Dataset Card for escher-human-edit

Human Edit dataset

  Dataset Structure





  Data Instances

Each instance contains:

source_image: The original image edited_image: The edited version of the image edit_instruction: The instruction used to edit the image source_image_caption: Caption for the source image target_image_caption: Caption for the edited image Additional metadata fields

  Data Splits

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The WikiPrefs dataset is a human preferences dataset for Large Language Models alignment. It was built using the EditPrefs method from historical edits of Wikipedia featured articles

WikiAtomicEdits is a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence.

Human post-edited test sentences for the WMT 2017 Automatic post-editing task. This consists in 2,000 German sentences belonging to the IT domain and already tokenized. Source and target segments can be downloaded from: https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-2133. All data is provided by the EU project QT21 (http://www.qt21.eu/).

NoHumanRequired (NHR) Dataset for image editing

🌐 NHR Website | 📜 NHR Paper on arXiv | 💻 GitHub Repository | 🤗 BAGEL-NHR-Edit |

NHR-Edit is a training dataset for instruction-based image editing. Each sample consists of an input image, a natural language editing instruction, and the corresponding edited image. All samples are generated fully automatically using the NoHumanRequired pipeline, without any human annotation or filtering. This dataset is… See the full description on the dataset page: https://huggingface.co/datasets/iitolstykh/NHR-Edit.

Dataset Card for HQ-EDIT

HQ-Edit, a high-quality instruction-based image editing dataset with total 197,350 edits. Unlike prior approaches relying on attribute guidance or human feedback on building datasets, we devise a scalable data collection pipeline leveraging advanced foundation models, namely GPT-4V and DALL-E 3. HQ-Edit’s high-resolution images, rich in detail and accompanied by comprehensive editing prompts, substantially enhance the capabilities of existing image editing… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/HQ-Edit-data-demo.

Human post-edited test sentences for the WMT 2017 Automatic post-editing task. This consists in 2,000 English sentences belonging to the IT domain and already tokenized. Source and target segments can be downloaded from: https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-2132. All data is provided by the EU project QT21 (http://www.qt21.eu/).

This page hosts downloadable data related to RE-Aging: A Functional Analysis Platform for Human RNA Editing Associated with Aging.

AIdata.zip: Contains detailed information on all A-to-I RNA editing sites.
CUdata.zip: Includes comprehensive data on all C-to-U RNA editing sites.
data_all.zip: Provides a complete dataset of all RNA editing sites across both A-to-I and C-to-U types.
cor.zip: Contains information on the relationship between editing levels of A-to-I sites in various organs and age.
sample_info.zip: Includes the corresponding GTEx Sample Information, essential for contextualizing the data.

The Gene-Editing Tools For Non-Human Primates Market report segments the industry into By Technology (CRISPR/Cas9, Transcription Activator-Like Effectror Nucleases (TALENs), Zinc Finger Nucleases (ZFNs), Others), By Application (Biomedical Research, Transgenic Model Development, Pharmaceutical Development, Gene Therapy Research), By End User (Research Institutions, and more), and Geography.

aGenomic position is the position in human genomic database from UCSC (http://genome.ucsc.edu, hg18 version, March 2006 assembly).bFrequency of RNA editing is presented as the percentage of the total population of transcripts.cReads is the number of transcripts sequenced.d11 new RNA editing sites identified by Li et al [24].

The human evaluation (HE) dataset created for English to German (EnDe) and English to French (EnFr) MT tasks was a subset of one of the official test sets of the IWSLT 2016 evaluation campaign. The resulting HE sets are composed of 600 segments for both EnDe and EnFr, each corresponding to around 10,000 words. Human evaluation was based on Post-Editing, i.e. the manual correction of the MT system output, which was carried out by professional translators. Nine and five primary runs submitted to the evaluation campaign were post-edited for the two tasks, respectively.

Data are publicly available through the WIT3 website wit3.fbk.eu. 600 segments for both EnDe and EnFr (10K tokens each). Respectively, 9 and 5 different automatic translations post-edited by professional translators (for Analysis of MT quality and Quality Estimation components).

Human post-edited and reference test sentences for the En-De PBSMT WMT 2018 Automatic post-editing task. This consists of 2,000 German sentences for each file belonging to the IT domain and already tokenized. All data is provided by the EU project QT21 (http://www.qt21.eu/).

The lower the rank of an edge, and the higher its support, the stronger is the dependency between the pair of editing sites (see text for details). The third column is the same as the second column, except that either A or B are marked as F. The rightmost column lists the models in which the given edge appears.

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The serotonin 2C receptor (5-HT2CR)–a key regulator of diverse neurological processes–exhibits functional variability derived from editing of its pre-mRNA by site-specific adenosine deamination (A-to-I pre-mRNA editing) in five distinct sites. Here we describe a statistical technique that was developed for analysis of the dependencies among the editing states of the five sites. The statistical significance of the observed correlations was estimated by comparing editing patterns in multiple individuals. For both human and rat 5-HT2CR, the editing states of the physically proximal sites A and B were found to be strongly dependent. In contrast, the editing states of sites C and D, which are also physically close, seem not to be directly dependent but instead are linked through the dependencies on sites A and B, respectively. We observed pronounced differences between the editing patterns in humans and rats: in humans site A is the key determinant of the editing state of the other sites, whereas in rats this role belongs to site B. The structure of the dependencies among the editing sites is notably simpler in rats than it is in humans implying more complex regulation of 5-HT2CR editing and, by inference, function in the human brain. Thus, exhaustive statistical analysis of the 5-HT2CR editing patterns indicates that the editing state of sites A and B is the primary determinant of the editing states of the other three sites, and hence the overall editing pattern. Taken together, these findings allow us to propose a mechanistic model of concerted action of ADAR1 and ADAR2 in 5-HT2CR editing. Statistical approach developed here can be applied to other cases of interdependencies among modification sites in RNA and proteins.

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… See the full description on the dataset page: https://huggingface.co/datasets/AILab-CVC/SEED-Data-Edit-Part1-Unsplash.

The Gene-Editing Tools For Non-Human Primates report features an extensive regional analysis, identifying market penetration levels across major geographic areas. It highlights regional growth trends and opportunities, allowing businesses to tailor their market entry strategies and maximize growth in specific regions.

Rapid advances in genome editing, including CRISPR-Cas9 endonucleases, and their potential application in medicine and enhancement have been hotly debated by scientists and ethicists. Although a veto on germ line gene editing has been proposed¹, the use of gene editing on human cells in the clinical context remains controversial, particularly for interventions aimed at enhancement². In a report on human genome editing the US National Academy of Sciences (NAS) note that “important questions raised with respect to genome editing include how to incorporate societal values into salient clinical and policy consideration”³. We report here our research that opens a window onto what the public think.

As per Cognitive Market Research's latest published report, the Global Gene Editing Service market size was $6.21 Billion in 2022 and it is forecasted to reach $18.77 Billion by 2030. Gene Editing Service Industry's Compound Annual Growth Rate will be 14.9% from 2023 to 2030. Factors Impacting on Gene Editing Service Market

The rising demand for gene therapy drives the Gene Editing Service Market growth

Gene therapy has marked its significant importance in the field of medication over the last few decades. Gene therapy is used for the treatment associated with the genetic disorder. The data from the National Human Genome Research Institute (2018) states that approx. 350 million people across the globe are living with rare disorders and fewer than 200,000 people are diagnosed with this condition. About 80 % of these rare disorders are genetic in origin. With technological advancement gene therapy has grown as a most considered option for the treatment and control of several life-threatening diseases. such as hemophilia. The data from US Centers for Disease Control and Prevention states the presence of around 30,000 – 33,000 people with hemophilia in the US. This raises the demand for the gene editing services market.

Challenges for the Gene Editing Service Market

High expenses related to gene editing can hamper the growth of the gene editing service market growth. (Access Detailed Analysis in the Full Report Version)

Rising R&D activities will boost the Gene Editing Service market growth

Gene editing is being explored in a varied array of diseases, including single-gene rare disorders such as sickle cell disease and hemophilia. The number of venture capital (VC) agreements for firms exploring gene editing technology has surged dramatically since 2012. According to GlobalData's Pharma Intelligence Center, the number of VC agreements climbed from one in 2012 to 29 in 2021, with the total value of VC deals reaching more than $3.2 billion since 2012. Over $1.3 billion was raised in 2021 alone, more than 250% higher than in 2020 ($500 million). This investment is expected to propel the growth of the market. What is Gene Editing?

Gene editing is also called genome editing. It is a group of technologies that permit researchers to make a change in the DNA of organisms. Currently, there are several approaches are being developed for gene editing. One of the popular gene editing technologies is the CRISPR-Cas9 system. These technologies enable the addition, elimination, or alteration of genetic information at precise locations in the genome.

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Genome Editing Market Size 2024-2028

The genome editing market size is forecast to increase by USD 7.23 billion at a CAGR of 15.88% between 2023 and 2028. The genomic editing landscape is experiencing rapid advancements, driven by the development of innovative technologies such as CRISPR-Cas9 and base editing. These tools enable precise modifications to DNA sequences, opening new possibilities for precision medicine. The demand for customized healthcare solutions is surging, as individuals seek treatments tailored to their unique genetic makeup. Additionally, the emergence of novel gene editing platforms, like prime editing and base editing, is expanding the scope of genomic research and therapy development. These advancements are revolutionizing the healthcare industry, offering potential cures for genetic diseases and paving the way for a more personalized and effective approach to patient care.

What will be the size of the market during the forecast period?

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Genome Editing Market Segmentation

The genome editing market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.

End-user

  Pharmaceutical and biotechnology companies
  Academic institutes and research laboratories
  CRO


Delivery Mode

  Ex-vivo
  In-vivo


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  Asia

    China


  Rest of World (ROW)

Which is the largest segment driving market growth?

The pharmaceutical and biotechnology companies segment is estimated to witness significant growth during the forecast period.

Genome editing is a revolutionary molecular biology technique that allows for precise modification of single genes within an organism's genome. This technology has gained significant attention in the scientific community due to its potential applications in the investigation and treatment of genetic abnormalities and various human diseases. Genome editing tools, such as base editing and prime editing, enable genetic manipulation and gene editing services, providing researchers with the ability to create knock-out or knock-in mutations in target genes. These modifications can elucidate gene function, signaling pathways, and mechanisms of drug resistance, contributing to a deeper understanding of disease mechanisms and therapeutic targets.

Pharmaceutical companies are increasingly utilizing genome-edited cell lines and animal models to study disease progression and screen potential drug candidates. For instance, genome editing and CRISPR technology have shown promise in the development of therapeutics for genetic disorders like sickle cell disease, Parkinson's disease, hearing loss, peripheral artery disease, and spinal muscular atrophy, as well as autoimmune diseases. In the agricultural sector, genome editing is used for animal breeding and in plant biotechnology for crop improvement. Clinical trials for gene therapy are underway for several human genetic diseases, with the in-vivo segment being a significant focus. Cell line engineering and biotechnology companies are at the forefront of gene-editing technology development, driving innovation in gene delivery, drug discovery, and large-molecule medicines.

This technology holds immense potential for the treatment of genetic diseases, including AIDS, cancer, and various other conditions. The clinical trial pipeline for gene editing-based therapeutics continues to expand, offering hope for those affected by these debilitating diseases.

Get a glance at the market share of various regions. Download the PDF Sample

The Pharmaceutical and biotechnology companies segment was valued at USD 1.86 billion in 2018 and showed a gradual increase during the forecast period.

Which region is leading the market?

North America is estimated to contribute 40% to the growth of the global market during the forecast period.

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Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.In North America, the biopharmaceutical and biotechnology sector is experiencing significant growth, encompassing pharmaceutical companies, biotech startups, Contract Research Organizations (CROs), and academic research institutions. These entities employ genome editing technologies for various applications, including drug discovery, target validation, preclinical research, and therapeutic development. The region's focus on personalized medicine and precision therapeutics is fueled by advancements in genomics, molecular diagnostics, and targeted therapies. Genome editing tools enable the precise modification of genes linked to diseases, paving the way for pe