanks to the high temporal resolution of EEG. It would then become possible to study detailed measures of the dynamics of connectivity.
Data from: Improving short-term information spreading efficiency in scale-free networks by specifying top large-degree vertices as the initial spreaders.
The positive function of initially influential vertices could be exploited to improve spreading efficiency for short-term spreading in scale-free networks. However, the selection of initial spreaders depends on the specific scenes. The selection of initial spreaders needs to offer low complexity and low power consumption for short-term spreading. In this paper, we propose a selection strategy for efficiently spreading information by specifying a set of top large-degree vertices as the initially informed vertices. The essential idea behind the proposed selection strategy is to exploit the significant diffusion of the top large-degree vertices at the beginning of spreading. To evaluate the positive impact of initially influential vertices, we first build an information spreading model in the Barabási-Albert (BA) scale-free network; next, we design 54 comparative Monte Carlo experiments based on a benchmark strategy and the proposed selection strategy in different BA scale-free network structures. Experimental results indicate that (1) the proposed selection strategy significantly can improve spreading efficiency in the short-term spreading and (2) both network size and number of hubs have a strong impact on spreading efficiency, while the number of initially informed vertices has a weak impact. The proposed selection strategy can be employed in short-term spreading, such as sending warnings or crisis information spreading or information spreading in emergency training or realistic emergency scenes.
Data from: The geographical variation of network structure is scale dependent: understanding the biotic specialization of host-parasitoid networks
Research on the structure of ecological networks suggests that a number of universal patterns exist. Historically, biotic specialization has been thought to increase towards the Equator. Yet, recent studies have challenged this view showing non-conclusive results. Most studies analysing the geographical variation in biotic specialization focus, however, only on the local scale. Little is known about how the geographical variation of network structure depends on the spatial scale of observation (i.e., from local to regional spatial scales). This should be remedied, as network structure changes as the spatial scale of observation changes, and the magnitude and shape of these changes can elucidate the mechanisms behind the geographical variation in biotic specialization. Here we analyse four facets of biotic specialization in host-parasitoid networks along gradients of climatic constancy, classifying the networks according to their spatial extension (local or regional). Namely, we analyse network connectance, consumer diet overlap, consumer diet breadth, and resource vulnerability at both local and regional scales along the gradients of both current climatic constancy and historical climatic change. While at the regional scale none of the climatic variables are associated to biotic specialization, at the local scale, network connectance, consumer diet overlap, and resource vulnerability decrease with current climatic constancy, whereas consumer generalism increases (i.e., broader diet breadths in tropical areas). Similar patterns are observed along the gradient of historical climatic change. We provide an explanation based on different beta-diversity for consumers and resources across the geographical gradients. Our results show that the geographical gradient of biotic specialization is not universal. It depends on both the facet of biotic specialization and the spatial scale of observation.
Dataset for "First Observations of Large Scale Traveling Ionospheric Disturbances Using Automated Amateur Radio Receiving Networks"
<p># 20171103.rbn_pskreporter_wspr_data.csv<br> #<br> # This data file contains the amateur radio spot data used in the manuscript:<br> # "First Observations of Large Scale Traveling Ionospheric Disturbances Using Automated Amateur Radio Receiving Networks"<br> # by Nathaniel A. Frissell W2NAF, Stephen R. Kaeppler AD0AE, Diego F. Sanchez KD2RLM, Gareth W. Perry KD2SAK,<br> # William D. Engelke AB4EJ, Philip J. Erickson W1PJE, Anthea J. Coster, J. Michael Ruohoniemi, and Joseph B. H. Baker<br> #<br> # Observations are provided courtesy of:<br> # 1. The Weak Signal Propagation Reporting Network (WSPRNet, https://www.wsprnet.org/)<br> # Operated by Bruce Walker W1BW<br> #<br> # 2. The Reverse Beacon Network (RBN, http://www.reversebeacon.net/)<br> # Operated by Nick Sinanis F5VIH/SV3SJ, David Pascoe KM3T, Mark Glenn K7MJG, Peter Smith N4ZR, Felipe Ceglia PY1NB/CT7ANO, and Dick Williams W3OA<br> #<br> # 3. PSKReporter (https://pskreporter.info/)<br> # Operated by Philip Gladstone N1DQ<br> #<br> # Explanation of Data Columns:<br> # freq: Frequency [kHz]<br> # ut: Time of Observation in Universal Time (UT)<br> # source: Observation Network (WSPRNet, RBN, or PSKReporter)<br> # tx_grid: Transmitter Maidenhead Gridsquare<br> # rx_grid: Receiver Maindenhead Gridsquare<br> # tx_lat: Transmitter Latitude (deg)<br> # tx_long: Transmitter Longitude (deg)<br> # rx_lat: Receiver Latitude (deg)<br> # rx_long: Receiver Longitude (deg)<br> # dist_Km: Ground-Level Great Circle Distance between Transmitter and Receiver (km)<br> # md_lat: Midpoint between Transmitter and Receiver Latitude (deg)<br> # md_long: Midpoint between Transmitter and Receiver Longitude (deg)<br> # slt_mid: Solar Local Time of Midpoint (hr)<br> #<br> # This datafile is a subset of the full WSPRNet/RBN/PSKReporter data, filtered with the following parameters:<br> # ut min: 2017-11-03 00:00:00<br> # ut max: 2017-11-03 23:59:59<br> # freq min: 14000.382<br> # freq max: 14280.0<br> # md_lat min: 20.004737453151876<br> # md_lat max: 54.99977112001497<br> # md_long min: -129.99845895196913<br> # md_long max: -60.008771378901315<br> # dist_Km min: 0.0<br> # dist_Km max: 19861.14355003741</p>
Supplementary material for "A large-scale demonstration and sustainability evaluation of ductile-porous vascular networks for self-healing concrete"
Open the record for dataset details and reuse information.
Rice genome-scale network integration reveals transcriptional regulators of grass cell wall synthesis
<p><span><span><span><span><span><span><span><span><span><span><span>Grasses have evolved distinct cell wall composition and patterning relative to dicotyledonous plants. However, despite the importance of this plant family, transcriptional regulation of its cell wall biosynthesis is poorly understood. To identify grass cell wall-associated transcription factors, we constructed the Rice Combined mutual Ranked Network (RCRN). The RCRN covers >90% of annotated rice (<i>Oryza sativa</i>) genes, is high quality, and includes most grass-specific cell wall genes, such as mixed-linkage glucan synthases and hydroxycinnamoyl acyltransferases. Comparing the RCRN and an equivalent <i>Arabidopsis </i>network suggests that grass orthologs of most genetically verified eudicot cell wall regulators also control this process in grasses, but some vary significantly in network connectivity between these divergent species. Reverse genetics, yeast-one-hybrid, and protoplast-based assays reveal that OsMYB61a activates a grass-specific acyltransferase promoter, which confirms network predictions and supports grass-specific cell wall synthesis genes being incorporated into conserved regulatory circuits. In addition, 10 of 15 tested transcription factors, including six novel <u>w</u>all-<u>a</u>ssociated regulators (WAP1, WACH1, WAHL1, WADH1, OsMYB13a, and OsMYB13b), alter abundance of cell wall-related transcripts when transiently expressed. The results highlight the quality of the RCRN for examining rice biology, provide insight into the evolution of cell wall regulation, and identify network nodes and edges that are possible leads for improving cell wall composition.</span></span></span></span></span></span></span></span></span></span></span></p>
Non-Invasive Brain Stimulation to Control Large-Scale Brain Networks
ClinicalTrials.gov study NCT04680481. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Improving short-term information spreading efficiency in scale-free networks by specifying top large-degree vertices as the initial spreaders.
Open the record for dataset details and reuse information.
Data from: Designing efficient hybrid strategies for information spreading in scale-free networks
Open the record for dataset details and reuse information.
Data from: The geographical variation of network structure is scale dependent: understanding the biotic specialization of host-parasitoid networks
Open the record for dataset details and reuse information.
Data from: Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis
Open the record for dataset details and reuse information.
Data from: Large-scale functional networks identified from resting-state EEG using spatial ICA
Open the record for dataset details and reuse information.
Rice genome-scale network integration reveals transcriptional regulators of grass cell wall synthesis
Open the record for dataset details and reuse information.
Data from: Large-scale assessment of intra- and inter-annual breeding success using a remote camera network
Open the record for dataset details and reuse information.
DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning (NG Capture-C)
GEO Series GSE137435. Homo sapiens. 4 samples. Type: Other.
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.