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

{}

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.

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.

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/).

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/).

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).

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

This update to the Human Footprint (HFP) provides a measure of the direct and indirect human pressures on the environment globally in years 2000, 2005, 2010, and 2013. Per the orinal Human Footprint, this dataset is derived from remotely-sensed and bottom-up survey information compiled on eight measured variables. This represents not only the most current information of its type, but also the first temporally-consistent set of Human Footprint maps. Data on human pressures were acquired or developed for: 1) built environments, 2) population density, 3) electric infrastructure, 4) crop lands, 5) pasture lands, 6) roads, 7) railways, and 8) navigable waterways. This update incorporates updated and higher resolution population, nightlights, pasture, road, and railway input datasets. The Human Footprint maps find a range of uses as proxies for human disturbance of natural systems and can provide an increased understanding of the human pressures that drive macro-ecological patterns, as well as for tracking environmental change and informing conservation science and application. HFP values range from 0 (no human impact) to 50 (heavily human impacted).

See: Venter, O. et al., 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications, 7, pp.1–11.

This dataset can be downloaded uniquly from UN Biodiversity Lab.
Updated data is made available only to FIP pilot countires at present - rasters are clipped to other FIP data extents.

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.

This dataset contains modeled temperature, ozone, and PM2.5 data for the United States over the 21st century, using two global climate model scenarios and two emissions datasets.

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.

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.

Raw data for ddPCR. Raw images.

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/).

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.

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/).

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.

This Special Issue of the Journal of South Pacific Law aims to provide insight into the role of international law in addressing the short-term and long-term challenges posed by climate change to Pacific Island States and their populations. It focuses on the two international legal frameworks that were designed to protect the Earth’s climate system and the human person: international climate change law on the one hand, and international human rights law on the other.

We developed datasets on the human modification of global terrestrial ecosystems for 2022. The methods and data sources associated with these data are fully described in:

Theobald, D.M., Oakleaf, J.R., Moncrieff, G., Voigt, M., Kiesecker, J., and Kennedy, C.M.

For 2022, raster datasets are provided in cloud-optimized GeoTIFF format at 300 m resolution (EPSG:4326). The naming convention is as follows: HMv2024080101_

Note that these data are available as Google Earth Engine assets via this script (including 90 m): https://code.earthengine.google.com/1b7b5976fdd6189c6533ca00a46386d1

The Google Earth Engine script to clip out custom extents and export to GeoTIFF is here: https://code.earthengine.google.com/44c9f092472edb9bac3c45096aa5091d

Please see companion repo here for datasets for 1990-2020: https://zenodo.org/uploads/14449495.

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].