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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This dataset contains all the data collected in the PhD thesis "Fostering human-centered, augmented machine translation: analysing interactive post-editing". The dataset contains the source texts and the post-edited translations, the MTUX scores, the quality and productivity scores, as well as the pre-task perceptions of each of the 11 translators who collaborated on the two-week longitudinal study.

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.

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).

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.

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/).

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

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.

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.

RNA editing levels of the AluJo sequence in OPHN1 pre-mRNA (sites 1–14) in human adult brain, spinal cord, skin, kidney and thyroid tissues. All the editing percentages are expressed as mean ± s.e.m. (n = 3).

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/).

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].

Background: Multiple surveys of the general public and experts on human genome editing have been conducted. However, many focused only on editing in clinical applications, with few regarding its use for basic research. Given that genome editing for research purposes is indispensable for the realization of clinical genome editing, understanding lay attitudes toward genome editing in research, particularly using human embryos, which is likely to provoke ethical concerns, is helpful for future societal discussion.Methods: An online survey was conducted with Japanese laypeople and researchers to ascertain their views regarding human genome editing for research purposes. Participants were queried about their acceptance as a function of the target of genome editing (germ cells, surplus IVF embryos, research embryos, somatic cells); then, those who answered “acceptable depending on the purpose” were asked about their acceptance in the context of specific research purposes of genome editing. Participants were also asked about their expectations and concerns regarding human genome editing.Results: Replies were obtained from 4,424 laypeople and 98 researchers. Approximately 28.2–36.9% of the laypeople exhibited strong resistance to genome editing for research purposes regardless of their applications. In contrast, 25.5% of the researchers demonstrated resistance only to genome editing in research embryos; this percentage was substantially higher than those concerning the other three targets (5.1–9.2%). Approximately 50.4–63.4% of laypeople who answered “acceptable depending on the purpose” approved germline genome editing for disease research; however, only 39.3–42.8% approved genome editing in basic research to obtain biological knowledge. In contrast, the researchers displayed a lower degree of acceptance of germline genome editing for research purposes related to chronic diseases (60.9–66.7%) than for other research purposes (73.6–90.8%). Analysis of responses concerning expectations and concerns indicated that laypeople who would not accept genome editing of human embryos did not necessarily worry about “instrumentalization of the embryo.” They also had substantially low expectations for recognized advantages of genome editing, including “advances in science” and “reduction of intractable diseases,” compared with other groups of respondents.Conclusion: The assumptions shared among experts in conventional bioethical debates and policy discussions on human genome editing are not self-evident to laypeople.

The fileset contains tables with terms found in a digitized newspaper archive that have edit distance 1-4 to the term ``Amsterdam''. The original data is a selection of 265 newspaper pages of the National Library of The Netherlands.

Raw data for ddPCR. Raw images.

This dataset is about book subjects. It has 1 row and is filtered where the books is Contemplating climate change : mental models and human reasoning. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

RNA editing levels (%) of OPHN1 pre-mRNA (AluJo, sites 1–14) in human fetal brain 18th gestation week (GW18), fetal brain 20th–33rd gestation weeks (GW20–33), adult brain and cerebellum are expressed as mean ± s.e.m (n = 3).

Overview

This dataset contains 20000 pieces of text collected from Wikipedia, Gutenberg, and CNN/DailyMail. The text is cleaned by replacing symbols such as (.*?/) with a white space using automatic scripts and regex.

Data Source Distribution

1. 10,000 Wikipedia Articles: From the 20220301 dump.
2. 3,000 Gutenberg Books: Via the GutenDex API.
3. 7,000 CNN/DailyMail News Articles: From the CNN/DailyMail 3.0.0 dataset.

Why These Sources

The data was collected from these source to ensure the highest level of integrity against AI generated text. * Wikipedia: The 20220301 dataset was chosen to minimize the chance of including articles generated or heavily edited by AI. * Gutenberg: Books from this source are guaranteed to be written by real humans and span various genres and time periods. * CNN/DailyMail: These news articles were written by professional journalists and cover a variety of topics, ensuring diversity in writing style and subject matter.

Dataset Structure

The dataset consists of 5 CSV files. 1. CNN_DailyMail.csv: Contains all processed news articles. 2. Gutenberg.csv: Contains all processed books. 3. Wikipedia.csv: Contains all processed Wikipedia articles. 4. Human.csv: Combines all three datasets in order. 5. Shuffled_Human.csv: This is the randomly shuffled version of Human.csv.

Each file has 2 columns: - Title: The title of the item. - Text: The content of the item.

Uses

This dataset is suitable for a wide range of NLP tasks, including: - Training models to distinguish between human-written and AI-generated text (Human/AI classifiers). - Training LSTMs or Transformers for chatbots, summarization, or topic modeling. - Sentiment analysis, genre classification, or linguistic research.

Disclaimer

While the data was collected from such sources, the data may not be 100% pure from AI generated text. Wikipedia articles may reflect systemic biases in contributor demographics. CNN/DailyMail articles may focus on specific news topics or regions.

For details on how the dataset was created, click here to view the Kaggle notebook used.

Licensing

This dataset is published under the MIT License, allowing free use for both personal and commercial purposes. Attribution is encouraged but not required.

After euthanizing the mice, solid tumors were collected and fixed with 10% formalin (Patterson Veterinary) for 24 hours. Samples were subsequently transferred into 70% ethanol and submitted to the Histology Core at the Koch Institute for paraffin embedding, in 3 batches, the paraffin blocks were subjected to tissue sectioning, Hematoxylin & Eosin (H&E) staining and digital whole slide scanning.