Dataset

This dataset was created by SamarthG2301

Contents

The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four learning rates, six minibatch sizes, four levels of label noise, and fourteen levels of L1 and L2 regularization each. Multiple repetitions (typically 30, sometimes 10) of each combination of hyperparameters were preformed, and statistics including training and test loss (using a 80% / 20% shuffled train-test split) are recorded at the end of each training epoch. In total, this dataset covers 178 thousand distinct hyperparameter settings ("experiments"), 3.55 million individual training runs (an average of 20 repetitions of each experiments), and a total of 13.3 billion training epochs (three thousand epochs were covered by most runs). Accumulating this dataset consumed 5,448.4 CPU core-years, 17.8 GPU-years, and 111.2 node-years.

Dataset

This dataset was created by mdnurhossen

Released under CC0: Public Domain

Contents

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

V2 is out!!! V2

  Simple "Reflection" method dataset inspired by mattshumer





  This is the prompt and response version. Find ShareGPT version here

This dataset was synthetically generated using Glaive AI.

Coups d'Ètat are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup d’État Project as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e., realized, unrealized, or conspiracy) the type of actor(s) who initiated the coup (i.e., military, rebels, etc.), as well as the fate of the deposed leader. Version 2.1.3 adds 19 additional coup events to the data set, corrects the date of a coup in Tunisia, and reclassifies an attempted coup in Brazil in December 2022 to a conspiracy. Version 2.1.2 added 6 additional coup events that occurred in 2022 and updated the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrected a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixed this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removed two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and added executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Version 2.0.0 improved several aspects of the previous version (v1.0.0) and incorporated additional source material to include: • Reconciling missing event data • Removing events with irreconcilable event dates • Removing events with insufficient sourcing (each event needs at least two sources) • Removing events that were inaccurately coded as coup events • Removing variables that fell below the threshold of inter-coder reliability required by the project • Removing the spreadsheet ‘CoupInventory.xls’ because of inadequate attribution and citations in the event summaries • Extending the period covered from 1945-2005 to 1945-2019 • Adding events from Powell and Thyne’s Coup Data (Powell and Thyne, 2011)
Items in this Dataset 1. Cline Center Coup d'État Codebook v.2.1.3 Codebook.pdf - This 15-page document describes the Cline Center Coup d’État Project dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d'État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. Revised February 2024 2. Coup Data v2.1.3.csv - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 1000 observations. Revised February 2024 3. Source Document v2.1.3.pdf - This 325-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify that particular event. Revised February 2024 4. README.md - This file contains useful information for the user about the dataset. It is a text file written in markdown language. Revised February 2024
Citation Guidelines 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2024. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Emilio Soto. 2024. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7

About this dataset

Context

The dataset tabulates the Kiawah Island population by age. The dataset can be utilized to understand the age distribution and demographics of Kiawah Island.

Content

The dataset constitues the following three datasets

• Kiawah Island, SC Age Group Population Dataset: A complete breakdown of Kiawah Island age demographics from 0 to 85 years, distributed across 18 age groups
• Kiawah Island, SC Age Cohorts Dataset: Children, Working Adults, and Seniors in Kiawah Island - Population and Percentage Analysis
• Kiawah Island, SC Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Dataset Card for RLAIF-V-Dataset

GitHub | Paper

  News:

[2024.05.28] 📃 Our paper is accesible at arxiv now! [2024.05.20] 🔥 Our data is used in MiniCPM-Llama3-V 2.5, which represents the first end-side MLLM achieving GPT-4V level performance!

  Dataset Summary

RLAIF-V-Dataset is a large-scale multimodal feedback dataset. The dataset provides high-quality feedback with a total number of 83,132 preference pairs, where the instructions are collected from a diverse… See the full description on the dataset page: https://huggingface.co/datasets/unsloth/RLAIF-V-Dataset.

QALD-9-Plus Dataset Description QALD-9-Plus is the dataset for Knowledge Graph Question Answering (KGQA) based on well-known QALD-9.

QALD-9-Plus enables to train and test KGQA systems over DBpedia and Wikidata using questions in 9 different languages: English, German, Russian, French, Armenian, Belarusian, Lithuanian, Bashkir, and Ukrainian.

Some of the questions have several alternative writings in particular languages which enables to evaluate the robustness of KGQA systems and train paraphrasing models.

As the questions' translations were provided by native speakers, they are considered as "gold standard", therefore, machine translation tools can be trained and evaluated on the dataset.

Dataset Statistics | | en | de | fr | ru | uk | lt | be | ba | hy | # questions DBpedia | # questions Wikidata | |-------|:---:|:---:|:--:|:----:|:---:|:---:|:---:|:---:|:--:|:-----------:|:-----------:| | Train | 408 | 543 | 260 | 1203 | 447 | 468 | 441 | 284 | 80 | 408 | 371 | | Test | 150 | 176 | 26 | 348 | 176 | 186 | 155 | 117 | 20 | 150 | 136 |

Given the numbers, it is obvious that some of the languages are covered more than once i.e., there is more than one translation for a particular question. For example, there are 1203 Russian translations available while only 408 unique questions exist in the training subset (i.e., 2.9 Russian translations per one question). The availability of such parallel corpora enables the researchers, developers and other dataset users to address the paraphrasing task.

LinTO DataSet Audio for Arabic Tunisian Augmented A collection of Tunisian dialect audio and its annotations for STT task

This is the augmented datasets used to train the Linto Tunisian dialect with code-switching STT linagora/linto-asr-ar-tn.

Dataset Summary Dataset composition Sources Content Types Languages and Dialects

Example use (python) License Citations

  Dataset Summary

The LinTO DataSet Audio for Arabic Tunisian Augmented is a dataset that builds on LinTO… See the full description on the dataset page: https://huggingface.co/datasets/linagora/linto-dataset-audio-ar-tn-augmented.

Context

This Dataset is an updated version of the Amazon review dataset released in 2014. As in the previous version, this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). In addition, this version provides the following features:

• More reviews:

  • The total number of reviews is 233.1 million (142.8 million in 2014).

• New reviews:

  • Current data includes reviews in the range May 1996 - Oct 2018.

• Metadata: - We have added transaction metadata for each review shown on the review page.

  • Added more detailed metadata of the product landing page.

Acknowledgements

If you publish articles based on this dataset, please cite the following paper:

• Jianmo Ni, Jiacheng Li, Julian McAuley. Justifying recommendations using distantly-labeled reviews and fined-grained aspects. EMNLP, 2019.

the layout planning of residential community has always been of concern

We introduce a large-scale dataset of the complete texts of free/open source software (FOSS) license variants. To assemble it we have collected from the Software Heritage archive—the largest publicly available archive of FOSS source code with accompanying development history—all versions of files whose names are commonly used to convey licensing terms to software users and developers. The dataset consists of 6.5 million unique license files that can be used to conduct empirical studies on open source licensing, training of automated license classifiers, natural language processing (NLP) analyses of legal texts, as well as historical and phylogenetic studies on FOSS licensing. Additional metadata about shipped license files are also provided, making the dataset ready to use in various contexts; they include: file length measures, detected MIME type, detected SPDX license (using ScanCode), example origin (e.g., GitHub repository), oldest public commit in which the license appeared. The dataset is released as open data as an archive file containing all deduplicated license blobs, plus several portable CSV files for metadata, referencing blobs via cryptographic checksums.

For more details see the included README file and companion paper:

Stefano Zacchiroli. A Large-scale Dataset of (Open Source) License Text Variants. In proceedings of the 2022 Mining Software Repositories Conference (MSR 2022). 23-24 May 2022 Pittsburgh, Pennsylvania, United States. ACM 2022.

If you use this dataset for research purposes, please acknowledge its use by citing the above paper.

RLHFlow/pair-preference-dataset-mix1 dataset hosted on Hugging Face and contributed by the HF Datasets community

Context

A retail bank would like to hire you to build a credit default model for their credit card portfolio. The bank expects the model to identify the consumers who are likely to default on their credit card payments over the next 12 months. This model will be used to reduce the bank’s future losses. The bank is willing to provide you with some sample datathat they can currently extract from their systems. This data set (credit_data.csv) consists of 13,444 observations with 14 variables.

Content

Based on the bank’s experience, the number of derogatory reports is a strong indicator of default. This is all that the information you are able to get from the bank at the moment. Currently, they do not have the expertise to provide any clarification on this data and are also unsure about other variables captured by their systems

In scientific research, the ability to effectively retrieve relevant documents based on complex, multifaceted queries is critical. Existing evaluation datasets for this task are limited, primarily due to the high costs and effort required to annotate resources that effectively represent complex queries. To address this, we propose a novel task, Scientific DOcument Retrieval using Multi-level Aspect-based quEries (DORIS-MAE), which is designed to handle the complex nature of user queries in scientific research.

Documentations for the DORIS-MAE dataset is publicly available at https://github.com/Real-Doris-Mae/Doris-Mae-Dataset. This upload contains both DORIS-MAE dataset version 1 and ada-002 vector embeddings for all queries and related abstracts (used in candidate pool creation). DORIS-MAE dataset version 1 is comprised of four main sub-datasets, each serving distinct purposes.

The Query dataset contains 100 human-crafted complex queries spanning across five categories: ML, NLP, CV, AI, and Composite. Each category has 20 associated queries. Queries are broken down into aspects (ranging from 3 to 9 per query) and sub-aspects (from 0 to 6 per aspect, with 0 signifying no further breakdown required). For each query, a corresponding candidate pool of relevant paper abstracts, ranging from 99 to 138, is provided.

The Corpus dataset is composed of 363,133 abstracts from computer science papers, published between 2011-2021, and sourced from arXiv. Each entry includes title, original abstract, URL, primary and secondary categories, as well as citation information retrieved from Semantic Scholar. A masked version of each abstract is also provided, facilitating the automated creation of queries.

The Annotation dataset includes generated annotations for all 165,144 question pairs, each comprising an aspect/sub-aspect and a corresponding paper abstract from the query's candidate pool. It includes the original text generated by ChatGPT (version chatgpt-3.5-turbo-0301) explaining its decision-making process, along with a three-level relevance score (e.g., 0,1,2) representing ChatGPT's final decision.

Finally, the Test Set dataset contains human annotations for a random selection of 250 question pairs used in hypothesis testing. It includes each of the three human annotators' final decisions, recorded as a three-level relevance score (e.g., 0,1,2).

The file "ada_embedding_for_DORIS-MAE_v1.pickle" contains text embeddings for the DORIS-MAE dataset, generated by OpenAI's ada-002 model. The structure of the file is as follows:

├── ada_embedding_for_DORIS-MAE_v1.pickle ├── "Query" │ ├── query_id_1 (Embedding of query_1) │ ├── query_id_2 (Embedding of query_2) │ └── query_id_3 (Embedding of query_3) │ . │ . │ . └── "Corpus" ├── corpus_id_1 (Embedding of abstract_1) ├── corpus_id_2 (Embedding of abstract_2) └── corpus_id_3 (Embedding of abstract_3) . . .

Dataset card for dataset-summaries-llama

This dataset contains AI-generated summaries of dataset cards from the Hugging Face Hub, generated using meta-llama/Llama-3.3-70B-Instruct. It is designed to be used in combination with a similar dataset of model card summaries for initial supervised fine-tuning (SFT) of language models specialized in generating tl;dr summaries of dataset and model cards from the Hugging Face Hub. This dataset was made with Curator.

  Dataset… See the full description on the dataset page: https://huggingface.co/datasets/davanstrien/hub-tldr-dataset-summaries-llama.

Wyze Rule Recommendation Dataset

  Dataset Summary

The Wyze Rule dataset is a new large-scale dataset designed specifically for smart home rule recommendation research. It contains over 1 million rules generated by 300,000 users from Wyze Labs, offering an extensive collection of real-world automation rules tailored to users' unique smart home setups. The goal of the Wyze Rule dataset is to advance research and development of personalized rule recommendation… See the full description on the dataset page: https://huggingface.co/datasets/wyzelabs/RuleRecommendation.

Yagmur Yigit