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.
130
datasets available to search
ShareScore release 0.9.0
Dataset results
130 results for “Markov”
Data from: An efficient independence sampler for updating branches in Bayesian Markov chain Monte Carlo sampling of phylogenetic trees
Open the record for dataset details and reuse information.
Data from: MatlabHTK: a simple interface for bioacoustic analyses using Hidden Markov models
Open the record for dataset details and reuse information.
Data from: Using hidden Markov models to improve quantifying physical activity in accelerometer data – a simulation study
Open the record for dataset details and reuse information.
Generalized hidden Markov models for phylogenetic comparative datasets
Open the record for dataset details and reuse information.
MESA files for paper "Progenitor properties of type II supernovae: fitting to hydrodynamical models using Markov chain Monte Carlo methods"
<p>Inlists to reproduce the pre-SN simulations of the paper "Progenitor properties of type II supernovae: fitting to hydrodynamical models using Markov chain Monte Carlo methods". These simulations were performed using MESA version 10398.</p>
Figure 7 from: Dedic M, Gutic S, Gicevic A, Becic E, Imamovic B, Markovic D, Ziga-Smajic N (2020) Application of membrane filters in determination of the adsorption of tetracycline hydrochloride on graphene oxide. Pharmacia 67(4): 339-345. https://doi.org/10.3897/pharmacia.67.e57242
Figure 7 Diffractogram of graphene oxide.
Figure 8 from: Dedic M, Gutic S, Gicevic A, Becic E, Imamovic B, Markovic D, Ziga-Smajic N (2020) Application of membrane filters in determination of the adsorption of tetracycline hydrochloride on graphene oxide. Pharmacia 67(4): 339-345. https://doi.org/10.3897/pharmacia.67.e57242
Figure 8 Diffractogram of thermally reduced graphene oxide.
Figure 6 from: Dedic M, Gutic S, Gicevic A, Becic E, Imamovic B, Markovic D, Ziga-Smajic N (2020) Application of membrane filters in determination of the adsorption of tetracycline hydrochloride on graphene oxide. Pharmacia 67(4): 339-345. https://doi.org/10.3897/pharmacia.67.e57242
Figure 6 Raman spectrum of thermally reduced graphene oxide.
Figure 3 from: Dedic M, Gutic S, Gicevic A, Becic E, Imamovic B, Markovic D, Ziga-Smajic N (2020) Application of membrane filters in determination of the adsorption of tetracycline hydrochloride on graphene oxide. Pharmacia 67(4): 339-345. https://doi.org/10.3897/pharmacia.67.e57242
Figure 3 FTIR spectrum of graphene oxide.
Figure 5 from: Dedic M, Gutic S, Gicevic A, Becic E, Imamovic B, Markovic D, Ziga-Smajic N (2020) Application of membrane filters in determination of the adsorption of tetracycline hydrochloride on graphene oxide. Pharmacia 67(4): 339-345. https://doi.org/10.3897/pharmacia.67.e57242
Figure 5 Raman spectrum of graphene oxide.
Figure 4 from: Dedic M, Gutic S, Gicevic A, Becic E, Imamovic B, Markovic D, Ziga-Smajic N (2020) Application of membrane filters in determination of the adsorption of tetracycline hydrochloride on graphene oxide. Pharmacia 67(4): 339-345. https://doi.org/10.3897/pharmacia.67.e57242
Figure 4 FTIR spectrogram of thermally reduced graphene oxide.
Figure 2 from: Dedic M, Gutic S, Gicevic A, Becic E, Imamovic B, Markovic D, Ziga-Smajic N (2020) Application of membrane filters in determination of the adsorption of tetracycline hydrochloride on graphene oxide. Pharmacia 67(4): 339-345. https://doi.org/10.3897/pharmacia.67.e57242
Figure 2 UV-Vis absorption spectrum of tetracycline hydrochloride.
Figure 1 from: Dedic M, Gutic S, Gicevic A, Becic E, Imamovic B, Markovic D, Ziga-Smajic N (2020) Application of membrane filters in determination of the adsorption of tetracycline hydrochloride on graphene oxide. Pharmacia 67(4): 339-345. https://doi.org/10.3897/pharmacia.67.e57242
Figure 1 Structure of graphene in the form of a hexagonal lattice (Garg et al. 2016).
Supplementary data for: "Progenitor properties of type II supernovae: fitting to hydrodynamical models using Markov chain Monte Carlo methods"
<p>This entry contains a grid of bolometric light curve and photospheric velocity models applied to stellar evolution progenitors. A full description of the models can be found in Martinez et al. 2020, A&A, 642, A143.</p>
Figure 1 from: Mudri-Stojnić S, Andrić A, Markov-Ristić Z, Đukić A, Vujić A (2021) Contribution to the knowledge of the bee fauna (Hymenoptera, Apoidea, Anthophila) in Serbia. ZooKeys 1053: 43-105. https://doi.org/10.3897/zookeys.1053.67288
Figure 1 Map of Serbia showing the locations where specimens were collected.
Figure 4 from: Tachkov K, Mitov K, Savova A (2019) Predicting the outcomes and costs for a cohort of 426 patients with Chronic Obstructive Pulmonary Disease (COPD) in Bulgaria through a Markov model. Pharmacia 66(2): 53-57. https://doi.org/10.3897/pharmacia.66.e35162
Figure 4 Tornado diagram for LYS
Figure 2 from: Tachkov K, Mitov K, Savova A (2019) Predicting the outcomes and costs for a cohort of 426 patients with Chronic Obstructive Pulmonary Disease (COPD) in Bulgaria through a Markov model. Pharmacia 66(2): 53-57. https://doi.org/10.3897/pharmacia.66.e35162
Figure 2 CEAC of all data points
Figure 1 from: Tachkov K, Mitov K, Savova A (2019) Predicting the outcomes and costs for a cohort of 426 patients with Chronic Obstructive Pulmonary Disease (COPD) in Bulgaria through a Markov model. Pharmacia 66(2): 53-57. https://doi.org/10.3897/pharmacia.66.e35162
Figure 1 ICER points and dispersion cloud of Monte-Carlo simulation
Figure 3 from: Tachkov K, Mitov K, Savova A (2019) Predicting the outcomes and costs for a cohort of 426 patients with Chronic Obstructive Pulmonary Disease (COPD) in Bulgaria through a Markov model. Pharmacia 66(2): 53-57. https://doi.org/10.3897/pharmacia.66.e35162
Figure 3 Tornado diagram for QALYs
Verification of Multi-Objective Markov Models: Replication Package
<p>Contains benchmarks, execution scripts, log files, and the Storm version exercised for the experiments given in Chapter 9 of the work <strong>Verification of Multi-Objective Markov Models</strong>.</p> <p>List of contents:</p> <ul> <li>storm-package.zip: contains the Storm version considered in the experiments including installation instructions and scripts. Tested on the <a href="https://doi.org/10.5281/zenodo.7113222">TACAS'23 artefact evaluation VM</a></li> <li>totalrew.zip: contains the benchmarks and experimental data for the evaluation of expected total reward computations (Sections 9.2)</li> <li>multi.zip: contains the benchmarks and experimental data for the evaluation of multi-objective verification (Sections 9.3 to 9.6)</li> </ul> <p> </p>
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.