4 datasets found
  1. h

    SEED-Data-Edit-Part2-3

    • huggingface.co
    Updated Mar 12, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    TencentAILab-CVC (2025). SEED-Data-Edit-Part2-3 [Dataset]. https://huggingface.co/datasets/AILab-CVC/SEED-Data-Edit-Part2-3
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 12, 2025
    Dataset authored and provided by
    TencentAILab-CVC
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Description

    SEED-Data-Edit

    SEED-Data-Edit is a hybrid dataset for instruction-guided image editing with a total of 3.7 image editing pairs, which comprises three distinct types of data: Part-1: Large-scale high-quality editing data produced by automated pipelines (3.5M editing pairs). Part-2: Real-world scenario data collected from the internet (52K editing pairs). Part-3: High-precision multi-turn editing data annotated by humans (95K editing pairs, 21K multi-turn rounds with a maximum of 5… See the full description on the dataset page: https://huggingface.co/datasets/AILab-CVC/SEED-Data-Edit-Part2-3.

  2. f

    Data_Sheet_1_Predicting Public Attitudes Toward Gene Editing of Germlines:...

    • frontiersin.figshare.com
    pdf
    Updated May 31, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Christine Critchley; Dianne Nicol; Gordana Bruce; Jarrod Walshe; Tamara Treleaven; Bernard Tuch (2023). Data_Sheet_1_Predicting Public Attitudes Toward Gene Editing of Germlines: The Impact of Moral and Hereditary Concern in Human and Animal Applications.PDF [Dataset]. http://doi.org/10.3389/fgene.2018.00704.s001
    Explore at:
    pdfAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Frontiers
    Authors
    Christine Critchley; Dianne Nicol; Gordana Bruce; Jarrod Walshe; Tamara Treleaven; Bernard Tuch
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Background and Objective: New and more efficient methods of gene editing have intensified the ethical and legal issues associated with editing germlines. Yet no research has separated the impact of hereditary concern on public attitudes from moral concern. This research compares the impact these two concerns have on public attitudes across five applications including, the prevention of human disease, human and animal research, animals for the use of human food and the enhancement of human appearance.Methods: A sample of 1004 Australians responded to either a telephone (n = 501; randomly selected) or online survey (n = 503; sourced by Qualtrics). Both samples were representative in terms of States and Territories as well as gender (51% female), though the online sample was younger (M = 40.64, SD = 16.98; Range = 18–87) than the telephone sample (M = 54.79, SD = 18.13; Range = 18–96). A 5 (application) by 3 (type of cell) within groups design was utilized, where all respondents reported their level of approval with scientists editing genes across the 15 different contexts. Multilevel modeling was used to examine the impact of moral (embryo vs. germ) and hereditary (germ vs. somatic) concern on attitudes across all applications.Results: Australians were comfortable with editing human and animal embryos, but only for research purposes and to enhance human health. The effect of moral concern was stronger than hereditary concern, existing in all applications except for the use of animals for human purposes. Hereditary concern was only found to influence attitudes in two applications: improving human health and human research. Moral concern was found to be accentuated amongst, women, more religious individuals and those identifying as Australian, while hereditary concern was strongest amongst non-Australians, those with stronger trust in scientists, and more religious respondents.Conclusion: Moral and hereditary concerns are distinct, and require different approaches to public education, engagement and possibly regulation. Further research needs to explore hereditary concern in relation to non-human applications, and the reasons underlying cultural and gender differences.

  3. f

    MiREDiBase Dataset - Version 1

    • figshare.com
    xlsx
    Updated May 24, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Gioacchino Paolo Marceca; Rosario Distefano; Giovanni Nigita; Carlo M Croce (2021). MiREDiBase Dataset - Version 1 [Dataset]. http://doi.org/10.6084/m9.figshare.14666466.v1
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    May 24, 2021
    Dataset provided by
    figshare
    Authors
    Gioacchino Paolo Marceca; Rosario Distefano; Giovanni Nigita; Carlo M Croce
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    List of validated and putative A-to-I and C-toU miRNA editing sites in Human, Macaque, Gorilla and Chimpazee

  4. f

    Improved Genome Editing in Human Cell Lines Using the CRISPR Method

    • plos.figshare.com
    tiff
    Updated May 31, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Ivan M. Munoz; Piotr Szyniarowski; Rachel Toth; John Rouse; Christophe Lachaud (2023). Improved Genome Editing in Human Cell Lines Using the CRISPR Method [Dataset]. http://doi.org/10.1371/journal.pone.0109752
    Explore at:
    tiffAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Ivan M. Munoz; Piotr Szyniarowski; Rachel Toth; John Rouse; Christophe Lachaud
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The Cas9/CRISPR system has become a popular choice for genome editing. In this system, binding of a single guide (sg) RNA to a cognate genomic sequence enables the Cas9 nuclease to induce a double-strand break at that locus. This break is next repaired by an error-prone mechanism, leading to mutation and gene disruption. In this study we describe a range of refinements of the method, including stable cell lines expressing Cas9, and a PCR based protocol for the generation of the sgRNA. We also describe a simple methodology that allows both elimination of Cas9 from cells after gene disruption and re-introduction of the disrupted gene. This advance enables easy assessment of the off target effects associated with gene disruption, as well as phenotype-based structure-function analysis. In our study, we used the Fan1 DNA repair gene as control in these experiments. Cas9/CRISPR-mediated Fan1 disruption occurred at frequencies of around 29%, and resulted in the anticipated spectrum of genotoxin hypersensitivity, which was rescued by re-introduction of Fan1.

  5. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
TencentAILab-CVC (2025). SEED-Data-Edit-Part2-3 [Dataset]. https://huggingface.co/datasets/AILab-CVC/SEED-Data-Edit-Part2-3

SEED-Data-Edit-Part2-3

AILab-CVC/SEED-Data-Edit-Part2-3

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Mar 12, 2025
Dataset authored and provided by
TencentAILab-CVC
License

Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
License information was derived automatically

Description

SEED-Data-Edit

SEED-Data-Edit is a hybrid dataset for instruction-guided image editing with a total of 3.7 image editing pairs, which comprises three distinct types of data: Part-1: Large-scale high-quality editing data produced by automated pipelines (3.5M editing pairs). Part-2: Real-world scenario data collected from the internet (52K editing pairs). Part-3: High-precision multi-turn editing data annotated by humans (95K editing pairs, 21K multi-turn rounds with a maximum of 5… See the full description on the dataset page: https://huggingface.co/datasets/AILab-CVC/SEED-Data-Edit-Part2-3.

Search
Clear search
Close search
Google apps
Main menu