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54 results for “sampling efficiency”

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zenodo32/100

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>

opencc-by-4.0Jun 2024View details →
zenodo32/100

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.

opencc-by-4.0Jan 2024View details →
zenodo32/100

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).

opennotspecifiedDec 2011View details →
zenodo32/100

Machine Learning-Assisted Sampling of SERS Substrates Improves Data Collection Efficiency: raw data and code

<p>Raw datasets and media accompanying the manuscript:&nbsp;<strong>Machine Learning-Assisted Sampling of SERS Substrates Improves Data Collection Efficiency</strong>: data, published in <em>Applied Spectroscopy </em>in 2021</p>

opencc-zeroJul 2021View details →
zenodo32/100

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 &amp; B) kick-net samples and C &amp; D) malaise trap samples

opencc-zeroJul 2021View details →
ClinicalTrials.gov32/100

Efficiency of Two Glucose Sampling Protocols for Maintenance of Euglycemia

ClinicalTrials.gov study NCT00993057. IPD Sharing: NO. Countries: 1. Publications: 11.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

Network-Targeted Strategies for Efficient Community SARS-CoV-2 (COVID-19) Sampling

ClinicalTrials.gov study NCT04437706. IPD Sharing: NO. Countries: 1. Publications: 2.

closedIPD-NOFeb 2026View details →
dryad32/100

Data from: Empowering conservation practice with efficient and economical genotyping from poor quality samples

Open the record for dataset details and reuse information.

publicApr 2019View details →
zenodo28/100

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>&nbsp;</p>

opencc-by-4.0May 2023View details →
zenodo28/100

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).

opencc-by-4.0Jul 2021View details →
zenodo28/100

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>

openOct 2024View details →
dryad28/100

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.

opencc-zeroDec 2014View details →
zenodo28/100

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

opencc-zeroJul 2021View details →
zenodo28/100

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)

opencc-zeroJul 2021View details →
zenodo28/100

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

opencc-zeroJul 2021View details →
zenodo28/100

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

opencc-zeroJul 2021View details →
zenodo28/100

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

opencc-zeroJul 2021View details →
zenodo28/100

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

opencc-zeroJul 2021View details →
zenodo28/100

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

opencc-zeroJul 2021View details →
ClinicalTrials.gov28/100

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.

restrictedIPD-UNDECIDEDFeb 2026View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record