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4,566 results for “marker”
Radiographic data of a wooden block with metal markers at the J. Paul Getty Museum X-ray facility
<p><strong>Summary</strong></p> <p>This submission contains radiographic data containing metal markers of a small wooden block. </p> <p>The data is made available as part of [Bossema et al., 2024].</p> <p><em> </em></p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the X-ray facility located at the J. Paul Getty Museum, Los Angeles. Full details can be found in [Bossema et al., 2024].</p> <p><em> </em></p> <p><strong>List of Contents</strong></p> <p>The content of the submission is given below. All raw data (i.e. no corrections) is made available in .tif format.</p> <p>The folder contains:</p> <ul> <li>darks: folder containing darkfield images, IMG<em>*.dicom</em></li> <li>flats: folder containing flatfield images, IMG<em>*.dicom</em></li> <li>data1: raw (unprocessed or uncorrected) projection data, IMG<em>*.dicom</em></li> <li>data2: raw (unprocessed or uncorrected) projection data, IMG<em>*.dicom</em></li> </ul> <p> </p> <p><strong>Additional Links</strong></p> <p>Accompanying code can be found here.</p> <p><em> </em></p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please get in touch with </p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p><strong>References</strong></p> <p><a href="https://www.nature.com/articles/s41467-024-48102-w">Bossema, F.G., Palenstijn, W.J., Heginbotham, A. <em>et al.</em> Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums. <em>Nat Commun</em> <strong>15</strong>, 3939 (2024). https://doi.org/10.1038/s41467-024-48102-w</a></p> <p><em> </em></p> <p> </p>
Radiographic data of a wooden block with metal markers at the Rijksmuseum X-ray facility
<p><strong>Summary</strong></p> <p>This submission contains radiographic data containing metal markers of a small wooden block. </p> <p>The data is made available as part of [Bossema et al., 2024].</p> <p><em> </em></p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the X-ray facility located at the Rijksmuseum, Amsterdam. Full details can be found in [Bossema et al., 2024].</p> <p><em> </em></p> <p><strong>List of Contents</strong></p> <p>The content of the submission is given below. All raw data (i.e. no corrections) is made available in .tif format.</p> <p>The folder contains:</p> <ul> <li>darks_flats: folder containing darkfield and flatfield images, <em>frame*.tif</em></li> <li>data: raw (unprocessed or uncorrected) projection data, <em>frame*.tif</em></li> </ul> <p> </p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a>Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). Accompanying code can be found here.</p> <p><em> </em></p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please get in touch with </p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p><strong>References</strong></p> <p><a href="https://www.nature.com/articles/s41467-024-48102-w">Bossema, F.G., Palenstijn, W.J., Heginbotham, A. <em>et al.</em> Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums. <em>Nat Commun</em> <strong>15</strong>, 3939 (2024). https://doi.org/10.1038/s41467-024-48102-w</a></p>
Radiographic data of a wooden block with metal markers at the FleX-ray laboratory (high resolution)
<p><strong>Summary</strong></p> <p>This submission contains radiographic data containing metal markers of a small wooden block. </p> <p>The data is made available as part of [Bossema et al., 2024].</p> <p><em> </em></p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020].</p> <p><strong>List of Contents</strong></p> <p>The content of the submission is given below. All raw data (i.e. no corrections) is made available in .tif format.</p> <p>The data folder contains:</p> <ul> <li>darkfield image, <em>di000000.tif</em>,</li> <li>flatfield image before acquisition, <em>io000000.tif</em>, and after acquisition, <em>io000001.tif</em>,</li> <li>raw (unprocessed or uncorrected) projections, <em>scan_*.tif</em>,</li> <li><em>data settings XRE.txt</em>, a text file with scanner metadata,</li> <li><em>scan settings.txt</em>, a text file with scanner metadata in more human readable format, and</li> <li><em>data settings XRE.ini</em>, a snapshot text file of basic geometry information at the start of a scan.</li> <li><em>script_executed.txt</em>, the text file containing the list of commands the apparatus has executed.</li> </ul> <p> </p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a>Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a>GitHub page</a>.</p> <p><em> </em></p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please get in touch with </p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p><em> </em></p> <p><strong>Acknowledgments</strong></p> <p>The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project numbers 341-60-001, 639.073.506 and 628.007.033 and Netherlands Institute for Conservation, Art and Science (NICAS).</p> <p><strong>References</strong></p> <p>S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” <em>J. Imaging</em>, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p><a href="https://www.nature.com/articles/s41467-024-48102-w">Bossema, F.G., Palenstijn, W.J., Heginbotham, A. <em>et al.</em> Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums. <em>Nat Commun</em> <strong>15</strong>, 3939 (2024). https://doi.org/10.1038/s41467-024-48102-w</a></p>
Dataset for the manuscript "Amapari Marker Band Metal-Enrichments: Potential Mechanisms and Implications for Surface and Subsurface Water and Weathering in Gale crater"
<p>The dataset for the main manuscript contains three CSV files: list of long distance remote micro images (LDRMIs), the ChemCam chemical data, and the alpha particle X ray spectrometer (APXS) chemical data. The LDRMI csv lists two sets of data, one for the sol (Mars solar day), sequence identifier, and target name of LDRMIs that target the Amapari Marker Band (AMB). These are represented as green points on manuscript Figure 1. The second list are for LDRMI images that did not specifically target the AMB, but contain the AMB, and these are orange points on manuscript Figure 1. The ChemCam data file has metadata for each observation point in a ChemCam target: file name, spacecraft clock, observation number, stratigraphic member, rock type, target distance, and total of all spectral channels. Chemical information includes: the oxide chemistry (oxide wt%), uncertainty (RMSEP accuracy and shot-to-shot precision), minor element quantification, and minor element uncertainty (ppm), preliminary sulfate composition (without uncertainty and only provided up to sol 3778), peak areas for some elements (Cu, Zn, and Cr). The oxide sum includes all oxides and elements for which we have quantitative data. Spectral unmixing qualitative values are listed for each element. Derived data includes the corrected MgO, Na<sub>2</sub>O, and CaO in both mol and oxide wt% (values before and after correction are provided), and the calculated alteration index. Derived data is only calculated for AMB bedrock targets (does not include veins or mixed vein-rock targets with elevated CaO). The APXS data sheet provides sol, target name, oxide data, and uncertainties for the drill tailings observations, and the derived data for each target.</p>
Automated cell annotation in scRNA-seq data using unique marker gene sets
<p>Single-cell RNA sequencing has revolutionized the study of cellular heterogeneity, yet accurate cell type annotation remains a significant challenge. Inconsistent labels, technological variability, and limitations in transferring annotations from reference datasets hinder precise annotation. This study presents a novel approach for accurate cell type annotation in scRNA-seq data using unique marker gene sets. By manually curating cell type names and markers from 280 publications, we verified marker expression profiles across these datasets and unified nomenclatures to consistently identify 166 cell types and subtypes. Our customized algorithm, which builds on the AUCell method, achieves accurate cell labeling at single-cell resolution and surpasses the performance of reference-based tools like Azimuth, especially in distinguishing closely related subtypes. To enhance accessibility and practical utility for researchers, we have also developed a user-friendly application that automates the cell typing process, enabling efficient verification and supporting comprehensive downstream analyses. The desktop application can be accessed at <a href="https://omnibusx.com/">https://omnibusx.com</a>.</p>
Molecular markers for taxonomic validation of 7 deep water invertebrate animals
<p>File 1: MolecularBarcodeSeqs_forValidation.fasta</p> <p>Molecular markers extracted from transcriptome assemblies for validation of animal taxonomic identification.</p> <p>Files 2-4: 2022-03-29-01_JAB_FALKOR_RNA_1, 2022-03-29-01_JAB_FALKOR_RNA_2, 2022-03-29-01_JAB_FALKOR_RNA_2.</p> <p>TapeStation data for total RNA quality assessment. Identifiers in the Sample Description column are linked to identifiers in the published table, <span>Table 2. RNA and sequence statistics for each specimen, from the manuscript: </span><em><span>Transcriptome sequencing of seven deep marine invertebrates</span></em></p>
Infection of mice by the enteroaggregative E. coli strain 042 and two mutant derivatives overexpressing virulence factors: impact on disease markers, gut microbiota and concentration of SCFAs in feces
<p>This dataset contains raw sequencing data from the microbiota analysis conducted in the enteroaggregative E. coli strain 042 study. The data includes FASTQ files generated from Illumina sequencing, along with metadata describing the sample collection and processing methods.</p>
Fig.1 in Testing The Microsatellites-Pcr Markers For Genetic Diversity Research Of Alien Ponto-Caspian Amphipod Pontogammarus Robustoides G. O. Sars, 1894
Fig.1. Localities of sampling sities in the Latvian reservoirs.
Gene Trees for Octopus bocki ANC neuron markers
<p>The evolutionary history was inferred by using the Maximum Likelihood method and Whelan And Goldman model [1]. The trees with the highest log likelihood are shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the JTT model, and then selecting the topology with superior log likelihood value. The trees are drawn to scale, with branch lengths measured in the number of substitutions per site. Evolutionary analyses were conducted in MEGA11 [2]</p> <p>1. Whelan, S. and Goldman, N. (2001). A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach. Molecular Biology and Evolution 18:691-699.<br>2. Tamura K., Stecher G., and Kumar S. (2021). MEGA 11: Molecular Evolutionary Genetics Analysis Version 11. Molecular Biology and Evolution https://doi.org/10.1093/molbev/msab120.</p>
Fig. 4 in Application of allozyme markers for screening of turbot populations along Western Black Sea coast
Fig. 4. Dendrogram of three turbot populations using PAUP cluster analysis of allozyme data.
Fig.1 in Application of allozyme markers for screening of turbot populations along Western Black Sea coast
Fig.1. Sampling localities of turbot samples
Fig. 1 in Soluble proteins in Messor structor (Latreille, 1798) (Hymenoptera: Formicidae) populations from Bulgaria - genetic variability and possible usage as population-genetic markers
Fig. 1. Sampling locations.
Quantifying the Spatial Distribution of Fluorescently-Labeled Cell Markers in Stroma-Rich Tumors
<p>Cell measurements (intensity for various compartments, cell morphology, spatial distance from stroma) from image processing of mouse xenograft tumour tissues stained for DAPI, Pan Cytokeratin, Fibronectin, pNDRG1 or Ki67. </p>
Brain signal variability within individually defined alpha frequency range as a marker of cognitive performance
<p><strong>General description</strong></p> <p>Eight subjects with severe acquired brain injury and fourteen healthy participants took part in the study. EEG was recorded at 19 international standard 10-20 system locations with a sampling frequency of 256 Hz, using an ear-linked reference and the <em>Fpz</em> location as ground.</p> <p>For all participants three-minute baseline recordings were obtained, one with eyes open and one with eyes closed. Both groups participated in the same sustained attention task while EEG data was being collected. Healthy participants underwent 10 blocks of 225 trials while ABI patients were given 7 blocks of 225 trials.</p> <p> </p> <p><strong>Content of the zip files uploaded here</strong></p> <p>Each zip file contains matlab files with raw EEG data plus a channel that contains information of the triggers used in the sustained attention task. In addition, log files contain the latency of the experimental events and responses.</p> <p> </p> <p><strong>Description of the task</strong></p> <p>The Continuous Temporal Expectancy Task (CTET), developed by O’Connell <em>et al.</em> (2009), is a computerized task that measures sustained attention. Participants are required to notice and respond with a click of the mouse when an alternating pattern (Fig. 1) is randomly displayed for a longer interval than the regular interval (800ms). Participants practiced the experiment initially to ensure they obtained 100% accuracy at the end of the training trials. Longer target duration should be easily detected during the training block, but challenging when performed over an extended period of time. The healthy group detected the target difference at 1120ms (duration 40% longer than the standard), whereas the ABI group detected a difference at 1200ms latency (duration 50% longer than the standard). The hand that responded to the longer stimulus with a mouse-click was counterbalanced in each block.</p>
Tau deposition is associated with imaging patterns of calcifications and immunhistochemical markers of osteogenesis in the P301L mouse model of human tauopathy
<p>Brain calcifications are associated with several neurodegenerative proteinopathies. Here, we report a new phenotype of imaging pattern of intracranial calcifications in transgenic P301L mice overexpressing 4 repeat human tau. P301L mice (Thy1.2) of 3, 5, 9 and 18-25 months-of-age and age-matched non-transgenic littermates (n = 4-11 per group) were assessed using <em>in vivo / ex vivo</em> magnetic resonance imaging with a gradient recalled echo sequence and micro computed tomography. Susceptibility weighted images computed from the gradient recalled echo data revealed regional hypointensities in the hippocampus, cortex, caudate nucleus and thalamus of P301L mice, in which corresponding phase images indicated diamagnetic lesions. In the hippocampus, occurrence of diamagnetic susceptibility calcifications increased with age. Concomitantly micro computed tomography detected hyperdense lesions. Immunochemical staining of brain sections revealed osteocalcin positive nodules, which is a marker of osteogenic maturation. Furthermore, we found vessel-associated and intra-neuronal osteocalcin positivity co-localizing with phosphorylated tau (AT8 and AT100) in the hippocampus. In contrast, osteocalcin containing nodules were vessel-associated, indicating ossified vessels, in the thalamus in absence of phosphorylated tau. In summary, we demonstrated imaging pattern of intracranial calcifications, early spheroid formation containing proteins associated with ossification along with phosphorylated tau in the P301L mouse model of human tauopathy.</p> <p> </p>
Figure. Experimental groups. in Effect of dual growth factor administration on oxidative markers during acute stage wound healing in rats
Figure. Experimental groups.
Fig. 1 in Isotopic discrimination and persistence of the C marker in adults of Anastrepha fraterculus (Diptera: Tephritidae) Brazilian-1 morphotype
Fig. 1. Schematic view of the diet switching experiments.
Files for 'Phage-bacteria dynamics during the first years of life revealed by trans-kingdom marker gene analysis'
<p>The archive contains most of the files that were used to generate the figures for "Phage-bacteria dynamics during the first years of life revealed by trans-kingdom marker gene analysis".</p> <p><span>data/TGVG_database_v1.1.VC_taxonomy_table.csv</span></p> <p><span>data/Marker-MAGu_virus_DB_v1.1_metadata.tsv</span></p> <p><span>data/MP142_CASE_CNTRL_T1D_LIST1.csv</span></p> <p><span>data/Mock_MSA1003_marker_magu_abundance_table1.tsv</span></p> <p><span>data/TGVG_database_v1.1.exemplars.iphop_genus_outputs1.csv</span></p> <p><span>data/TGVG_database_v1.1.exemplars.iphop_genus_outputs1.filt.csv</span></p> <p><span>data/some_teddy_MP142_metadata2.all_samples1.delivery.csv</span></p> <p><span>data/TGVG_database_v1.1.exemplars.iphop_genome_outputs1.csv</span></p> <p><span>data/Mock_MSA1003_mpa4_abundance_table.txt</span></p> <p><span>data/TGVG_database_v1.1.exemplars.bacphlip.tsv</span></p> <p><span>data/MP142_CASE_CNTRL_IA_LIST1.csv</span></p> <p><span>data/mp142_TGVG1.1_MPA4_combined_abundance_table_longform1.tsv</span></p> <p><span>data/mp142_TEDDY_marker_gene_detection_out1.reads_by_sample.tsv</span></p> <p><span>data/all_shuf_samples_samcov.tsv</span></p> <p><span>data/gtdb_ncbi_taxonomy_map_summary.tsv</span></p> <p><span>data/Mock_MSA1003_read_stats.tsv</span></p> <p><span>data/marker_gene_vir_bac_specificity_sensitivity_tests1.csv</span></p> <p><span>data/TGVG_database_v1.1.exemplars.iphop_genome_outputs1.filt.csv</span></p> <p><span>data/TGVG_database_v1.1.crassvirales_tbl.tsv</span></p> <p><span>data/TGVG_database_v1.1.checkv_completeness.tsv</span></p> <p> </p> <p>For code, see: <a href="https://github.com/mtisza1/teddy_vir_bac_marker_gene">https://github.com/mtisza1/teddy_vir_bac_marker_gene</a></p> <p> </p>
Virus Specific Markers
<p>CC by 4.0, <strong>Antonio Camargo</strong>, I am only redistributing part of the amazing work by A. Camargo and others, as described in their publication:</p> <p>Camargo, A. P., Roux, S., Schulz, F., Babinski, M., Xu, Y., Hu, B., Chain, P. S. G., Nayfach, S., & Kyrpides, N. C. — <em>Nature Biotechnology</em> (2023), DOI: 10.1038/s41587-023-01953-y.</p> <p> </p> <p>--</p> <p>This is the virus specific markers data set, the fraction of markers that are labeled as being at least specific for viruses, therefore labeled as `*V' or `V*' in the MMSeqs2 profile database file `genomad db v1.7.tar.gz' from https://zenodo.org/records/10594875, CC by 4.0, Antonio Camargo.<br>MMseqs2 Version: 15.6f452 was used to generate the database using the subcommand `createdb'.</p>
Multi markers Raw data
<p>Raw data from NGS sequencing targeting the 12S, 16S, 18S and COI marker. The data was used in a study of aquatic biodiversity on Réunion Island, to investigate the responses of biological communities to environmental and anthropogenic pressures using environmental DNA (JANNEL et al. 2024).</p>
ScienceDex guides
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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.