Find research datasets worth reusing

opencc-by-4.0Sep 2023View details →

Files for training purposes - Cluster usage training session @BIOI2

3 sets of inputs to go with our cluster usage training session @BIOI2: - fastq extract top 1000 from SRR9732589 - full-length homologs outputted by a BLAST search with NCBI of human ASF1A protein sequence (<a href="https://www.uniprot.org/uniprotkb/Q9Y294/entry#sequence">Q9Y294</a>) - 5 AlphaFold2 models of yeast Protein transport protein SEC39 (<a href="http://www.uniprot.org/uniprotkb/Q6CWC7/entry#sequences">Q6CWC7</a>) (with simplified names) and its X-ray structure <a href="https://www.rcsb.org/structure/8FTU">8FTU</a>

opencc-by-4.0Jul 2024View details →

POLITO Radiosonde Cluster: In-Field Experiment Dataset

This dataset comprises data from in-field experiments involving a cluster of POLITO radiosondes launched under various atmospheric conditions: Launch 1. In-field cluster launch of 10 radiosondes on a cloudy day within the Alpine Atmospheric Boundary Layer (ABL), St. Barthelemy, Valle d'Aosta, November 3, 2022.  Launches 2-3. Two in-field cluster launches with 10 and 12 radiosondes within the Chilbolton ABL, UK (July 5-6, 2023, WESCON campaign).  Launch 4. In-field cluster launch of 10 radiosondes within the Udine, Italy ABL (June 19, 2024, CISM). Launches 5-6. Two in-field cluster launches with 5 and 9 radiosondes respectively within the Chilbolton ABL, UK (September 24-26, 2023, AMOF application). For more info and questions, please contact to shahbozbek.abdunabiev@polito.it, daniela.tordella@polito.it.

opencc-zeroOct 2021View details →

Data from: The stochastic dynamics of early epidemics: probability of establishment, initial growth rate, and infection cluster size at first detection

Emerging epidemics and local infection clusters are initially prone to stochastic effects that can substantially impact the epidemic trajectory. While numerous studies are devoted to the deterministic regime of an established epidemic, mathematical descriptions of the initial phase of epidemic growth are comparatively rarer. Here, we review existing mathematical results on the epidemic size over time, and derive new results to elucidate the early dynamics of an infection cluster started by a single infected individual. We show that the initial growth of epidemics that eventually take off is accelerated by stochasticity. These results are critical to improve early cluster detection and control. As an application, we compute the distribution of the first detection time of an infected individual in an infection cluster depending on the testing effort, and estimate that the SARS-CoV-2 variant of concern Alpha detected in September 2020 first appeared in the United Kingdom early August 2020. We also compute a minimal testing frequency to detect clusters before they exceed a given threshold size. These results improve our theoretical understanding of early epidemics and will be useful for the study and control of local infectious disease clusters.

opencc-by-4.0Nov 2021View details →

Impact of Community Masking on COVID-19: A Cluster-Randomized Trial in Bangladesh

We ran a randomized trial of mask promotion in Bangladesh; the intervention increased mask-use and reduced symptomatic SARS-CoV-2 infections.

opencc-zeroNov 2021View details →

Vesicles clustering around Wdr35-/- cilia lack electron dense decorations although electron-dense clathrin coated vesicles are still observed budding from the mutant plasma membrane (Figure 7- source data 1)

Intraflagellar transport (IFT) is a highly conserved mechanism for motor-driven transport of cargo within cilia, but how this cargo is selectively transported to cilia is unclear. WDR35/IFT121 is a component of the IFT-A complex best known for its role in ciliary retrograde transport. In the absence of WDR35, small mutant cilia form but fail to enrich in diverse classes of ciliary membrane proteins. In Wdr35 mouse mutants, the non-core IFT-A components are degraded and core components accumulate at the ciliary base. We reveal deep sequence homology of WDR35 and other IFT-A subunits to α and ß' COPI coatomer subunits, and demonstrate an accumulation of 'coat-less' vesicles which fail to fuse with Wdr35 mutant cilia. We determine that recombinant non-core IFT-As can bind directly to lipids and provide the first in-situ evidence of a novel coat function for WDR35, likely with other IFT-A proteins, in delivering ciliary membrane cargo necessary for cilia elongation.

opencc-by-4.0Nov 2021View details →

LISC catalogue of Galactic disk star clusters in Gaia EDR3

This is a database of the color-magnitude diagrams (CMDs) and fundamental parameters of star clusters in LISC catalogue. It corresponds to the study of Zhongmu Li et al. in 2021, which was submitted to ApJS. When one use these data, please cite to that work. Note that (V-I) color and V magnitude in the observed CMD data which are given in the database are transformed from the Gaia EDR3 magnitudes using some fitting correlations.  The database contains three directories. These directories are explained as follows. (1)The first directory "all_clusters" gives the basic information and the observed CMD data of 3597 clusters  in the work of Li et al.(2021). These clusters are all searched clusters by FOF and compared with previous catalogs. It has two subfolders and two files with '.dat' suffix. The first subfolder "new_candidates" contains observed CMD data of 868 clusters. These clusters are the different clusters in the previous catalogues, i.e., Liu & Pang(2019), Kharchenko et al.(2013), Cantat-Gaudin et al.(2018), Cantat-Gaudin et al.(2019), Bica et al.(2019), Castro-Ginard et al.(2019), Castro-Ginard et al.(2020), and Casado(2021) ,and the catalogue proposed in this work. Each file is named "obcmd_LISC****. dat".There are 10 columns in each file. The colums are for V, V-I, BP, BP-RP, ra, dec, parallax, rv, e_ra, e_dec respectively. The first and second columns are for V magnitude and V-I color which are transformed from the Gaia EDR3 magnitudes using some fitting correlations, and the other columns are the information of single star from Gaia EDR3. The second subfolder "matched_clusters" contains observed CMD data of 2729 clusters. These clusters are the same cluster in the previous catalogues, i.e., Liu & Pang(2019), Kharchenko et al.(2013), Cantat-Gaudin et al.(2018), Cantat-Gaudin et al.(2019), Bica et al.(2019), Castro-Ginard et al.(2019), Castro-Ginard et al.(2020), and Casado(2021), and the catalogue proposed in this work. The name and content of each file are the same as the file in the first subfolders. The first file "new_candidates" gives basic information of 868 candidates which corresponds to the candidates in the first subfolder. The second file "match_clusters.dat" gives basic information of 2729 clusters which corresponds to the clusters in the second subfolder of this directory. (2)The second directory "new_clusters" gives observed CMD data, best-fitted CMD data and best-fit parameters of 61 unknown clusters before. These clusters come from 868 star clusters that have not been matched by other catalogues. They were identified as potential new clusters in Li et al.(2021). These clusters are fitted via ASPS model and Powerful CMD code. Three parts of this directory are as follows. The first subfolder "obcmd" contains observed CMDs of 61 newly found clusters. The name and content of each file are the same as the file in the first subfolders of the first directory. The second subfolder "fitcmd" contains best-fitted CMDs of 61 new found clusters. These clusters are fitted by the ASPS model and Powerful CMD code. Each file is named "fitcmd_LISC****. dat". The first two lines give the best fitting parameters of the cluster. Note that "age0" is the age of the youngest star in the cluster if the stellar population type of cluster is composite stellar population(CSP). The first and second columns from the fourth row are for (V-I) color and V magnitude. The file "fit_parameter.dat" contains best-fit parameters of 61 new found clusters. There are 16 columns in this file. The colums are for id, ra, dec, plx,sig_plx, μαcosδ,sig_μαcosδ, μδ,sig_μδ, rsc, m-M, E(V-I), Z, t/t_range, f_bin and f_rot respectively. These parameters are the best-fit parameters by the ASPS model and Powerful CMD code. They correspond to the contents of a manuscript that was submitted to ApJS. (3)The third directory "known_clusters" gives observed CMD data, best-fitted CMD data and best-fit parameters of 594 known clusters. These clusters come from 2729 star clusters that have been matched by other catalogues. They have relatively clear CMDs structure and be fitted via ASPS model and Powerful CMD code. This directory have two subfolders. The first subfolder "good_fit" have the same structure with the second directory "new_clusters" but for 309 known clusters that have high quality CMDs and are fitted well.  The second subfolder "other" have the same structure with the second directory "new_clusters" but for 285 known clusters that did not have high quality CMDs or are not well fitted. If you have any problems when using these data, send an email to Prof. Dr. Zhongmu LI, at email: zhongmuli@126.com.

opencc-by-4.0Dec 2021View details →

Self-Limiting Earthquake Dynamics and Spatio-Temporal Clustering of Seismicity Enabled by Off-Fault Plasticity

Earthquakes are among nature’s deadliest and costliest hazards. Physics-based simulations are essential for overcoming the lack of data and elucidating the complex patterns of earthquakes. Enabled by a novel numerical scheme, this work discovers a new mechanism for regulating earthquake dynamics that emerges due to the co-evolution of fault slip and fault zone plasticity. It enables transition from periodic events to fully irregular sequences of earthquakes. The impact of plasticity on earthquake source characteristics goes beyond its limited contribution to the overall energy budget, emphasized in earlier studies, to underscore its crucial role on the redistribution of stresses that self-limits earthquake growth and leads to clustering of seismicity. This work highlights the need for characterizing the fault zone mechanical response beyond their elastic properties to better inform seismic hazard models.

Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water

<ul> <li> Description</li> </ul> The 1520 water clusters were extracted from Ref. 1. The respective energies and atomic forces were recomputed at the revPBE-D3/def2-TZVP [2-6], B3LYP-D3/def2-TZVP [4-6, 7, 8], and  BLYP-D3/def2-TZVP [4-6, 7, 9] level. <ul> <li> Format</li> </ul> The data is stored in python compressed array format (.npz) with the atomization energy in kcal/mol and atomic forces in kcal/mol/Ang. The data set contains five np.ndarray <pre><code>import numpy as np data = np.load('revpbe.npz') data['R'] # Cartesian coordinates of nuclei in Ang. data['E'] # Total energy in kcal/mol data['F'] # Atomic forces in kcal/mol/Ang. data['N'] # Number of atoms in each structure data['Z'] # Nuclear charges</code></pre> References <ul> </ul> [1] Molpeceres G., Zaverkin V., and Kästner J., “Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – I. adsorption and desorption,” Mon. Not. R. Astron. Soc. 499, 1373 (2020). [2] P. E. Blöchl, “Projector augmented-wave method,” Phys. Rev. B 50, 17953 (1994). [3] Y. Zhang and W. Yang, “Comment on “generalized gradient approximation made simple”,” Phys. Rev. Lett. 80, 890 (1998). [4] S. Grimme, J. Antony, S. Ehrlich, and H. Krieg, “A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu,” J. Chem. Phys. 132, 154104 (2010). [5] F. Weigend and R. Ahlrichs, “Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy,” Phys. Chem. Chem. Phys. 7, 3297 (2005). [6] F. Weigend, “Accurate Coulomb-fitting basis sets for H to Rn,” Phys. Chem. Chem. Phys. 8, 1057 (2006). [7] A. D. Becke, “Density-functional thermochemistry. iii. the role of exact exchange,” J. Chem. Phys. 98, 5648 (1993). [8] P. J. Stephens, F. J. Devlin, C. F. Chabalowski, and M. J. Frisch, “Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields,” J. Phys. Chem. 98, 11623 (1994). [9] C. Lee, W. Yang, and R. G. Parr, “Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density,” Phys. Rev. B 37, 785 (1988).

Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: 35 Binding Site Data

This is the original data for the manuscript "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse. This repository contains movies of DNA origami with 35 binding sites.

Model-based analysis of tuberculosis genotype clusters in the United States reveals high degree of heterogeneity in transmission, and state-level differences across California, Florida, New York, and Texas.

Data and codes for the publication

Data files for "Hybrid quantum-classical approach for coupled-cluster Green's function theory"

Source code and data files for the manuscript "Hybrid quantum-classical approach for coupled-cluster Green's function theory." Reference: Quantum 6, 675 (2022); https://doi.org/10.22331/q-2022-03-30-675. Title: Hybrid quantum-classical approach for coupled-cluster Green's function theory Authors: Trevor Keen, Bo Peng, Karol Kowalski, Pavel Lougovski, and Steven Johnston. Abstract: The three key elements of a quantum simulation are state preparation, time evolution, and measurement. While the complexity scaling of dynamics and measurements are well known, many state preparation methods are strongly system-dependent and require prior knowledge of the system’s eigenvalue spectrum. Here, we report on a quantum-classical implementation of the coupled-cluster Green’s function (CCGF) method, which replaces explicit ground state preparation with the task of applying unitary operators to a simple product state. While our approach is broadly applicable to a wide range of models, we demonstrate it here for the Anderson impurity model (AIM). The method requires a number of T gates that grow as $O(N^5)$ per time step to calculate the impurity Green’s function in the time domain, where N is the total number of energy levels in the AIM. For comparison, a classical CCGF calculation of the same order would require computational resources that grow as $O(N^6)$ per time step.

A Clustering Approach to Improve IntraVoxel Incoherent Motion Maps from DW-MRI using Conditional Auto-Regressive Bayesian Model

Simulated data generated and used in the paper "A Clustering Approach to Improve IntraVoxel Incoherent Motion Maps from DW-MRI using Conditional Auto-Regressive Bayesian Model" are here available. Results generated from both simulated and clinical datasets are also available on the excel tables.

opencc-zeroFeb 2022View details →

R code and example data for using genogeographic clustering approach

While in recent years there have been considerable advances in discerning spatial genetic patterns within species, the task of identifying common patterns across species is still challenging. Approaches using new data from co-sampled species permit rigorous statistical analysis but are often limited to a small number of species; meta-analyses of published data can encompass a much broader range of species, but are usually restricted by uneven data properties. There is a need for new approaches that bring greater statistical rigour to meta-analyses, and are also able to discern more than a single spatial pattern among species. We propose a new approach for comparative multi-species meta-analyses of published population genetic data that addresses many existing limitations. This analysis takes a three-stage approach: (i) use common genetic metrics to measure location-specific diversity across the sampled range of each species, (ii) use an innovative graphing technique to describe spatial patterns within each species, and (iii) quantitatively cluster species by their similarity in pattern. We apply this technique to 21 species of intertidal invertebrate from the New Zealand coastline, to resolve common spatial patterns from disparate profiles of genetic diversity. The genogeographic curves are shown to successfully capture the known spatial patterns within each intertidal species, and readily permit statistical comparison of those patterns, regardless of sampling and marker inconsistencies. The species clustering technique is shown to discern groups of species that clearly share spatial patterns within groups but differ significantly among groups. The species groups defined were not identifiable a priori from their taxonomy or life history, but their spatial genetic patterns appear biologically relevant. Genogeographic species clustering provides a novel approach to discerning multiple common spatial patterns of diversity among a large number of species. It will permit more rigorous comparative studies from diverse published data, and can be easily extended to a wide variety of alternative measures of genetic diversity or divergence. We see the approach best used as an exploratory method, to uncover the patterns often hidden in multi-species communities, likely to be followed by more targeted model-testing analyses.

opencc-by-4.0Mar 2022View details →

Data Transformation for Clustering Utilization for Feature Detection in MS

Dataset and roof-of-concept method used for proceeding titled "Data Transformation for Clustering Utilization for Feature Detection in MS" of IWBBIO 22 conference.

opencc-by-4.0Mar 2022View details →

The effect of local universe constraints on halo abundance and clustering

These archives contain the data products described in "The effect of local universe constraints on halo adundance and clustering" by M. L. Hutt, H. Desmond, J. Devriendt and A. Slyz (2022). See the README for further details.

opencc-zeroApr 2022View details →

Assessing changes in clusters of wildlife road mortalities after the construction wildlife mitigation structures

Collisions with vehicles can be a major threat to wildlife populations, so wildlife mitigation structures, including exclusionary fencing and wildlife crossings, are often constructed. To assess mitigation structure effectiveness, it is useful to compare wildlife road mortalities (WRMs) before, during, and after mitigation structure construction; however, differences in survey methodologies may make comparisons of counts impractical. Location-based cluster analyses provide a means to assess how WRM spatial patterns have changed over time. We collected WRM data between 2015 and 2019 on State Highway 100 in Texas, USA. Five wildlife crossings and exclusionary fencing, were installed in this area between Sep 2016 and May 2018 for the endangered ocelot (Leopardus pardalis) and other similarly sized mammals. Roads intersecting State Highway 100 were mitigated by gates, wildlife guards, and wing walls. However, these structures may have provided wildlife access to the highway. We combined local hot spot analysis and time series analysis to assess how WRM cluster intensity changed after mitigation structure construction at fine spatial and temporal scales and generalized linear regression to assess how gaps in fencing and landcover were related to WRM cluster intensity in the before, during, and after construction periods. Overall, WRMs/survey day decreased after mitigation structure construction and most hot spots occurred where there were more fence gaps, and, while cluster intensity increased in a few locations, these were not at fence gaps. Cluster intensity of WRMs increased when nearer to fence gaps in naturally vegetated areas, especially forested areas, and decreased nearer to fence gaps in areas with less natural vegetation. We recommend that if fence gaps are necessary in forested areas, less permeable mitigation structures, such as gates should be used. Local hot spot analysis, coupled with time series and regression techniques, can effectively assess how WRM clustering changes over time.

opencc-by-4.0May 2022View details →

Dataset for the Manuscript: Structural and mechanistic insights into the cleavage of clustered O-glycan patches-containing glycoproteins by mucinases of the human gut (in revision)

This dataset provides the classical and QM/MM MD simulation trajectory data to the manuscript: Structural and mechanistic insights into the cleavage of clustered O-glycan patches-containing glycoproteins by mucinases of the human gut The data set contains classical MD simulations of AM0627 with three substrate peptides P1, P2, P9, and BT4244 with glycopeptides, as well as QM/MM metadynamics simulations for our manuscript. PDB files for Figures 4,5 and Figure S5-8,10 are also included.

opencc-by-4.0Jul 2022View details →

Co-added optical spectra of globular clusters around M87 in the Virgo Cluster

This dataset provides the co-added optical spectra of globular clusters in the Virgo core region. The spectra were used in the stellar population analysis of the paper "The Next Generation Virgo Cluster Survey. XXXIII. Stellar Population Gradients in the Virgo Cluster Core Globular Cluster System" by Ko et al. (2022). The detailed description can be found in the README file.

opencc-by-4.0Jul 2022View details →

Data from the paper "Learning to clusterize urban areas: two competitive approaches and an empirical validation"

Data for urban clustering used in the paper "Learning to clusterize urban areas: two competitive approaches and an empirical validation". We release two datasets for urban clustering based on data acquired in Santiago de Chile. The first dataset is computed at the level of urban blocks. The second dataset is computed at the level of individuals using a uniform sample of Santiago inhabitants. Both datasets comprises features based on social characteristics (e.g., SES), land use, and aesthetic visual perception of the city. The features of each data unit (blocks or individuals) are provided using row packing (each row is a data unit) in CSV files. We release PCA (Principal Components Analysis) features for both datasets.