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1,199 results for “aligners”
Re-projection alignment for trajectory perturbation estimation in micro-tomography: simulation data
<p>Simulation cone-beam-tomography data sets including:</p> <p>a) Synthetic phantom tomogram (3 spheres + random ellipsoids)</p> <p>b) Forward projection of phantom using low-pitch-helix (LPH) trajectory</p> <p>c) Forward projection of phantom using space-filling (SF) trajectory</p> <p>d) Synthetic per-projection perturbations</p>
The Role of Lexical Alignment in Human Understanding of Explanations by Conversational Agents
<p>This dataset was collected as part of the research investigating the role of lexical alignment in understanding. The dataset consists of the responses of 179 participants recruited via Prolific (a crowdsourcing platform).</p> <p>1. Dialogue with a conversational agent (CA)</p> <p>2. Survey questionnaire responses from the participants.</p> <p>3. Primes selected or provided by the participants.</p> <p> </p> <p>This repository contains:</p> <ol> <li>Cleaned data sources listed above. The data is cleaned by removing data from participants who failed attention checks, or any other factor leading to error such as experiment setup, etc.</li> <li>A Python (Jupyter) playbook to analyse the collected data (Analysis_20230927.ipynb)</li> <li>Experiment design (exp_design_flow.png)</li> </ol>
Kinetic features dictate sensorimotor alignment in the superior colliculus.
<p>Tetrode recordings dataset from González-Rueda et al. “Kinetic features dictate sensorimotor alignment in the superior colliculus”, Nature 2024 (<em>https://doi.org/10.1038/s41586-024-07619-2)</em></p> <p>This subset of data corresponds to 5 C57BL6 male adult mice recorded both freely moving during an open field foraging task ("Open field") and head restrained during two visual stimulation tasks ("Visual Stimulation"). Neurons were recorded in both conditions across the superior colliculus using tetrodes in order to determine the sensorimotor alignment between static/kinetic visual features and egocentric head rotations. Detailled explanation of data organisation and content can be found in the file Guide.pdf.</p>
Dataset: Alignment Healthcare, Inc. (ALHC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Align Technology, Inc. (ALGN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Align Technology, Inc. (ALGN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: iShares Paris-Aligned Climate MSCI USA ETF (PABU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: iShares Paris-Aligned Climate MSCI World Ex USA ETF (PABD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: SPDR MSCI USA Climate Paris Aligned ETF (NZUS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: SPDR MSCI ACWI Climate Paris Aligned ETF (NZAC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
◂Fig. 6 A molecular phylogeny of 56 systematically representative Peridiniaceae, including 42 accessions assignable to P. cinctum from various geographic regions. Maximum likelihood tree (– ln = 21,884.93), as inferred from a rRNA nucleotide alignment (1137 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (CZE Czech Republic, E East, GER Germany, HET Heterocapsaceae, N North, PPE Protoperidiniaceae, POL Poland, rbn ribotype n, S South, SWE Sweden, UKR Ukraine, W West) in Bumps on the back: An unusual morphology in phylogenetically distinct Peridinium aff. cinctum (= Peridinium tuberosum; Peridiniales, Dinophyceae)
◂Fig. 6 A molecular phylogeny of 56 systematically representative Peridiniaceae, including 42 accessions assignable to P. cinctum from various geographic regions. Maximum likelihood tree (– ln = 21,884.93), as inferred from a rRNA nucleotide alignment (1137 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (CZE Czech Republic, E East, GER Germany, HET Heterocapsaceae, N North, PPE Protoperidiniaceae, POL Poland, rbn ribotype n, S South, SWE Sweden, UKR Ukraine, W West)
◂Fig. 4 A molecular tree of 51 systematically representative Peridiniaceae, including all 28 accessions assignable to P. volzii. Maximum Likelihood tree (–ln = 22,017.62), as inferred from a rRNA nucleotide alignment (1,129 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (abbreviations: HET, Heterocapsaceae; PPE, Protoperidiniaceae) in Morphological and molecular variability of Peridinium volzii Lemmerm. (Peridiniaceae, Dinophyceae) and its relevance for infraspecific taxonomy
◂Fig. 4 A molecular tree of 51 systematically representative Peridiniaceae, including all 28 accessions assignable to P. volzii. Maximum Likelihood tree (–ln = 22,017.62), as inferred from a rRNA nucleotide alignment (1,129 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (abbreviations: HET, Heterocapsaceae; PPE, Protoperidiniaceae)
Beam Alignment Measurements using a Hybrid Massive MIMO Testbed
<p>Measurement data and processing code for Mathworks Matlab underlying the beam alignment results of the publication. The results compare 3 algorithms for beam alignment and are measured at 2.4GHz using the Hybrid Massive MIMO testbed of the CommIT chair of the Technische Universität Berlin.</p> <p>Type of Data: Processed data (Measurement results and processing code)</p> <p>Hardware/software used: Technische Universität Berlin CommIT Hybrid Massive MIMO testbed, Mathworks Matlab</p> <p>Data format: Measurements: Matlab mat data files, Code: m UTF8 text files</p> <p>Source: experiments</p> <p>Number of samples: 2 locations</p> <p>Size per sample: 10 files per location</p> <p>Total size of samples: 170MB</p>
Reference data for "Limitations of alignment-free tools in total RNA-seq quantification"
<p>This repository contains reference data for "Limitations of alignment-free tools in total RNA-seq quantification"</p>
Turkish Makam Music Audio-Score Alignment Dataset
<p>This release contains the annotations and the scores to test the audio-score alignment methodology explained in:</p> <blockquote> <p><em>Şentürk, S., Gulati, S., and Serra, X. (2014). <strong>Towards alignment of score and audio recordings of Ottoman-Turkish makam music.</strong> In Proceedings of 4th International Workshop on Folk Music Analysis, pages 57–60, Istanbul, Turkey.</em></p> </blockquote> <p>The dataset in this release is derived from the transcription test dataset used in the paper:</p> <blockquote> <p><em>Benetos, E. & Holzapfel, A. (2013). <strong>Automatic transcription of Turkish makam music.</strong> In Proceedings of 14th International Society for Music Information Retrieval Conference, 4 - 8 Nov 2013, Curitiba, PR, Brazil.</em></p> </blockquote> <p>The scores for each composition are obtained from the SymbTr collection explained in:</p> <blockquote> <p><em>Karaosmanoğlu, K. (2012). <strong>A Turkish makam music symbolic database for music information retrieval: SymbTr.</strong> In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), pages 223–228.</em></p> </blockquote> <p>From the annotated score onsets for some of the above recordings only the main singing voice segments have been selected. Further separately only a subset of vocal onsets crresponding to phoneme transitions rules have been explicitly annotated as annotationOnsets.txt</p> <blockquote> <p><a href="http://mtg.upf.edu/biblio/author/810">Dzhambazov, G.</a>, <a href="http://mtg.upf.edu/biblio/author/644">Srinivasamurthy A.</a>, <a href="http://mtg.upf.edu/biblio/author/494">Şentürk S.</a>, & <a href="http://mtg.upf.edu/biblio/author/1012">Serra X.</a> (2016). <a href="http://mtg.upf.edu/node/3492">On the Use of Note Onsets for Improved Lyrics-to-audio Alignment in Turkish Makam Music</a>. 17th International Society for Music Information Retrieval Conference (ISMIR 2016</p> </blockquote> <p><strong>Using this dataset</strong></p> <p>Please cite the above publications if you use this dataset in a publication.</p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p> </p> <p><a href="http://compmusic.upf.edu/node/233">http://compmusic.upf.edu/node/233 </a></p>
JAAH: Audio-aligned jazz harmony dataset
<p>The dataset consists of annotations of 113 tracks selected from “The Smithsonian Collection of Classic Jazz” and “Jazz: The Smithsonian Anthology,” covering a range of performers, subgenres, and historical periods. Annotations were made by a jazz musician and contain information about the meter, structure, and chords for entire audio tracks.</p> <p>For referencing the JAAH, and obtaining a more detailed description of it, please refer to:</p> <blockquote>Eremenko, V., Demirel, E., Bozkurt, B., & Serra, X. (2018). <a href="https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&ved=2ahUKEwiU2cGnvoLgAhUkiKYKHT96AW0QFjACegQICBAC&url=https%3A%2F%2Fzenodo.org%2Frecord%2F1291834%2Ffiles%2FJazzHarmonyDataset_Ismir2018.pdf&usg=AOvVaw38mi5jVoPpKLWiIJYt5CGl">Audio-aligned jazz harmony dataset for automatic chord transcription and corpus-based research.</a> International Society for Music Information Retrieval Conference.</blockquote>
Alignments of simulated data for PsiCLASS evaluation
<p>BAM alignment files of simulated RNA-seq data used to evaluate PsiCLASS and other transcript assemblers (archive contains 25 files).</p>
Aggregated frequencies of transcription initiations observed in FANTOM5 CAGE data on GRCh38, including alignments with low mapping qualities
<p><strong>Overview</strong></p> <p>Aligned reads of the FANTOM5 CAGE data have been used after filtering (ones with mapping quality less than 20 or percent identity less than 85% were discarded) for general purpose, resulting in the data set consisting of only the reads aligned with confidence. The filtering process made possible to interpret the data without ambiguity, however it also limited interpretation of paralogous or duplicated regions within the genome. Here all of the 5'-ends of the CAGE read alignments, including the ones with low mapping quality, were counted. The counts in the individual profiles were aggregated and summed up. </p> <p> </p> <p><strong>Special usage note</strong></p> <p>As noted above, this data derived from the alignments with low mapping qualities, as well as the ones with high mapping qualities. The result has to be examined very carefully: observations on the genome does not support transcription initiation with confidence, and even absence of such observation does not support silence of transcription with confidence. For example, file size on the forward strand is substantially larger than the one on the reverse strand, which is likely caused by an arbitrary preference of the alignment process. It does not mean transcription happens more frequently on the forward strand. Interpretation has to be made always in comparison with the standard data (BED files under http://fantom.gsc.riken.jp/5/datafiles/reprocessed/hg38_v4/basic/ or bigWig files under http://fantom.gsc.riken.jp/5/datahub/hg38/reads/).</p> <p> </p> <p><strong>Data files</strong></p> <p>The resulting data files are formatted as bigWig (https://genome.ucsc.edu/FAQ/FAQformat.html#format6.1). '*.fwd.bw' and '*.rev.bw' represent forward and reverse strand on the genome, respectively. </p> <p> </p> <p><strong>Methods</strong></p> <p>The BAM files under http://fantom.gsc.riken.jp/5/datafiles/reprocessed/hg38_v4/basic/ were subjected to 5'-end counting by bedtools v2.27.1 (https://github.com/arq5x/bedtools2), followed by conversion into bigWig with jksrc v357 (http://hgdownload.cse.ucsc.edu/admin/).</p> <p> </p>
Aligned ISNI and Ringgold identifiers for institutions
<p>This dataset provides a correspondence between ISNI and Ringgold identifiers, by combining two datasets:</p> <ol> <li>Open ISNI for Institutions, available at http://isni.ringgold.com/ , which provides metadata for institutions identified by ISNI.</li> <li>The dataset of institutions used by ORCID for disambiguation, which also comes from Ringgold, but exposes Ringgold ids.</li> </ol> <p>The alignment between the two datasets was performed by exact matching on tuples of (name,city,region,country). This very conservative matching succeeds for 316,870 of the 499,658 institutions covered (as of June 2019, when the extraction was performed): this relatively high matching rate can be explained by the fact that both datasets come from the same database. Matching fails in other cases because of metadata differences which might be introduced by ORCID's own workflow.</p> <p><strong>Structure of the dataset</strong></p> <ul> <li>ringgold_ids.tsv.bz2 provides all the Ringgold ids we could extract from ORCID, with their metadata (locality, region, country, type);</li> <li>aligned_ringgold_and_isni.tsv.bz2 provides the aligned dataset, containing all ISNI records and all matching Ringgold ids. The Ringgold ids that were not matched are not included in this file.</li> </ul> <p><strong>License</strong></p> <p>Quoting Ringgold: "The use of the ISNI data contained herein is completely open and you may utilise and share the ISNI identifiers as you see fit."</p> <p>Quoting ORCID: "Per our agreement with Ringgold, we are allowed to share the Ringgold identifiers and limited metadata (organization name, location) under CC0 license, just as the rest of ORCID data are available. We would not be using Ringgold otherwise. If someone gets a Ringgold ID out of ORCID, they are free to use it."</p>
DNA sequences alignements for 27 species of ticks (SCO50 matrix)
<p>The two files contain respectively the concatenation of DNA sequences alignements for 27 species of ticks (SCO50 matrix, n=952 genes) and to the partition file indicating the positions of each gene in the concatenation.</p> <p>The data set corresponds to the article "A transcriptome-based phylogenetic study of hard ticks (Ixodidae)" to be published in Scientific Reports, by N Pierre Charrier, Axelle Hermouet, Caroline Hervet, Albert Agoulon,<br> Stephen Barker, Dieter Heylen, Céline Toty, Karen McCoy, Olivier Plantard, Claude Rispe.</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.