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1,474 results for “Reliability”
Test Collection Reliability: A Study of Bias and Robustness to Statistical Assumptions via Stochastic Simulation
<p>This archive contains the simulated collections, their diagnosis data, and the estimates of accuracy. For the full code and description, please refer to https://github.com/julian-urbano/irj2015-reliability</p>
CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images
<p>This is the dataset from the preprint "Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images" by Raufeisen et al. (2024). It contains 6,683 annotations (3,991 nuclei and 2,607 whole cells) of head and neck squamous cell carcinoma cells in hematoxylin and eosin stained histological images. The annotations are in COCO format and distributed over 83 PNG images. Cyto R-CNN was trained on this dataset and compared with other state-of-the-art methods. The CytoNuke dataset is released under the CC BY 4.0 license.</p> <p>The histological images are from the CPTAC dataset:<br>National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC). (2018). The Clinical Proteomic Tumor Analysis Consortium Head and Neck Squamous Cell Carcinoma Collection (CPTAC-HNSCC) (Version 15) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.UW45NH81</p> <p>Funding: Behrus Puladi was funded by the Medical Faculty of RWTH Aachen University as part of the Clinician Scientist Program. We acknowledge FWF enFaced 2.0 [KLI 1044, https://enfaced2.ikim.nrw/] and KITE (Plattform für KI-Translation Essen) from the REACT-EU initiative [https://kite.ikim.nrw/, EFRE-0801977]. Fabian Hörst, Jianning Li, Jens Kleesiek and Jan Egger received funding from the Cancer Research Center Cologne Essen (CCCE).</p>
Data for Project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen'
<p>Data for Project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen' consisting of (1) the complete data set of all data analyzed for the project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen' ('Data_Brain-IT-Validation-Qmci_for-publication.xlsx'; and (2) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
Supplementary File 7 from: Rapier-Sharman N et. al., Secondary Transcriptomic Analysis of Triple-Negative Breast Cancer Reveals Reliable Universal and Subtype-Specific Mechanistic Markers, 2024
<p>Supplementary Materials File 7. Please note that though the order of the supplementary materials has changed since initial upload (File S7 was previously File S9 or S10), the contents of this zipped folder remain the same.</p>
Figure 1 in Determination of an efficient and reliable method for PCR detection of borrelial DNA from engorged ticks
Figure 1. Different amount of starting material – A. The whole individual of partially engorged tick; B. Anterior half of fully-engorged tick (above red line); C. Paired DNA extraction – mouthparts (1) and a part of scutum (2).
Figure 2 in Determination of an efficient and reliable method for PCR detection of borrelial DNA from engorged ticks
Figure 2. Results of PCR amplification – A1. Efficient PCR with clear band of 250bp presented as successful amplification, determined by using a positive control; All amplified bands with sizes differing from the positive control were defined as non-specific alleles; Four types of different PCR results: A2. Non-specific alleles; A3. Poor amplification; A4. A combination of poor amplification and non-specific alleles; B. Unsuccessful amplification detected in paired DNA extraction, after using DNA obtained from mouthparts (B1), whereas target region was amplified for the same sample but using DNA obtained from a part of scutum (B2); a 50 bp DNA ladder (BioLabs, New England) (A, B).
Fault tree reliability analysis via squarefree polynomials
<p>Artefact for the paper "Fault tree reliability analysis via squarefree polynomials" by Milan-Lopuhaä-Zwakenberg, MODELSWARD 2024.</p>
Calibration curves for Folin-Ciocalteu assay for reliable quantification of total polyphenols.
<p><span>Five different protocols (P1-P5) for determining total polyphenol content in samples were investigated and compared. Detailed information on the protocols are provided in: <br></span></p> <p><span>P1 </span><span>Singleton, V.L.; Rossi, J.A. Colorimetry of Total Phenolics with Phosphomolybdic-Phosphotungstic Acid Reagents. <em>Am J Enol Vitic.</em> <strong>1965</strong>, <em>16</em>, 144–158, doi:10.5344/ajev.1965.16.3.144.</span></p> <p><span>P2<span> </span>Singleton, V.L.; Orthofer, R.; Lamuela-Raventós, R.M. [14] Analysis of Total Phenols and Other Oxidation Substrates and Antioxidants by Means of Folin-Ciocalteu Reagent. In <em>Methods in Enzymology</em>; Oxidants and Antioxidants Part A; Academic Press, 1999; Vol. 299, pp. 152–178.</span></p> <p><span>P3<span> </span>Cicco, N.; Lanorte, M.T.; Paraggio, M.; Viggiano, M.; Lattanzio, V. A Reproducible, Rapid and Inexpensive Folin–Ciocalteu Micro-Method in Determining Phenolics of Plant Methanol Extracts. <em>Microchemical Journal</em> <strong>2009</strong>, <em>91</em>, 107–110, doi:10.1016/j.microc.2008.08.011.</span></p> <p><span>P4. Sotenko, M.; Coles, S.; Barker, G.; Song, L.; Jiang, Y.; Longhurst, P.; Romanova, T.; Shuvaeva, O.; Kirwan, K. Phytoremediation-Biorefinery Tandem for Effective Clean-up of Metal Contaminated Soil and Biomass Valorisation. <em>International Journal of Phytoremediation</em> <strong>2017</strong>, <em>19</em>, 965–975, doi:10.1080/15226514.2016.1267705.</span></p> <p><span>P5<span> </span>Tsibranska, I.; Tylkowski, B.; Kochanov, R.; Alipieva, K. Extraction of Biologically Active Compounds from <em>Sideritis</em> Ssp. L. <em>Food and Bioproducts Processing</em> <strong>2011</strong>, <em>89</em>, 273–280, doi:10.1016/j.fbp.2010.10.004.</span></p> <p> </p> <p><span><span>Total polyphenol content was determined spectrophotometrically using different variation of Folin–Ciocalteu’s method by means of UV-VIS UV-1800 Shimadzu spectrophotometer (Tokio, Japan). </span><span>The Folin-Ciocalteu reagent, a</span><span> mixture of phosphomolybdic and phosphotungstic acids reacts with phenolic compounds and is reduced, forming a blue chromophore detectable spectrophotometrically. Seven concentration levels of gallic acid standard were used to construct the calibration curves: L1 - 0.6 g/L, L2 - 0.4 g/L, L3 - 0.2 g/L, L4 - 0.1 g/L, L5 - 0.05 g/L, L6 - 0.025 g/L and L7 - 0.0125 g/L. A 1 g/L gallic acid stock solution was prepared in Milli-Q water. Each concentration level was measured in triplicate using all the investigated polyphenol determination methods. Once the calibration curves for each method were prepared, </span><span>two independent standard solutions were used for methods validation. The first one was gallic acid at a concentration of 0.2 g/L prepared in Milli-Q water. The second standard was a commercial polyphenol extract Vitaflavan at a concentration of 0.2 g/L which contains 80% of total polyphenols. Each standard solution was measured in triplicate using all the methods investigated. </span></span></p>
Testing Protocols for Obtaining Reliable PDFs from Laboratory x-ray Sources Using PDFgetX3
<p>In this work, we explored data acquisition protocols and improved data reduction protocols using PDFgetX3 to obtain reliable data for atomic pair distribution function (PDF) analysis from a laboratory-based Mo x-ray source. A variable counting scheme is described that preferentially counts in the high-angle region of the diffraction pattern. The effects on the resulting PDF are studied by varying the overall count time, the use of Soller slits, and limiting the out-of-plane divergence of the incident beam. The protocols are tested using an amorphous silica and a quartz sample. We also present a modification to the current PDFgetX3 data corrections to take care of sample absorption, which was previously neglected in the use of that program for high-energy synchrotron x-ray data. We show that, despite limitations in the Q-range and flux of laboratory instruments, reasonable data for PDF model fits may be obtained using the best protocols in a few hours of counting. </p>
Reliability of citations of medRxiv preprints in articles published on COVID-19 in the world leading medical journals
<p>Articles published on COVID in 2020 in the BMJ, The Lancet, the JAMA and the NEJM were manually screened to identify all articles citing at least one preprint from medRxiv. We searched PubMed, Google and Google Scholar to assess if the preprint had been published in a peer-reviewed journal, and when. Published articles were screened to assess if the title, data or conclusions were identical to the preprint version.</p>
Data and Analysis for "On the Reliability of Coverage-based Fuzzer Benchmarking"
<pre><strong>Data and Analysis for "On the Reliability of Coverage-based Fuzzer Benchmarking"</strong> <strong>## Cite</strong> </pre> <pre><code>@inproceedings{benchmarking, author = {B{\"o}hme, Marcel and Szekeres, L{\'a}szl{\'o} and Metzman, Jonathan}, title = {On the Reliability of Coverage-based Fuzzer Benchmarking}, year = {2022}, booktitle = {Proceedings of the 44th International Conference on Software Engineering}, series = {ICSE '22}, pages = {1-13}, doi = {10.1145/3510003.3510230} }</code></pre> <pre> <strong>## Data Analysis</strong> The Jupyter notebook generating all tables and figures can be found in fuzzbench.manual.ipynb <strong>## Generated Images and Tables</strong> The generated data analysis artifacts are also available in this artifact. <strong>## Data</strong> All the data is available in the FuzzBench Reports and will be automatically downloaded. * 20 trials of 23 hours with 15 programs and 10 fuzzers. * Experiment name: 2021-02-17-bug-paper * Report: https://www.fuzzbench.com/reports/2021-02-17-bug-paper/index.html * Data: https://www.fuzzbench.com/reports/2021-02-17-bug-paper/data.csv.gz * Fuzzbench Commit: [38e344fef2f1079579391a0d9dcb52319f7051f2](https://github.com/google/fuzzbench/commits/38e344fef2f1079579391a0d9dcb52319f7051f2) * 30 trials of 23 hours with 11 programs and 10 fuzzers. * Experiment name: 2021-08-19-crash-s * Report: https://www.fuzzbench.com/reports/2021-08-19-crash-s/index.html and * Data: https://www.fuzzbench.com/reports/2021-08-19-crash-s/data.csv.gz * Fuzzbench Commit: db192b60815ac87f69ee0f7f3e37aeac71949e1b * 30 trials of 23 hours with 11 programs and 10 fuzzers. * Experiment name: 2021-08-19-crash-s2 * Report: https://www.fuzzbench.com/reports/2021-08-19-crash-s2/index.html and * Data: https://www.fuzzbench.com/reports/2021-08-19-crash-s2/data.csv.gz * Fuzzbench Commit: db192b60815ac87f69ee0f7f3e37aeac71949e1b The deduplicated data can be found in * 2021-02-17-bug-paper-fixed2.csv.gz * 2021-08-19-crash-s-fixed2.csv.gz * 2021-08-19-crash-s2-fixed2.csv.gz <strong>## Reproducibility</strong> </pre> <pre><code class="language-bash"># Download the precise version of FuzzBench used for the experiment git clone https://github.com/google/fuzzbench.git cd fuzzbench git checkout <Fuzzbench Commit> # Download the internal config file. curl https://storage.googleapis.com/[experiment-name]/config/experiment.yaml > /tmp/experiment-config.yaml make install-dependencies # Launch the experiment using paramters from the internal config file. PYTHONPATH=. python experiment/reproduce_experiment.py -c /tmp/experiment-config.yaml -e <new_experiment_name></code></pre> <p> </p>
The impact of low input DNA on the reliability of DNA methylation as measured by the Illumina Infinium MethylationEPIC BeadChip, supplementary table 3
<p>Supplementary table 3: Summary statistics from an EWAS assessing the relationship between variance in DNA methylation value and DNA input level.</p>
Fig. 10 in Taxonomic Reassessment of Albula (Albuliformes: Albulidae) from Japan and Adjacent Waters with Reliable Records of Albula argentea, A. koreana and A. oligolepis from Japan
Fig. 10. Relationships among numbers of scale rows (SR) between lateral line (LL) and mid-dorsal-fin base (DB), vertebrae and pored lateral-line scales in Albula argentea (red circles), A. koreana (green downward-triangles) and A. oligolepis (yellow upward-triangles).
Fig. 11 in Taxonomic Reassessment of Albula (Albuliformes: Albulidae) from Japan and Adjacent Waters with Reliable Records of Albula argentea, A. koreana and A. oligolepis from Japan
Fig. 11. Neighbor-joining (NJ) dendrogram showing derived p- distance matrix from parietal mitochondrial Cytochrome b gene sequence. Numbers at branches indicate>50% bootstrap probabilities in 1000 bootstrap replications. Colored (italicized) sample names indicate determined in present study; other sample names indicate accession numbers (see Table 1). At, EP and IP indicate Atlantic Ocean, Eastern Pacific Ocean and Indo-Pacific Oceans, respectively (showing distributional ranges).
Fig. 8 in Taxonomic Reassessment of Albula (Albuliformes: Albulidae) from Japan and Adjacent Waters with Reliable Records of Albula argentea, A. koreana and A. oligolepis from Japan
Fig. 8. Heads of Albula koreana (A) and A. argentea (B), showing comparison of color markings. (A) KAUM–I. 125129, 246.9 mm SL, Taiwan; (B) KAUM–I. 50220, 347.0 mm SL, Japan. Photos by KAUM.
Fig. 7 in Taxonomic Reassessment of Albula (Albuliformes: Albulidae) from Japan and Adjacent Waters with Reliable Records of Albula argentea, A. koreana and A. oligolepis from Japan
Fig. 7. Distributional map of Albula argentea (red circles), A. glossodonta (blue diamond), A. koreana (green downward-triangles) and A. oligolepis (yellow upward-triangles) in Indo-Pacific region (A) and Japan and adjacent waters (B). Albula glossodonta omitted in A. Closed and open symbols indicate records based on examined specimens and literature.
Fig. 6 in Taxonomic Reassessment of Albula (Albuliformes: Albulidae) from Japan and Adjacent Waters with Reliable Records of Albula argentea, A. koreana and A. oligolepis from Japan
Fig. 6. Frontal (top) and dorsal (bottom) views of snouts of (A) Albula argentea, (B) A. oligolepis and (C) A. koreana. (A) KAUM–I. 80897, 293.5 mm SL, Kagoshima, Japan; (B) NSMT-P 129038, 289.0 mm SL, Kagoshima, Japan; (C) KAUM–I. 1246, 250.1 mm SL, Kagoshima, Japan. Arrow in C indicates blotch on snout tip.
Fig. 5 in Taxonomic Reassessment of Albula (Albuliformes: Albulidae) from Japan and Adjacent Waters with Reliable Records of Albula argentea, A. koreana and A. oligolepis from Japan
Fig. 5. Dorsum of (A–C) Albula argentea and (D–F) A. koreana in preserved specimens (A, D), and schematic drawings (B, C, E, F). Scales with number indicate scale rows between lateral line and mid-dorsal-fin base; red, blue and yellow scales indicate 10th, 9th and 8th scale rows (SR) from lateral line; scales with small circle indicate pored lateral-line scales (PLS). (A, B) KAUM–I. 9013, 265.4 mm SL, Kagoshima, Japan; (C) KAUM–I. 80897, 293.5 mm SL, Kagoshima, Japan; (D, E) KAUM–I. 125129, 246.9 mm SL, Kaohsiung, Taiwan; (F) KAUM–I. 1246, 250.1 mm SL, Kagoshima, Japan.
Fig. 4 in Taxonomic Reassessment of Albula (Albuliformes: Albulidae) from Japan and Adjacent Waters with Reliable Records of Albula argentea, A. koreana and A. oligolepis from Japan
Fig. 4. Ventral views of lower jaw of preserved specimens of (A) Albula argentea, (B) A. koreana, (C) A. oligolepis and (D) A. glossodonta. (A) KAUM–I. 80897, 293.5 mm SL, Kagoshima, Japan; (B) KAUM–I. 1246, 250.1 mm SL, Kagoshima, Japan; (C) NSMT-P 129038, 289.0 mm SL, Kagoshima, Japan; (D) NSMT-P 102552, 273.5 mm SL, Okinawa, Japan. Bars indicate 2 mm.
Fig. 3 in Taxonomic Reassessment of Albula (Albuliformes: Albulidae) from Japan and Adjacent Waters with Reliable Records of Albula argentea, A. koreana and A. oligolepis from Japan
Fig. 3. Preserved specimens of (A) Albula argentea, (B) A. glossodonta, (C) A. koreana and (D) A. oligolepis from Japan. (A) KAUM–I. 80897, 293.5 mm SL, Uchinoura Bay, Kagoshima; (B) NSMT-P 102552, 273.5 mm SL, Iriomote-jima Island, Okinawa; (C) KAUM–I. 1246, 250.1 mm SL, Kasasa, Minamisatsuma, Kagoshima; (D) NSMT-P 129038, 289.0 mm SL, Amami-oshima Island, Kagoshima.
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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.