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11,177
datasets available to search
ShareScore release 0.9.0
Dataset results
11,177 results for “identify”
'Proto-rivalry': how the binocular brain identifies gloss
<p>Dataset relative to the following publication:</p> <p>Muryy, A. A., Fleming, R. W., & Welchman, A. E. (2016). ‘Proto-rivalry’: how the binocular brain identifies gloss. <em>Proceedings of the Royal Society B, 283(1830)</em>: 20160383.</p> <p>Each folder contains *.mat files with the data relative to the respective experiment(s) and a matlab script for loading and plotting the data. The matlab scripts also include comments regarding the data files.</p>
Replication data for Anterior Cruciate Ligament Injury: identifying information sources and risk factor awareness among the general population
<p>Creating awareness about a disorder is important for prevention from the perspective of public health. However, for sports injuries, like the anterior cruciate ligament (ACL) injury, there is no study which has investigated the awareness of risk factors for the injury and prevention methods among the general population, to the best of our knowledge. The sources of information among the population are also unclear. The purpose of present study was to identify these aspects of public awareness about the ACL injury.</p> <p>A questionnaire was randomly distributed among the general population registered with a web based questionnaire supplier, to recruit 900 participants who were aware about the ACL injury. The questionnaire consisted of two parts: Question 1 asked them about their sources of information regarding the ACL injury; Question 2 asked them about the risk factors for ACL injury. Multivariate logistic regression was used to determine the information sources that provide a good understanding of the risk factors.</p> <p>The leading source of information for ACL injury was television (57.0%). However, the results of logistic regression analysis revealed that television was not an effective medium to create awareness about the risk factors, among the general population. Instead “Lecture by a coach”, “Classroom session on Health”, and “Newspaper” were significantly effective in creating a good awareness of the risk factors (p < 0.001).</p>
miRNA counts identified by RNA seq in the caudate nucleus of patients neuropathologically diagnosed with late-onset Alzheimer's disease (non-carriers and carriers of intermediate expansions in the HTT gene) and healthy subjects.
<p>These data include the raw miRNA counts identified by RNA-seq in <em>post mortem</em> caudate nucleus samples. Samples with identifier <strong>A</strong> belong to patients with a neuropathological diagnosis of late-onset Alzheimer's disease. Group <strong>B</strong> samples belong to patients with the same neuropathological diagnosis, but carrying CAG expansions in the intermediate range (27-35 CAG) in the <em>HTT</em> gene. Finally, group <strong>C </strong>samples belong to healthy subjects without neuropathological diagnosis. </p> <p> </p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Petra De Block, <a href="https://orcid.org/0000-0001-7210-7868">https://orcid.org/0000-0001-7210-7868</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Katherine Parys, <a href="https://orcid.org/0000-0001-7459-3687">https://orcid.org/0000-0001-7459-3687</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Torsten Dikow, <a href="https://orcid.org/0000-0003-4816-2909">https://orcid.org/0000-0003-4816-2909</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by M. Andrew Johnston, <a href="https://orcid.org/0000-0002-0166-6985">https://orcid.org/0000-0002-0166-6985</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk
<p>sushie.molqtl.weights.tar.gz contains ancestry-specific eQTL and pQTL weights trained on mRNA and protein levels measured in American European, American African, and American Hispanic ancestries from TOPMed-MESA and GENOA studies. Column “a1” is the counting allele.</p> <p>mesa.*.fusion.tar.gz contains the weights in FUSION format.</p> <p>sushie_real_data_results.tar.gz contains all the real data analyzed in the sushie project.</p> <p>sushie_sim_data_results.tar.gz contains all the sim data analyzed in the sushie project.</p> <p>sushie_analysis_codes.tar.gz contains all the codes and scripts to generate and analyze these data.</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Katja C. Seltmann, <a href="https://orcid.org/0000-0001-5354-6048">https://orcid.org/0000-0001-5354-6048</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Sarah Wagner, <a href="https://orcid.org/0000-0002-8961-7730">https://orcid.org/0000-0002-8961-7730</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
LABind: Identifying Protein Binding Ligand-Aware Sites via Learning Interactions Between Ligand and Protein
<p>This dataset contains the three datasets used in LABind. For each dataset, we have saved the corresponding FASTA sequence files, the associated labels (0 for non-binding and 1 for binding), and the corresponding PDB files.</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Shawn E. Krosnick, <a href="https://orcid.org/0000-0002-8027-5295">https://orcid.org/0000-0002-8027-5295</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Sofie Meeus, <a href="https://orcid.org/0000-0003-0715-8647">https://orcid.org/0000-0003-0715-8647</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Luis Moreira da Costa, <a href="https://orcid.org/0000-0001-5910-7126">https://orcid.org/0000-0001-5910-7126</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
ParaMask, a new method to identify multicopy genomic regions, corrects major biases in whole-genome sequencing data. Additional Datasets.
<p>Data supporting the main figures of the "ParaMask, a new method to identify multicopy genomic regions, corrects major biases in whole-genome sequencing data" manuscript and a copy of the ParaMask software and scripts for analysis, and SV calls from longreads. README files are included.</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Evan Waite, <a href="https://orcid.org/0000-0001-6877-3964">https://orcid.org/0000-0001-6877-3964</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Jennifer C. Girón Duque, <a href="https://orcid.org/0000-0002-0851-6883">https://orcid.org/0000-0002-0851-6883</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Sabine von Mering, <a href="https://orcid.org/0000-0003-2982-7792">https://orcid.org/0000-0003-2982-7792</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Publication identifiers of the perovskite dataset and the QA code
<p>The dataset contains the identifiers for separate 194,322 publications used in the article 'Question Answering models for information extraction from perovskite materials science literature'. The publications are separated by the data provider. For articles scraped from Core API and arXiV API which did not contain DOI, the identifier is either arXiV API identifier or Core API identifier.</p> <p>The zipped folder "QA_for_MS-main.zip" contains the codes used in the article. They are also available in https://gitlab.com/mil-utu/QA_for_MS.</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Brad Ruhfel, <a href="https://orcid.org/0000-0002-0230-4185">https://orcid.org/0000-0002-0230-4185</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
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.