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97 results for “Process Improvement”
The Role of Emotional Processing in Improving the Quality of Life of Breast Cancer Patients
ClinicalTrials.gov study NCT03377816. IPD Sharing: NO. Countries: 1. Publications: 2.
Trial to Improve Multisensory Neural Processing, Language & Motor Outcomes in Preterm Infants
ClinicalTrials.gov study NCT03232931. IPD Sharing: NO. Countries: 1. Publications: 2.
A Quality Improvement Process to Support Delivery of Cardiovascular Care in Community Mental Health Organizations
ClinicalTrials.gov study NCT04696653. IPD Sharing: NO. Countries: 1. Publications: 2.
Protocol to Improve the Representation of the Reproductive Process in Spanish Fiction.
ClinicalTrials.gov study NCT06842004. IPD Sharing: NO. Countries: 1. Publications: 2.
Improved dual-permeability model for characterizing the mass transfer process inside matrix blocks
Open the record for dataset details and reuse information.
Including a spatial predictive process in band recovery models improves inference for Lincoln estimates of animal abundance
Open the record for dataset details and reuse information.
Data from: Measured perceptual nonlinearities show how ON-OFF asymmetric processing improves motion estimation in natural scenes
Animals detect motion using a variety of visual cues that reflect regularities in the natural world. Experiments in animals across phyla have shown that motion percepts incorporate both pairwise and triplet spatiotemporal correlations that could theoretically benefit motion computation. However, it remains unclear how visual systems assemble these cues to build accurate motion estimates. Here we use comprehensive measurements of fruit fly motion perception to show how flies combine local pairwise and triplet correlations to reduce variability in motion estimates across natural scenes. By generating synthetic images with statistics controlled by maximum entropy distributions, we showed that the observed improvement occurs only when light-dark asymmetries mimic natural ones. Thus, fly behavior suggests that asymmetric ON-OFF processing is tuned to the particular statistics of natural scenes. Since all animals encounter the world's light-dark asymmetries, many visual systems are likely to use asymmetric ON-OFF processing to improve motion estimation.
Data from: Early diving behaviour in juvenile penguins: improvement or selection processes
The early life stage of long-lived species is critical to the viability of population, but is poorly understood. Longitudinal studies are needed to test whether juveniles are less efficient foragers than adults as has been hypothesized. We measured changes in the diving behaviour of 17 one-year-old king penguins Aptenodytes patagonicus at Crozet Islands (subantartic archipelago) during their first months at sea, using miniaturized tags that transmitted diving activity in real time. We also equipped five non-breeder adults with the same tags for comparison. The data on foraging performance revealed two groups of juveniles. The first group made shallower and shorter dives that may be indicative of early mortality while the second group progressively increased their diving depths and durations, and survived the first months at sea. This surviving group of juveniles required the same recovery durations as adults, but typically performed shallower and shorter dives. There is thereby a relationship between improved diving behaviour and survival in young penguins. This long period of improving diving performance in the juvenile life stage is potentially a critical period for the survival of deep avian divers and may have implications for their ability to adapt to environmental change.
Data from: Accounting for observation processes across multiple levels of uncertainty improves inference of species distributions and guides adaptive sampling of environmental DNA
Understanding factors that influence observation processes is critical for accurate assessment of underlying ecological processes. When indirect methods of detection, such as environmental DNA, are used to determine species presence, additional levels of uncertainty from observation processes need to be accounted for. We conducted a field trial to evaluate observation processes of a terrestrial invasive species (wild pigs- Sus scrofa) from DNA in water bodies. We used a multi-scale occupancy analysis to estimate different levels of observation processes (detection, p): the probability DNA is available per sample (θ), the probability of capturing DNA per extraction (γ), and the probability of amplification per qPCR run (δ). We selected four sites for each of three water body types and collected 10 samples per water body during two months (September and October 2016) in central Texas. Our methodology can be used to guide sampling adaptively to minimize costs while improving inference of species distributions. Using a removal sampling approach was more efficient than pooling samples, and was unbiased. Availability of DNA varied by month, was considerably higher when water pH was near neutral, and was higher in ephemeral streams relative to wildlife guzzlers and ponds. To achieve a cumulative detection probability greater than 90% (including availability, capture, and amplification), future studies should collect 20 water samples per site, conduct at least 2 extractions per sample, and conduct 5 qPCR replicates per extraction. Accounting for multiple levels of uncertainty of observation processes improved estimation of the ecological processes and provided guidance for future sampling designs.
Dataset: The Role and Lifetime of Dissociative Heterogeneous Processes in Improving Simulated Ozone on Mars
<p>VERSION 1:</p> <p>Dataset for the manuscript 'The Role and Lifetime of Dissociative Heterogeneous Processes in Improving Simulated Ozone on Mars'. The four netcdf files contain the full outputs to the MGCM for sols 7351-7381 (approximately Ls = 0 - 14, MY 35) with all chemical tracers:</p> <ul> <li>fullry_0_diagfi : 0% release dataset</li> <li>fullry_50_diagfi : 50% release dataset</li> <li>fullry_100_diagfi : 100% release dataset</li> <li>fullry_control_diagfi : control (gas only) dataset</li> </ul> <p>The MGCM values which match locations of observed ozone and water vapour TCA, as well as the observations themselves are found in df_nadir_gcm_vapo3_hyb_spin_20230829.pkl. </p> <p> </p> <p>VERSION 2:</p> <p>Also uploaded are the data for sols 7501-7531 (Ls = 68 - 82, MY 35):</p> <ul> <li>d7501-7531_control_diagfi : control (gas only) dataset</li> <li>d7501-7531_50_diagfi : 50% release dataset</li> </ul> <p> </p> <p>VERSION 3:</p> <p>Uploaded are data for sols 7621-7651 (Ls = 124 - 138, MY 35)</p> <ul> <li>d7621-7651_control_diagfi : control (gas only) dataset</li> <li>d7621-7651_50_diagfi : 50% release dataset</li> </ul> <p> </p>
Use of Gamification as Implementation for Software Process Improvement
<p>Studies carried out in the context of software process improvement (SPI) report problems and difficulties that organizations face to implement process models and standards, mainly related to the inability to overcome some critical factors. Thus, properly conducting SPI can facilitate the development of more efficient processes to overcome such difficulties, using gamification elements. Therefore, this article aims to identify problems in the implementation phase of an SPI effort and use gamification as a tool to mitigate barriers during SPI implementation in a company that opted for an SPI program such as CMMI Dev 2.0 and /or MR-MPS-SW and create the applicability guide using gamification in SPI that can be used by software organizations. The search string returned 499 (Four hundred and Ninety-Nine) works, of which 7 (Seven) were selected according to the Systematic Review of Literature criteria.</p>
Incorporating the image formation process into deep learning improves network performance
<p>These are representative source data for the main figures (Figs, 1d, 2a, 2d, 3c, 4b, 5a) in paper "Incorporating the image formation process into deep learning improves network performance".</p>
Processed data - DegNorm: Normalization of generalized transcript degradation improves accuracy in RNA-seq analysis
<p>Processed data from DegNorm:</p> <ul> <li>"_raw.txt": raw read counts matrix;</li> <li>"_DI.txt": Degradation index score matrix;</li> <li>"_DegNorm.txt": normalized read counts matrix from DegNorm output;</li> <li>"_coverage.Rdata": list of coverage matrix for the sample;</li> <li>"_countsTIN.txt": TIN normalized counts.</li> </ul> <p> </p>
Improvements for the Planning Process in the Scrum Method - Case studies datasets
<p>Data for the article Improvements for the Planning Process in the Scrum Method. These are the data inputs for the case studies located in the article.</p>
Experiment results for paper about discovery of improvement opportunities in knock-out checks of Business Processes
<p>This dataset contains the results (in .csv format) of experiments performed using the code from the paper "Minimizing Overprocessing Waste via Predictive Activity Ordering" (https://doi.org/10.1007/978-3-319-39696-5_12).</p> <p>Each file corresponds to an execution of the entire training & evaluation pipeline, each time with different random subsets of the full dataset. They contain the number of knockout checks performed with 3 different approaches (variable rejection probabilities, constant rejection probabilities, and random ordering).</p>
MD simulation trajectories associated to the publication: Multi-eGO: model improvements towards the study of complex self-assembly processes
<p>The three tgz compressed files include the simulations data and resulting trajectories for the three systems discussed in the work. In particular: </p><ul><li>ab42.tgz includes a random_coil simulation, the multi-eGO simulation of the monomer performed in triplicate and the simulations performed with the original multi-eGO model.</li><li>ttr.tgz includes the randomcoil simulations for both the intramolecular as well as the intermolecular interactions at the three different concentrations, the simulation of the monomer performed in triplicate, and the aggregation kinetics performed in triplicate at the three reported concentrations</li><li>protein_g.tgz includes the reference GB1 simulation, a randomcoil simulation and the 200 multi-eGO folding simulations.</li></ul>
Multiple Joint, Structural Barbell Resistance Exercise Improves Information Processing Speed
ClinicalTrials.gov study NCT04534374. IPD Sharing: YES. Countries: 1. Publications: 1.
Board Games at Kindergarten (5 Years Old) to Improve Cognitive and Emotional Processes
ClinicalTrials.gov study NCT06304454. IPD Sharing: NO. Countries: 1. Publications: 2.
Speed of Processing Training to Improve Cognition in Traumatic Brain Injury and Mild Cognitive Impairment
ClinicalTrials.gov study NCT02020564. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Improving Participation of Autistic Children and Adolescents in the Habilitation Process
ClinicalTrials.gov study NCT06381856. IPD Sharing: NO. Countries: 1. Publications: 1.
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.