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.
24
datasets available to search
ShareScore release 0.9.0
Dataset results
24 results for “link clustering”
Topics in Research on International Relations as Clusters of Citation Links
<p>Data, scripts, and results of a memetic topic clustering of citation links in papers published 2011-2015 in the specialty of political science that is dealing with international relations </p> <p>Supplementary Information to the paper about "Topics as clusters of citation links to highly cited sources: The case of research on international relation" by Frank Havemann<em> </em>(published 2021 in the OA-journal<em> Quantitative Science Studies</em> 2 (1): 204–223). <a href="https://doi.org/10.1162/qss_a_00108">https://doi.org/10.1162/qss_a_00108</a></p>
Science ready spectra and their best-fitting models described in the research paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al.
<p>Science ready spectra of nine ultra-diffuse galaxies in the Coma cluster collected with the Binospec multi-object spectrograph and their best-fitting PEGASE.HR templates obtained using the NBursts full spectrum fitting code. These spectra were presented in the paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al. accepted for publication in the Astrophysical Journal on Sep/3/2019 (arXiv:1901.05489).</p> <p>Each spectrum is presented as a binary FITS table, which contains a spectrum (wavelength, flux, uncertainties), best-fitting template, best-fitting parameters (radial velocity, age, metallicity), and a pixel mask used in the fitting procedure. For six galaxies there are two files provided: (i) one-dimensional optimally extracted integrated spectrum and (ii) two dimensional spectrum for spatially resolved radial velocity information. For the remaining three galaxies, only spatially resolved spectra are provided.</p>
Figure 3. Different domain-Hybridization of Fuzzy Clustering and Hierarchical Method for Link Discovery
<p>In this paper, we propose a new hybrid algorithm, which combines the features of fuzzy<br> algorithm and hierarchical algorithm. Our algorithm decreases the number of comparisons on link<br> discovery. Using hierarchical algorithm in the first level, the data is divided into two groups. In the<br> second level the worst cluster is determined by matrix memberships and then it split. This stage is<br> repeated until the optimal number of clusters is achieved.Creating typed links, between the entities<br> of different datasets is one of the key challenges on web of data .We presented the clustering<br> approach, which decreases the number of comparisons on link discovery. The results of linking the<br> movies in LinkedMDB to corresponding movies in DBpedia and also linking the places in<br> LinkedGeoData to the places of DBpedia show the it reduces the number of comparisons without<br> loss of recall and precision. Hopefully in the future, we will be able to elevate the proposed method<br> recall to 100 % using the membership matrix.</p>
Figure 1. Workflow of approach-Hybridization of Fuzzy Clustering and Hierarchical Method for Link Discovery
<p>In this section, we present our model in more detail. Figure 1 gives an overview of the<br> workflow. Our approach is organized in two phases: first, the division of data in two clusters; thenthe determination of the worst cluster and splitting. The number of clusters is unknown, but our<br> algorithms can find this parameter based on the complexity of cluster structure.</p>
Figure 2. Decrease comparisons.-Hybridization of Fuzzy Clustering and Hierarchical Method for Link Discovery
<p>In this paper, we propose a new hybrid algorithm, which combines the features of fuzzy<br> algorithm and hierarchical algorithm. Our algorithm decreases the number of comparisons on link<br> discovery. Using hierarchical algorithm in the first level, the data is divided into two groups. In the<br> second level the worst cluster is determined by matrix memberships and then it split. This stage is<br> repeated until the optimal number of clusters is achieved.Creating typed links, between the entities<br> of different datasets is one of the key challenges on web of data .We presented the clustering<br> approach, which decreases the number of comparisons on link discovery. The results of linking the<br> movies in LinkedMDB to corresponding movies in DBpedia and also linking the places in<br> LinkedGeoData to the places of DBpedia show the it reduces the number of comparisons without<br> loss of recall and precision. Hopefully in the future, we will be able to elevate the proposed method<br> recall to 100 % using the membership matrix.</p>
Topics in Research on International Relations 2011-2015: Results from Clustering of Citation Links
<p>Supplementary information for the paper</p> <p><strong>IR Theory </strong><strong>and the Core-Periphery Structure of Global IR - Lessons from Citation Analysis</strong></p> <p>by Thomas Risse, Wiebke Wemheuer-Vogelaar, and Frank Havemann (<em>International Studies Review</em>, 2022, Vol. 4, Iss. 3, <a href="https://doi.org/10.1093/isr/viac029">doi.org/10.1093/isr/viac029</a>)</p> <p> </p> <p>Description of Files:</p> <p>File 1: Top-300 Cited Sources and Citing IR Journals<br> file name: Havemann2020top-300-sources.pdf (fourth file in list)<br> content: List of top-300 cited sources and a list of IR journals used for downloads from Web of Science</p> <p>File 2: Cited Sources in Clusters<br> file name: Havemann2020clusters.pdf (third file in list)<br> content: Lists of cited sources in link clusters and graphs of these clusters</p> <p>File 3: Citing Journals of Clusters<br> file name: citing.journals.of.clusters.pdf (second file in list)<br> content: Rank lists of publication channels with regard to numbers of papers citing papers in clusters</p> <p>File 4: Rank Lists of Cited Sources in Seven Journals Outside Web of Science<br> file name: cited-sources-in-7-journals.zip (first file in list)<br> content: Seven ASCII files</p> <p> </p> <p>A preprint of the ISR paper by Risse, Wemheuer-Vogelaar, and Havemann was published with the title "Theory Makes Global IR Hang Together. Lessons from Citation Analysis" and is available at: <a href="https://refubium.fu-berlin.de/handle/fub188/2/browse?type=affiliation&value=Otto-Suhr-Institut+f%C3%BCr+Politikwissenschaft+%2F+Arbeitsstelle+Transnationale+Beziehungen%2C+Au%C3%9Fen-+und+Sicherheitspolitik">Refubium-OSI-IR-group</a></p>
Original Data for Publication: Exceptionally Stable Dimers and Trimers of Au25 Clusters Linked with a Bidentate Dithiol: Synthesis, Structure and Chirality Study.
<p>Original Data for Publication: Exceptionally Stable Dimers and Trimers of Au25 Clusters Linked with a Bidentate Dithiol: Synthesis, Structure and Chirality Study. Angewandte Chemie, 2023</p>
Data from "Linked Coupled Cluster Monte Carlo"
<p>We consider a new formulation of the stochastic coupled cluster method in terms of the similarity transformed Hamiltonian. We show that improvement in the granularity with which the wavefunction is represented results in a reduction in the critical population required to correctly sample the wavefunction for a range of systems and excitation levels and hence leads to a substantial reduction in the computational cost. This development has the potential to substantially extend the range of the method, enabling it to be used to treat larger systems with excitation levels not easily accessible with conventional deterministic methods.</p>
FIGURE 3 in Linking operational clustered taxonomic units (OCTUs) from parallel ultra sequencing (PUS) to nematode species
FIGURE 3. Numbers of OCTUs formed at 99% within-OCTU similarity for each recovered nematode species. Brackets group species by the persistence (Always, Often, Never) of Head-Tail structure of OCTUs. Error bars indicate standard deviation across 7 metagenetic datasets.
FIGURE 5 in Linking operational clustered taxonomic units (OCTUs) from parallel ultra sequencing (PUS) to nematode species
FIGURE 5. Estimated (using the Head-Tail guidelines) number of nematode species from OCTUs generated at 99% within-OCTU similarity for sequencing reads recovered from soil, litter, and canopy habitats at La Selva Biological Station in Costa Rica.
FIGURE 2 in Linking operational clustered taxonomic units (OCTUs) from parallel ultra sequencing (PUS) to nematode species
FIGURE 2. Number of nematode species recovered using different levels of within-OCTU (operational clustered taxonomic units) similarity. Bars represent means across 7 metagenetic datasets. Error bars indicate standard deviation. Note: out of 41 used nematode species, three did not amplify (Porazinska et al. 2009a).
FIGURE 4 in Linking operational clustered taxonomic units (OCTUs) from parallel ultra sequencing (PUS) to nematode species
FIGURE 4. An example of OCTUs generated at 99% within-OCTU similarity matching Bursaphelenchus seani illustrating Head-Tail structure associated with the presence of within-species/individual variation of the SSU repeats. OCTUPUS generates a series of indices allowing OCTU ranking. When ranked by "Bit Score" and Reads, OCTUs can be divided into two categories: 1. Head – a single OCTU (27) that contains most abundant (63) and most common sequencing reads and matches a database (DB match) sequence (B. seani 175) at the highest value of bit score (525), Evalue and % Similarity (100) across the OCTU length. 2. Tail – many OCTUs each with fewer than the Head OCTU reads and lower values of bit score and % similarity.
FIGURE 1 in Linking operational clustered taxonomic units (OCTUs) from parallel ultra sequencing (PUS) to nematode species
FIGURE 1. Nematode OCTUs generated at different levels of within-OCTU similarity. Points represent the means across 7 metagenetic datasets, bars represent standard deviation.
Data from: Defector clustering is linked to cooperation in a pathogenic bacterium
Spatial clustering is thought to favour the evolution of cooperation because it puts cooperators in a position to help each other. However, clustering also increases competition. The fate of cooperation may depend on how much cooperators cluster relative to defectors, but these clustering differences have not been the focus of previous models and experiments. By competing siderophore-producing cooperator and defector strains of the opportunistic pathogen Pseudomonas aeruginosa in experimental microhabitats, we found that at the spatial scale of individual interactions, cooperator clustering lowers cooperation, but defector clustering favours cooperation. A theoretical model and individual-based simulations show these counterintuitive effects can arise when competition and cooperation occur at a single resource-determined scale, with population dynamics crucially allowing cooperators and defectors to cluster differently. The results suggest that cooperation relies on the regulation of sufficient defector clustering relative to cooperator clustering, which may be important in bacteria, social amoeba, and cancer inhibition.
Data from "Linked Coupled Cluster Monte Carlo"
<p>We consider a new formulation of the stochastic coupled cluster method in terms of the similarity transformed Hamiltonian. We show that improvement in the granularity with which the wavefunction is represented results in a reduction in the critical population required to correctly sample the wavefunction for a range of systems and excitation levels and hence leads to a substantial reduction in the computational cost. This development has the potential to substantially extend the range of the method, enabling it to be used to treat larger systems with excitation levels not easily accessible with conventional deterministic methods.</p>
Data from: Linkage disequilibrium clustering-based approach for association mapping with tightly linked genome-wide data
Open the record for dataset details and reuse information.
Data from: Defector clustering is linked to cooperation in a pathogenic bacterium
Open the record for dataset details and reuse information.
SAM domain polymerization links subnuclear clustering of PRC1 to silencing detected using expression data and ChIP experiments
GEO Series GSE38154. Mus musculus. 10 samples. Type: Expression profiling by array; Genome binding/occupancy profiling by array; Genome binding/occupancy profiling by high throughput sequencing.
N'-(1-phenylethylidene)-benzohydrazide cytotoxicity is LSD1 independent and linked to Fe-S cluster disruption in Ewing sarcoma
GEO Series GSE286100. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
NSD2 overexpression links drives clustered chromatin and transcriptional changes in a subset of insulated domains of insulated domains
GEO Series GSE131651. Homo sapiens. 48 samples. Type: Expression profiling by high throughput sequencing; Other; Genome binding/occupancy profiling by high throughput sequencing.
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.