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ShareScore release 0.7.1
Dataset results
72 results for “Collective Experiments”
FIG. 10 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 10. — Adding text to an image.
FIG. 4 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 4. — Example of angle measurements on an orchid seed from the genus Phaius.
FIG. 3 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 3. — Example of measurements: surface and perimeters.
FIG. 8 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 8. — Transcription of a written area into a plate.
FIG. 14 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 14. — One of the three modes of importing images,drag and drop from a computer hard drive.
FIG. 5 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 5. — Example of organ counts:number of rays in an umbel.
FIG. 17 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 17. — Data export.
FIG. 7 in Récolnat Annotate-On: a tool to improve your experience with virtual collections
FIG. 7. — Colour tool.
Moca: An efficient Memory trace collection system, preliminary experiments results analysis
<p>Every files required to replay the statistic analysis of the preliminary experiments for the artice: "Moca: An efficient Memory trace collection system" submitted at HPDC</p>
Moca: An efficient Memory trace collection system, experiments raw traces
<p>Raw traces generated for preliminary experiment of the article "Moca: An efficient Memory trace collection system" submitted at HPDC'16.</p> <p> </p> <p>Download and extract the raw.tgz archive, then go to the hpdc directory, download and extract all the other archives inside it.</p> <p> </p> <p><strong>Warning:</strong> there are about 80 Gib of raw traces</p>
Moca: An efficient Memory trace collection system, experiments raw traces
<p>Raw traces generated for by the experiment of the article "Moca: An efficient Memory trace collection system" submitted at PMBS'16.</p> <p>Download and extract the raw.tgz archive, then go to the created directory, download and extract all the other archives inside it.</p> <p><strong>Warning:</strong> there are about 90 Gib of raw traces</p>
Moca: An efficient Memory trace collection system, experiments results analysis
<p>Every files required to replay the statistic analysis of the experiments presented in the artice: "Moca: An efficient Memory trace collection system" submitted at PMBS'16</p>
Data for collected samples and mating experiment of Acilius Japonicus
<p>Previous studies have predicted that antagonistic intraspecific evolution of sexually dimorphic characters causing rapid speciation can be driven by demographic history and environmental variations. However, researchers have rarely examined this issue in the wild. Here, we examined intraspecific evolution of sexually dimorphic characters and its driving force by using a diving beetle, <em>Acilius japonicus</em>, which has very marked sexually dimorphic characters. Males with wider big suction cups could copulate with females with a higher success rate, whereas the mating durations of females with more hairs on their pronota were shorter. Females in a region with greater interpopulation genetic differentiation had more pronotal hairs. Considering that a previous study showed that less continuity among populations leads to a higher female cost of mating, this result suggests a greater female cost of mating in this region. Females at warmer sites also had more pronotal hairs. In light of the increase in O<sub>2</sub> consumption in warmer water, our result suggests that more pronotal hairs in females at warmer sites have been maintained to prevent prolonged underwater mating at higher O<sub>2</sub> demand. These findings suggest that demographic history and temperature can direct the evolution of sexually dimorphic characters related to sexual conflict in females.</p>
utility: Collection of Tumor-Infiltrating Lymphocyte Single-Cell Experiments with TCR
<p><strong>Introduction</strong></p> <p>The original intent of assembling a data set of publicly-available tumor-infiltrating T cells (TILs) with paired TCR sequencing was to expand and improve the <a href="https://github.com/ncborcherding/scRepertoire">scRepertoire</a> R package. However, after some discussion, we decided to release the data set for everyone, a complete summary of the sequencing runs and the sample information can be found in the meta data of the Seurat object. This repository is the 4th version of the data, with addition of cells and changes to the workflow. </p> <p><strong>Methods</strong></p> <p><em>Single-Cell Data Processing</em></p> <p>The filtered gene matrices output from Cell Ranger align function from individual sequencing runs (10x Genomics, Pleasanton, CA) loaded into the R global environment. For each sequencing run cell barcodes were appended to contain a unique prefix to prevent issues with duplicate barcodes. The results were then ported into individual Seurat objects (<a href="https://pubmed.ncbi.nlm.nih.gov/34062119/">citation</a>), where the cells with > 10% mitochondrial genes and/or 2.5x natural log distribution of counts were excluded for quality control purposes. At the individual sequencing run level, doublets were estimated using the scDblFinder (v1.4.0) R package.</p> <p><em>Annotation of Cells</em></p> <p>Automatic annotation was performed using the singler (v1.4.1) R package (<a href="https://pubmed.ncbi.nlm.nih.gov/30643263/">citation</a>) with the HPCA (<a href="https://pubmed.ncbi.nlm.nih.gov/24053356/">citation</a>) and Monaco (<a href="https://pubmed.ncbi.nlm.nih.gov/30726743/">citation</a>) data sets as references and the fine label discriminators. Individual sequencing runs were subsetted to run through the singleR algorithm in order to reduce memory demands. The output of all the singleR analyses were collated and appended to the meta data of the seurat object. Likewise, the ProjecTILs (v0.4.1) R Package (<a href="https://pubmed.ncbi.nlm.nih.gov/34017005/">citation</a>) was used for automatic annotation as a partially orthogonal approach. </p> <p><em>Addition of TCR data</em></p> <p>The filtered contig annotation T cell receptor (TCR) data for available sequencing runs were loaded into the R global environment. Individual contigs were combined using the combineTCR() function of scRepertoire (v1.3.5) R Package (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400693/">citation</a>). Clonotypes were assigned to barcodes and were multiple duplicate chains for individual cells were filtered to select for the top expressing contig by read count. The clonotype data was then added to the Seurat Object with proportion across individual patients being used to calculate frequency.</p> <p><strong>Citations</strong></p> <p>As of right now, there is no citation associated with the assembled data set. However if using the data, please find the corresponding manuscript for each data set in the meta.data of the single-cell object. In addition, if using the processed data, feel free to modify the language in the methods section (above) and please cite the appropriate manuscripts of the software or references that were used.</p> <p><em>Itemized List of the Software Used</em></p> <ul> <li>Seurat v4.0.3 - <a href="https://pubmed.ncbi.nlm.nih.gov/34062119/">citation</a></li> <li>harmony v1.0 - <a href="https://pubmed.ncbi.nlm.nih.gov/31740819/">citation</a></li> <li>singler v1.4.1 - <a href="https://pubmed.ncbi.nlm.nih.gov/30643263/">citation</a></li> <li>ProjecTILs v2.0.3 - <a href="https://pubmed.ncbi.nlm.nih.gov/34017005/">citation</a></li> <li>UCell v1.0.0 - <a href="https://www.biorxiv.org/content/10.1101/2021.04.13.439670v1">citation</a></li> <li>scRepertoire v1.3.5 - <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400693/">citation</a></li> </ul> <p><em>Itemized List of Reference Data Used</em></p> <ul> <li>Human Primary Cell Atlas (HPCA) - <a href="https://pubmed.ncbi.nlm.nih.gov/24053356/">citation</a></li> <li>Monaco Data Set - <a href="https://pubmed.ncbi.nlm.nih.gov/30726743/">citation</a></li> </ul> <p><strong>Future Directions</strong></p> <ul> <li>Data Hosting for Interactive Analysis</li> <li>Easy Submission Portal for Researchers to Add Data</li> <li>Using the Data to Build a Reference Atlas</li> </ul> <p>There are areas in which we are actively hoping to develop to further facilitate the usage of the data set - if you have other suggestions, please reach out using the contact information below.</p> <p><strong>Contact</strong></p> <p>Questions, comments, and suggestions, please feel free to contact Nick Borcherding via this repository, <a href="mailto:ncborch@gmail.com">email</a>, or using <a href="https://twitter.com/theHumanBorch">twitter</a>.</p>
The Painful Real-life Experience of the Child of Less Than Three Years During the Removal of the Collecting Bags in the Pediatric Urgency: What Strategy of Coverage?
ClinicalTrials.gov study NCT01659190. IPD Sharing: Not stated. Countries: 1. Publications: 1.
This Study Will Collect Clinical and Patient Reported Experience Data From Participants Requiring Urine Output Management Overnight in the Home Setting.
ClinicalTrials.gov study NCT06666426. IPD Sharing: NO. Countries: 1. Publications: 1.
Data for collected samples and mating experiment of Acilius Japonicus
Open the record for dataset details and reuse information.
"This was on this huge market Kikar Shabat in Jerusalem. Iwas on holiday in Israel during a Jewish Festival called Sukkot. I live in England and I'm a Jew. Iwas in the market to buy lulav and estrog. It was a huge traditional festive fair opened a week after Sukkot. It was my first trip to Israel since Iwas a teenager. There was bustle, a lot of Jews. Irecorded this sound, because Iwanted to have a souvenir, to keep this experience of this bustle and hubbub". [Mike/Sparrer]11 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"This was on this huge market Kikar Shabat in Jerusalem. Iwas on holiday in Israel during a Jewish Festival called Sukkot. I live in England and I'm a Jew. Iwas in the market to buy lulav and estrog. It was a huge traditional festive fair opened a week after Sukkot. It was my first trip to Israel since Iwas a teenager. There was bustle, a lot of Jews. Irecorded this sound, because Iwanted to have a souvenir, to keep this experience of this bustle and hubbub". [Mike/Sparrer]11
A Study to Collect Participants Experience of Living With Adult Upper Limb (AUL) Spasticity and to Assess the Arm Activity Measure (ArmA)
ClinicalTrials.gov study NCT05546190. IPD Sharing: YES. Countries: 1. Publications: 0.
Non-Interventional Study (NIS) Collecting Experiences For IPF in Taiwan
ClinicalTrials.gov study NCT03242759. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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.