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ShareScore release 0.9.0
Dataset results
54 results for “sampling efficiency”
Sequential memory improves sample and memory efficiency in Episodic Control - Dataset
<p>Dataset for the research paper titled "Sequential memory improves sample and memory efficiency in Episodic Control"</p>
Dataset Klöckner_et_al 2024 paper (Understanding users of online energy efficiency counselling: Comparison to representative samples in Norway)
Open the record for dataset details and reuse information.
FIGURE 1 in Orchid Bees (Hymenoptera: Apidae) In The Coastal Forests Of Southern Brazil: Diversity, Efficiency Of Sampling Methods And Comparison With Other Atlantic Forest Surveys
FIGURE 1: DCA analysis of the orchid bee assemblages along the Brazilian Atlantic forest (see text for site codes; the two sites from the current study, PR3 and SP3, are shown in gray).
Machine Learning-Assisted Sampling of SERS Substrates Improves Data Collection Efficiency: raw data and code
<p>Raw datasets and media accompanying the manuscript: <strong>Machine Learning-Assisted Sampling of SERS Substrates Improves Data Collection Efficiency</strong>: data, published in <em>Applied Spectroscopy </em>in 2021</p>
Supplementary material 7 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Figure S2. Baseline-corrected amplification curves (left half) and melting -curves (right half) for A & B) kick-net samples and C & D) malaise trap samples
Efficiency of Two Glucose Sampling Protocols for Maintenance of Euglycemia
ClinicalTrials.gov study NCT00993057. IPD Sharing: NO. Countries: 1. Publications: 11.
Network-Targeted Strategies for Efficient Community SARS-CoV-2 (COVID-19) Sampling
ClinicalTrials.gov study NCT04437706. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: Empowering conservation practice with efficient and economical genotyping from poor quality samples
Open the record for dataset details and reuse information.
Toxicity Dataset for "Efficient solar disinfection (SODIS) using polypropylene based transparent jerrycans: An investigation into its effectiveness, viability, and water sample toxicity"
<p>Toxicity data including abosrbance and percentage viability obtainbed from research for three TJC Verison 1</p> <p> </p>
Figure 3 in Efficiency of sampling methods for capturing soil-dwelling ants in three landscapes in southern Cameroon
Figure 3. Difference in ant community compositions between sampling methods (A) and habitat (B) using ANOSIM (999 randomisations).
MATLAB code and interfaces, quantum efficiency data for Hyper-sampling imaging
<p>Supplementary Materials for "Hyper-sampling imaging by measurement of intra-pixel quantum efficiency using steady wave field"</p>
Data from: An efficient independence sampler for updating branches in Bayesian Markov chain Monte Carlo sampling of phylogenetic trees
Sampling tree space is the most challenging aspect of Bayesian phylogenetic inference. The sheer number of alternative topologies is problematic by itself. In addition, the complex dependency between branch lengths and topology increases the difficulty of moving efficiently among topologies. Current tree proposals are fast but sample new trees using primitive transformations or re-mappings of old branch lengths. This reduces acceptance rates and presumably slows down convergence and mixing. Here, we explore branch proposals that do not rely on old branch lengths but instead are based on approximations of the conditional posterior. Using a diverse set of empirical data sets, we show that most conditional branch posteriors can be accurately approximated via a Γ distribution. We empirically determine the relationship between the logarithmic conditional posterior density, its derivatives, and the characteristics of the branch posterior. We use these relationships to derive an independence sampler for proposing branches with an acceptance ratio of ∼90% on most data sets. This proposal samples branches between 2× and 3× more efficiently than traditional proposals with respect to the effective sample size per unit of runtime. We also compare the performance of standard topology proposals with hybrid proposals that use the new independence sampler to update those branches that are most affected by the topological change. Our results show that hybrid proposals can sometimes noticeably decrease the number of generations necessary for topological convergence. Inconsistent performance gains indicate that branch updates are not the limiting factor in improving topological convergence for the currently employed set of proposals. However, our independence sampler might be essential for the construction of novel tree proposals that apply more radical topology changes.
Supplementary material 4 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Table S3
Supplementary material 6 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Figure S1. Pictures were taken with a digital microscope (Keyence VHX-6000, Keyence, Osaka, Japan)
Supplementary material 1 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Protocol 1 – DIY-DS
Supplementary material 3 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Table S2. Raw read table
Supplementary material 2 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Table S1. PCR primers used in this study
Supplementary material 8 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Figure S3
Supplementary material 5 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Script 1
Stool Sampling in the Acute Isolated Patient , Efficient Acquiring With a Novel Device
ClinicalTrials.gov study NCT06495775. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.