Dataset Card for escher-human-edit

Human Edit dataset

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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

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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.

Relative edit time data from Chapter 6 "Paper-based semantic speech editing" from the PhD thesis "Semantic Audio Tools for Radio Production" by Chris Baume.

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.

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 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.

These are the Cumulative Impact data for publication: 'Recent pace of change in human impact on the world's ocean' (Halpern et al. 2019). The goal of this project was to describe the patterns and pace of change in cumulative impact on ocean ecosystems due to expanding and increasing intensity of human activities. We combined high resolution, annual data on the intensity of 14 human stressors and their impact on 21 marine ecosystems over 11 years (2003-2013). To determine average annual change in cumulative impacts, we applied a linear regression model to each raster cell. These data include: Cumulative impacts rasters for 2003 to 2013. These rasters are created using scripts located in the 'impacts' folder of the 'impact_acceleration' Github repository. Average annual change in cumulative impact raster from 2003 to 2013 (i.e., trend). This raster is created using scripts located in the 'trend' folder of the 'impact_acceleration' Github repository. Ecosystem rasters describing the location (1 if present, otherwise NA) of 21 global marine ecosystems. These rasters are the same as used in previous years, with the exception of seaice. The seaice raster was created using a script located in the 'habitats' folder of the 'impact_acceleration' Github repository. A vulnerability matrix describing the vulnerability of each ecosystem to each stressor, with values ranging from 0-4. The impact_acceleration-1.0.zip GitHub repository with the code used to generate, analyze, and visualize data. Data for the individual 14 stressors comprising the cumulative impact are provided in other KNB repositories.

This graph shows the forecast of *** executives of UK-based companies and *** executives of companies based in Germany, when asked how would your company's production and/or human resources capacity change in the UK within three years of a Brexit? The most common assessment from a business perspective was that their respective company would maintain capacity and/or remain unchanged.

Around **** percent of people interviewed in ** European countries stated that they think climate change is caused mainly by human activities. During the same interview, **** percent agreed that such change was caused about equally by natural and human processes. When it comes to specific examples, Sweden got the highest number of respondents, with more than half believing that the current climate change is being caused mainly by humans.

Because of the pivotal role of mitochondrial alterations in several diseases, the Human Proteome Organization (HUPO) has promoted in recent years an initiative to characterize the mitochondrial human proteome, the mitochondrial human proteome project (mt-HPP). Here we generated an updated version of the functional mitochondrial human proteome network, made by nodes (mitochondrial proteins) and edges (gold binary interactions), using data retrieved from neXtProt, the reference database for HPP metrics. The principal new concept suggested was the consideration of mitochondria-associated proteins (first interactors), which may influence mitochondrial functions. All of the proteins described as mitochondrial in the sublocation or the GO Cellular Component sections of neXtProt were considered. Their other subcellular and submitochondrial localizations have been analyzed. The network represents the effort to collect all of the high-quality binary interactions described so far for mitochondrial proteins and the possibility for the community to reuse the information collected. As a proof of principle, we mapped proteins with no function, to speculate on their role by the background knowledge of their interactors, and proteins described to be involved in Parkinson’s Disease, a neurodegenerative disorder, where it is known that mitochondria play a central role.

Data on the extent, patterns, and trends of human land use are critically important to support global and national priorities for conservation and sustainable development. To inform these issues, we created a series of detailed global datasets for 1990, 1995, 2000, 2005, 2010, 2015, and 2017 to evaluate temporal changes and spatial patterns of land use modification of terrestrial lands (excluding Antarctica). These data were calculated using the degree of human modification approach that combines the proportion of a pixel of a given stressor (i.e. footprint) times the intensity of that stressor (ranging from 0 to 1.0). Our novel datasets are detailed (0.09 km^2 resolution), temporally consistent (for 1990-2015, every 5 years), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. We also provide a dataset that represents ~2017 conditions and has 14 stressors for an even more comprehensive dataset, but the 2017 results should not be used to calculate change with the other datasets (1990-2015). Note that because of repo file size limits, the datasets for the for the HM overall for 1990 and 1995, as well as major stressors for all years, are located this Google Drive.

This version 1.5 provides the following updates:

Datasets are provided for each of the 6 stressor groups: built-up areas (BU), agricultural/timber harvest (AG), extractive energy and mining (EX), human intrusions (HI), natural system modifications (NS), and transportation & infrastructure (TI), available now at 300 m resolution for each of the time steps in the 1990-2015 time series.

It provides the addition datasets for the years 1995 and 2005, calculated using linear interpolation when stressor data do not provide data at the specific year.

The ESA 150 m water-mask dataset (Lamarche et al. 2017) was used to provide better and more consistent alignment of datasets at the ocean-land-inland water interfaces.

The built-up stressor uses an updated version of the Global Human Settlement Layer (v2022A).

Values provided are 32-bit floating point values, with human modification values ranging from 0.0 to 1.0.

For more details on the approach and methods, please see: Theobald, D. M., Kennedy, C., Chen, B., Oakleaf, J., Baruch-Mordo, S., and Kiesecker, J.: Earth transformed: detailed mapping of global human modification from 1990 to 2017, Earth Syst. Sci. Data., https://doi.org/10.5194/essd-2019-252, 2020.

Version 1.5 was completed in collaboration with the Center for Biodiversity and Global Change at Yale University and supported by the E.O. Wilson Biodiversity Foundation.

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.

The buffer zones of 4 km of the human settlements in Guoluo Prefecture and Yushu Prefecture are analyzed. Settlements are categorized based on whether their locations are close to main roads, temples, or water bodies of lakes or rivers.Number of human settlements found with land use change in Guoluo and Yushu.

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.

No description is available. Visit https://dataone.org/datasets/doi%3A10.5063%2FF1XG9PGM for complete metadata about this dataset.

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/).

Maps depicting the intensity of human pressure on the environment have become a critical tool for spatial planning and management, monitoring the extent of human influence across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. Here we provide an updated Human Footprint methodology to run on an annual basis to monitor changing anthropogenic pressures. Software and methods are parameterized to enable regular updates in the future. In addition, we release a 100-meter global dataset for the years 2015–2019 and 2020 based on land use, population, infrastructure, and accessibility data. Results show high levels of agreement in validation against expert-interpreted satellite imagery and improved performance compared to previous iterations of similar datasets. These maps are directly relevant to measuring progress towards national and international targets related to biodiversity conservation and sustainable development. Methods This dataset was created by combining data on human pressures across the period 2015 to 2019 and for 2020 to map: 1) Land cover change (built environments, crop lands, and pasture lands), 2) population density, 3) electric infrastructure, 4) roadways, 5) railways, and 6) navigable waterways. Each pressure layer is assigned a score relative to its level of human pressure, then computed into a standardized scale of 0–50 as the sum of all pressure layers. Pressures are not mutually exclusive, rather the co-occurrence of pressures is intended to identify the greatest levels of human impact. The majority of layers cover the complete time period of 2015–2020, however, pressures from pasture, roads, and railways are treated as static in the Human Footprint maps due to limitations in the input datasets. Scripts used to produce this data are available at: https://gitlab.com/impactobservatory/dwi-humanfootprint Overall methodology is based on the following: --B. A. Williams, O. Venter, J. R. Allan, S. C. Atkinson, J. A. Rehbein, M. Ward, M. Di Marco, H. S. Grantham, J. Ervin, S. J. Goetz, A. J. Hansen, P. Jantz, R. Pillay, S. Rodríguez-Buriticá, C. Supples, A. L. S. Virnig, J. E. M. Watson, Change in Terrestrial Human Footprint Drives Continued Loss of Intact Ecosystems. One Earth. 3, 371–382 (2020). --E. W. Sanderson, M. Jaiteh, M. A. Levy, K. H. Redford, A. V. Wannebo, G. Woolmer, The Human Footprint and the Last of the Wild: The human footprint is a global map of human influence on the land surface, which suggests that human beings are stewards of nature, whether we like it or not. BioScience. 52, 891–904 (2002). --O. Venter, E. W. Sanderson, A. Magrach, J. R. Allan, J. Beher, K. R. Jones, H. P. Possingham, W. F. Laurance, P. Wood, B. M. Fekete, M. A. Levy, J. E. M. Watson, Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data. 3, 160067 (2016). Please see the following for more detail: Gassert F, Venter O, Watson JEM, Brumby SP, Mazzariello JC, Atkinson SC and Hyde S, An operational approach to near real-time global high-resolution mapping of the terrestrial human footprint. Front. Remote Sens. 4:1130896. doi: 10.3389/frsen.2023.1130896 (2023)

This data supplements the publication "Land use intensification increasingly drives the spatiotemporal patterns of the global human appropriation of net primary production in the last century" by Thomas Kastner, Sarah Matej, Matthew Forrest, Simone Gingrich, Helmut Haberl, Thomas Hickler, Fridolin Krausmann, Gitta Lasslop, Maria Niedertscheider, Christoph Plutzar, Florian Schwarzmüller, Jörg Steinkamp, Karl-Heinz Erb.

For details, please refer to the included readme file and to the publication (https://doi.org/10.1111/gcb.15932)

In this new Version 1.01, we changed the file structure to make the data more accessible, we added data on means across modulations as used in the paper, and we include csv files with national totals for the different HANPP components.

This dataset is about book series. It has 1 row and is filtered where the books is Human rights education and social change : reproduction and resistance. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.