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.
305
datasets available to search
ShareScore release 0.9.0
Dataset results
305 results for “characterization model”
Project files provided as supporting information to the manuscript "Molecular dynamics characterization of the free and encapsidated RNA2 of CCMV with the oxRNA model"
<pre>README file to the project files provided as supporting information to the manuscript "Molecular dynamics characterization of the free and encapsidated RNA2 of CCMV with the oxRNA model" <br>October 8, 2024<br> Authors: Giovanni Mattiotti, Manuel Micheloni, Lorenzo Petrolli, Lorenzo Rovigatti, Luca Tubiana, Samuela Pasquali, Raffaello Potestio</pre> <p>==================================</p> <p>Trajectories and obsvervables relative to the scientific paper entitled:<br>"Molecular dynamics characterization of the free and encapsidated RNA2 of CCMV with oxRNA"</p> <p>Authors:<br>Giovanni Mattiotti, Manuel Micheloni, Lorenzo Petrolli, Luca Tubiana, Samuela Pasquali, Raffaello Potestio</p> <p>Brief description of the content of this folder:</p> <p><br>|_ oxDNA-CCMV_force.zip: the modified version of the oxDNA software we used to make the simulations with the CCMV-like spherical potential described in the paper<br>|<br>|_ CCMV_RNA2_oxRNA_simulations<br> |_ FOLDING (files of the "freely-folding" simulations)<br> | |_ FIRST_LAST_FRAMES (frames generated by oxDNA software)<br> | | |_ 0.15M (first and last frames of the runs at 0.15M)<br> | | | |_ REP1 (files relative to REP1 / REPI)<br> | | | | |_ first.dat<br> | | | | |_ last.dat <br> | | | |<br> | | | |_ REP2 (files relative to REP2 / REPII)<br> | | | | |_ first.dat<br> | | | | |_ last.dat <br> | | | |<br> | | | |_ REP3 (files relative to REP3 / REPIII)<br> | | | |_ first.dat<br> | | | |_ last.dat <br> | | |<br> | | |_ 0.50M (first and last frames of the runs at 0.50M)<br> | | | |_ [same structure as for 0.15M]<br> | | |<br> | | |_ 0.50M (first and last frames of the runs at 0.50M, T293K)<br> | | | |_ [same structure as for 0.15M]<br> | | |<br> | | |_ 0.50M (first and last frames of the runs at 0.50M, T293K, harmonic constraint to the ends)<br> | | |_ [same structure as for 0.15M]<br> | |<br> | |<br> | |_ HB_FILES (base pairs files generated by oxDNA software)<br> | | |_ hb_015M_I.dat (annotation of base pairs in oxDNA format, corresponding to REP1 at 0.15M salt concentration)<br> | | |_ hb_015M_II.dat (annotation of base pairs in oxDNA format, corresponding to REP2 at 0.15M salt concentration)<br> | | |_ hb_015M_III.dat (annotation of base pairs in oxDNA format, corresponding to REP3 at 0.15M salt concentration)<br> | | |_ hb_050M_I.dat (annotation of base pairs in oxDNA format, corresponding to REP1 at 0.5M salt concentration)<br> | | |_ hb_050M_II.dat (annotation of base pairs in oxDNA format, corresponding to REP2 at 0.5M salt concentration)<br> | | |_ hb_050M_III.dat (annotation of base pairs in oxDNA format, corresponding to REP3 at 0.5M salt concentration)<br> | | |_ hb_050M_293K_I.dat (annotation of base pairs in oxDNA format, corresponding to REP1 at 0.5M salt concentration, T 293K)<br> | | |_ hb_050M_293K_II.dat (annotation of base pairs in oxDNA format, corresponding to REP2 at 0.5M salt concentration, T 293K)<br> | | |_ hb_050M_293K_III.dat (annotation of base pairs in oxDNA format, corresponding to REP3 at 0.5M salt concentration, T 293K)<br> | | |_ hb_050M_293K_harm_I.dat (annotation of base pairs in oxDNA format, corresponding to REP1 at 0.5M salt concentration, T 293K, harmonic constraint to the ends)<br> | | |_ hb_050M_293K_harm_II.dat (annotation of base pairs in oxDNA format, corresponding to REP2 at 0.5M salt concentration, T 293K, harmonic constraint to the ends)<br> | | |_ hb_050M_293K_harm_III.dat (annotation of base pairs in oxDNA format, corresponding to REP3 at 0.5M salt concentration, T 293K, harmonic constraint to the ends)<br> | |<br> | |_ example_input.ox (example of oxDNA input file used to launch simulations and dump observables and trajectories)<br> | |<br> | |_ example_input_harm.ox (example of oxDNA input file used to launch simulations with harmonic constraint to the ends and dump observables and trajectories)<br> | |<br> | |_ harmonic_ends.dat (constraint force file used to run simulations with harmonic constraint to the ends)<br> |<br> |_ PACKED<br> |_ SIM_VTHEO (files of the simulations with the analytic-based external potential, as described in the paper...)<br> | |_ 0.15M (... with a salt concentration of 0.15M)<br> | | |_ REP1 (Replica with id 1, or I)<br> | | | |_ MD_scripts (files required by oxDNA software to launch the simulation for this replica)<br> | | | | |_ input_0.15M_REP1.ox (input file of oxDNA software to launch the simulation)<br> | | | | |_ force_0.15M_REP1.dat (additional file required for the application of an external force in the simulation, containing the relative parameters)<br> | | | |<br> | | | |_ input_structure (structure files in oxDNA format, one of which is used as starting configuration for the specific replica)<br> | | | | |_ PackingRNA2_0.15M_REP1.dat (last structure of simulation with time-dependent external packaging force)<br> | | | | |_ PackingRNA2_0.15M_REP2.dat (last structure of simulation with time-dependent external packaging force, not used - see name corresponding to this replica)<br> | | | | |_ PackingRNA2_0.15M_REP3.dat (last structure of simulation with time-dependent external packaging force, not used - see name corresponding to this replica)<br> | | | | |_ PackingRNA2_0.5M_REP1.dat (last structure of simulation with time-dependent external packaging force, not used - see name corresponding to this replica)<br> | | | | |_ PackingRNA2_0.5M_REP2.dat (last structure of simulation with time-dependent external packaging force, not used - see name corresponding to this replica)<br> | | | | |_ PackingRNA2_0.5M_REP3.dat (last structure of simulation with time-dependent external packaging force, not used - see name corresponding to this replica)<br> | | | |<br> | | | |_ data (output data generated by the simulation)<br> | | | |_ 1.E (files of the observables dumped during simulation)<br> | | | | |_ E_RNA2_0.15M_REP1.dat (energies of the system per each time frame: time frame, potential energy U, kinetic energy, total energy)<br> | | | | |_ P_RNA2_0.15M_REP1.dat (internal pressure of the system: time frame, total pressure, stress tensor components xx, yy, zz, xy, xz, yz)<br> | | | | |_ F_RNA2_0.15M_REP1.dat (components of the external force acting on each nucleotide, ordered according to the topology, per each frame)<br> | | | | |_ HB_RNA2_0.15M_REP1.dat (base pairs annotated, per each time frame dumped)<br> | | | |<br> | | | |_ 3.restart (restart/last configuration file dumped during simulation)<br> | | | |_ last_conf_RNA2_0.15M_Yukawa_REP1.dat (last frame dumped in the simulation, in oxDNA format)<br> | | |<br> | | |<br> | | |_ REP2 (Replica with id 2, or II)<br> | | | |_ [same content as REP1, but relative to replica 2 or II]<br> | | |<br> | | |_ REP3 (Replica with id 3, or III)<br> | | |_ [same content as REP1, but relative to replica 3 or III]<br> | |<br> | |_ 0.5M (... with a salt concentration of 0.5M)<br> | | |_ [same content as 0.15M, but for simulations done at 0.5M of salt concentration]<br> | |<br> | |_ 0.5M_293K_harm (... with a salt concentration of 0.5M, Temperature 293K and harmonic constraint to the ends)<br> | |_ [same content as 0.15M, but for simulations done at 0.5M of salt concentration]<br> |<br> |_ SIM_VCCMV (files of the simulations with the structure-based external potential, as described in the paper)<br> |_ [same content as SIM_VTHEO, but for simulations done with the structure-based external potential]</p> <p><br>For any additional file or data not present here, that was used to produce results in the paper, please ask to: giovanni.mattiotti@inserm.fr, manuel.micheloni@unitn.it, lorenzo.petrolli@unitn.it</p>
Characterizing the consensus residue specificity and surface of Bcl-2 binding to BH3 ligands using the knob-socket model
<p><span>Cancer cells bypass cell death by changing the expression of the BCL-2 family of proteins, which are apoptotic pathway regulators. Upregulation of pro-survival BCL-2 proteins or downregulation of cell death effectors BAX and BAK interferes with the initiation of the intrinsic apoptotic pathway. In normal cells, apoptosis can occur through pro-apoptotic BH3-only proteins interacting and inhibiting pro-survival BCL-2 proteins. When cancer cells over-express pro-survival BCL-2 proteins, a potential remedy is the sequestration of these pro-survival proteins through a class of anti-cancer drugs called BH3 mimetics that bind in the hydrophobic groove of pro-survival BCL-2 proteins. To improve the design of these BH3 mimetics, the packing interface between BH3 domain ligands and pro-survival BCL-2 proteins was analyzed using the Knob-Socket model to identify the amino acid residues responsible for interaction affinity and specificity. A Knob-Socket analysis organizes all the residues in a binding interface into simple 4 residue units: 3-residue sockets defining surfaces on a protein that pack a 4th residue knob from the other protein. In this way, the position and composition of the knobs packing into sockets across the BH3/BCL-2 interface can be classified. A Knob-Socket analysis of 19 BCL-2 protein and BH3 helix co-crystals reveal multiple conserved binding patterns across protein paralogs. Conserved knob residues such as a Gly, Leu, Ala and Glu most likely define binding specificity in the BH3/BCL-2 interface, whereas other residues such as Asp, Asn, and Val are important for forming surface sockets that bind these knobs. These findings can be used to inform the design of BH3 mimetics that are specific to pro-survival BCL-2 proteins for cancer therapeutics.</span></p>
Characterization of Continuous Experimentation in Software Engineering: Expressions, Models and Strategies
<p>Systematic Mapping Study's Dataset</p>
Data set for "Spatially explicit ecological modeling improves empirical characterization of dispersal"
<p>Data set used and created in the simulations, analysis and figures of the associated paper.</p>
Fig. 4. The stability parameters for modelled D10 in In silico characterization of the impact of mutation (LEU112PRO) on the structure and function of carotenoid cleavage dioxygenase 8 in Oryza sativa
Fig. 4. The stability parameters for modelled D10 protein: wild-type (Blue), mutant (Green), Complex I (wild-type & auxin complex) (orange), Complex II (wild-type & cytokinin complex) (purple), Complex III (mutant-auxin complex) (red) and (D) Complex IV (mutant-cytokinin complex) (black) throughout 100 ns simulation: (A) RMSD, (B) RMSF, (C) PCA of C-α movement, and (D) Radius of gyration. The trajectory projected to the two-dimensional space. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Peritoneal Surface Malignancies - Characterization, Models and Treatment Strategies
ClinicalTrials.gov study NCT02073500. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
A Study to Characterize Multidimensional Model to Predict the Course of Crohn's Disease (CD)
ClinicalTrials.gov study NCT03668249. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: Coalescent models characterize sources and demographic history of recent round goby colonization of Great Lakes and inland waters
Open the record for dataset details and reuse information.
Characterizing the consensus residue specificity and surface of Bcl-2 binding to BH3 ligands using the knob-socket model
Open the record for dataset details and reuse information.
Bayesian stable isotope mixing models effectively characterize the diet of an Arctic raptor
Open the record for dataset details and reuse information.
Data from: Characterizing neutral and adaptive genomic differentiation in a changing climate: the most northerly freshwater fish as a model
Open the record for dataset details and reuse information.
Data from: Development of an adrenocortical cancer humanized mouse model to characterize anti-PD1 effects on tumor microenvironment
Open the record for dataset details and reuse information.
Data for: Synthetic red supergiant explosion model grid for systematic characterization of Type II supernovae
Open the record for dataset details and reuse information.
Data from: Genome-wide single nucleotide polymorphism (SNP) identification and characterization in a non-model organism, the African buffalo (Syncerus caffer), using next generation sequencing
Open the record for dataset details and reuse information.
Data from: A spatially explicit hierarchical model to characterize population viability
Open the record for dataset details and reuse information.
Data from: A segmentation algorithm for characterizing Rise and Fall segments in seasonal cycles: an application to XCO2 to estimate benchmarks and assess model bias
Open the record for dataset details and reuse information.
Raw in vitro screening data and R scripts for: A Bayesian method for population-wide cardiotoxicity hazard and risk characterization using an in vitro human model
<p>Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes are an established model for testing potential chemical hazards. Inter-individual variability in toxicodynamic sensitivity has also been demonstrated <i>in vitro</i>; however, quantitative characterization of the population-wide variability has not been fully explored. We sought to develop a method to address this gap by combining a population-based iPSC-derived cardiomyocyte model with Bayesian concentration-response modeling. A total of 136 compounds, including 44 pharmaceuticals and 82 environmental chemicals, were tested in iPSC-derived cardiomyocytes from 43 non-diseased humans. Hierarchical Bayesian population concentration-response modeling was conducted for five phenotypes reflecting cardiomyocyte function or viability. Toxicodynamic variability was quantified through the derivation of chemical- and phenotype-specific variability factors (TDVF). Toxicokinetic modeling was used for probabilistic <i>in vitro</i>-to-<i>in vivo </i>extrapolation in order to derive population-wide margins of safety (MOS) for pharmaceuticals and margins of exposure (MOE) for environmental chemicals. Pharmaceuticals were found to be active across all phenotypes. Over half of tested environmental chemicals showed activity in at least one phenotype, most commonly positive chronotropy. TDVF estimates for the functional phenotypes were greater than those for cell viability, usually exceeding the generally-assumed default of ~3. Population variability-based MOS for pharmaceuticals were correctly predicted to be relatively narrow, between 10-100; however, MOE for environmental chemicals, based on population exposure estimates, generally exceeded 1000, suggesting they pose little risk at general population exposures even to sensitive sub populations. This study represents a first of its kind human <i>in vitro</i> model that can be used to characterize toxicodynamic population variability in cardiotoxic risk.</p>
Data from: Antagonistic versus non-antagonistic models of balancing selection: characterizing the relative timescales and hitchhiking effects of partial selective sweeps
Antagonistically selected alleles-–those with opposing fitness effects between sexes, environments, or fitness components-–represent an important component of additive genetic variance in fitness-related traits, with stably balanced polymorphisms often hypothesized to contribute to observed quantitative genetic variation. Balancing selection hypotheses imply that intermediate-frequency alleles disproportionately contribute to genetic variance of life-history traits and fitness. Such alleles may also associate with population genetic footprints of recent selection, including reduced genetic diversity and inflated linkage disequilibrium at linked, neutral sites. Here, we compare the evolutionary dynamics of different balancing selection models, and characterize the evolutionary timescale and hitchhiking effects of partial selective sweeps generated under antagonistic versus nonantagonistic (e.g., overdominant and frequency-dependent selection) processes. We show that the evolutionary timescales of partial sweeps tend to be much longer, and hitchhiking effects are drastically weaker, under scenarios of antagonistic selection. These results predict an interesting mismatch between molecular population genetic and quantitative genetic patterns of variation. Balanced, antagonistically selected alleles are expected to contribute more to additive genetic variance for fitness than alleles maintained by classic, nonantagonistic mechanisms. Nevertheless, classical mechanisms of balancing selection are much more likely to generate strong population genetic signatures of recent balancing selection.
Data from: Characterization of advanced glycation end products and their receptor (RAGE) in an animal model of myocardial infarction
Circulating advanced glycation end products (AGE) and their receptor, RAGE, are increased after a myocardial infarction (MI) episode and seem to be associated with worse prognosis in patients. Despite the increasing importance of these molecules in the course of cardiac diseases, they have never been characterized in an animal model of MI. Thus, the aim of this study was to characterize AGE formation and RAGE expression in plasma and cardiac tissue during cardiac remodeling after MI in rats. Adult male Wistar rats were randomized to receive sham surgery (n = 15) or MI induction (n = 14) by left anterior descending coronary artery ligation. The MI group was stratified into two subgroups based on postoperative left ventricular ejection fraction: low (MIlowEF) and intermediate (MIintermEF). Echocardiography findings and plasma levels of AGEs, protein carbonyl, and free amines were assessed at baseline and 2, 30, and 120 days postoperatively. At the end of follow-up, the heart was harvested for AGE and RAGE evaluation. No differences were observed in AGE formation in plasma, except for a decrease in absorbance in MIlowEF at the end of follow-up. A decrease in yellowish-brown AGEs in heart homogenate was found, which was confirmed by immunodetection of N-ε-carboxymethyl-lysine. No differences could be seen in plasma RAGE levels among the groups, despite an increase in MI groups over the time. However, MI animals presented an increase of 50% in heart RAGE at the end of the follow-up. Despite the inflammatory and oxidative profile of experimental MI in rats, there was no increase in plasma AGE or RAGE levels. However, AGE levels in cardiac tissue declined. Thus, we suggest that the rat MI model should be employed with caution when studying the AGE-RAGE signaling axis or anti-AGE drugs for not reflecting previous clinical findings.
Data from: Characterization of leukemia-inducing genes using a proto-oncogene/homeobox gene retroviral human cDNA library in a mouse in vivo model
The purpose of this research is to develop a method to screen a large number of potential driver mutations of acute myeloid leukemia (AML) using a retroviral cDNA library and murine bone marrow transduction-transplantation system. As a proof-of-concept, murine bone marrow (BM) cells were transduced with a retroviral cDNA library encoding well-characterized oncogenes and homeobox genes, and the virus-transduced cells were transplanted into lethally irradiated mice. The proto-oncogenes responsible for leukemia initiation were identified by PCR amplification of cDNA inserts from genomic DNA isolated from leukemic cells. In an initial screen of ten leukemic mice, the MYC proto-oncogene was detected in all the leukemic mice. Of ten leukemic mice, 3 (30%) had MYC as the only transgene, and seven mice (70%) had additional proto-oncogene inserts. We repeated the same experiment after removing MYC-related genes from the library to characterize additional leukemia-inducing gene combinations. Our second screen using the MYC-deleted proto-oncogene library confirmed MEIS1and the HOX family as cooperating oncogenes in leukemia pathogenesis. The model system we introduced in this study will be valuable in functionally screening novel combinations of genes for leukemogenic potential in vivo, and the system will help in the discovery of new targets for leukemia therapy.
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.