Dataset Card for "RLCD-generated-preference-data-split"

More Information needed

Dataset

This dataset was created by Maryam Khan Afridi 2024

Contents

Split Paragraphs Dataset

Split paragraphs data with configs 000-099.

Data Split

## Overview

Data Split is a dataset for classification tasks - it contains 1 annotations for 639 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This article studies the projection test for high-dimensional mean vectors via optimal projection. The idea of projection test is to project high-dimensional data onto a space of low dimension such that traditional methods can be applied. We first propose a new estimation for the optimal projection direction by solving a constrained and regularized quadratic programming. Then two tests are constructed using the estimated optimal projection direction. The first one is based on a data-splitting procedure, which achieves an exact t-test under normality assumption. To mitigate the power loss due to data-splitting, we further propose an online framework, which iteratively updates the estimation of projection direction when new observations arrive. We show that this online-style projection test asymptotically converges to the standard normal distribution. Various simulation studies as well as a real data example show that the proposed online-style projection test retains the Type I error rate well and is more powerful than other existing tests. Supplementary materials for this article are available online.

Dataset

This dataset was created by Ali Gold Medalist

Released under Apache 2.0

Contents

Dataset

This dataset was created by Dicka taksa

Contents

Dataset Card for "cleaned-data-split-0"

More Information needed

Full and dummy snapshots (2022-06-04) of data for mp-time-split encoded via matminer convenience functions grabbed via the new Materials Project API. The dataset is restricted to experimentally verified compounds with no more than 52 sites. No other filtering criteria were applied. The snapshots were developed for sparks-baird/mp-time-split as a benchmark dataset for materials generative modeling. Compressed version of the files (.gz) are also available. dtypes python from pprint import pprint from matminer.utils.io import load_dataframe_from_json filepath = "insert/path/to/file/here.json" expt_df = load_dataframe_from_json(filepath) pprint(expt_df.iloc[0].apply(type).to_dict()) {'discovery': , 'energy_above_hull': , 'formation_energy_per_atom': , 'material_id': , 'references': , 'structure': , 'theoretical': , 'year': } index/mpids (just the number for the index). Note that material_id-s that begin with "mvc-" have the "mvc" dropped and the hyphen (minus sign) is left to distinguish between "mp-" and "mvc-" types while still allowing for sorting. E.g. mvc-001 -> -1.

{146: MPID(mp-146), 925: MPID(mp-925), 1282: MPID(mp-1282), 1335: MPID(mp-1335), 12778: MPID(mp-12778), 2540: MPID(mp-2540), 316: MPID(mp-316), 1395: MPID(mp-1395), 2678: MPID(mp-2678), 1281: MPID(mp-1281), 1251: MPID(mp-1251)}

This resource contains the data used in the study "Analyzing the Effect of Data Splitting and Covariate Shift on Machine Leaning Based Streamflow Prediction in Ungauged Basins" published in Water Resources Research (doi: 10.1029/2023WR034464)

Advances in neuroimaging, genomic, motion tracking, eye-tracking and many other technology-based data collection methods have led to a torrent of high dimensional datasets, which commonly have a small number of samples because of the intrinsic high cost of data collection involving human participants. High dimensional data with a small number of samples is of critical importance for identifying biomarkers and conducting feasibility and pilot work, however it can lead to biased machine learning (ML) performance estimates. Our review of studies which have applied ML to predict autistic from non-autistic individuals showed that small sample size is associated with higher reported classification accuracy. Thus, we have investigated whether this bias could be caused by the use of validation methods which do not sufficiently control overfitting. Our simulations show that K-fold Cross-Validation (CV) produces strongly biased performance estimates with small sample sizes, and the bias is still evident with sample size of 1000. Nested CV and train/test split approaches produce robust and unbiased performance estimates regardless of sample size. We also show that feature selection if performed on pooled training and testing data is contributing to bias considerably more than parameter tuning. In addition, the contribution to bias by data dimensionality, hyper-parameter space and number of CV folds was explored, and validation methods were compared with discriminable data. The results suggest how to design robust testing methodologies when working with small datasets and how to interpret the results of other studies based on what validation method was used.

Cleaned_Dataset.csv – The combined CSV files of all scraped documents from DABI, e-LiS, o-bib and Springer.

Data_Cleaning.ipynb – The Jupyter Notebook with python code for the analysis and cleaning of the original dataset.

ger_train.csv – The German training set as CSV file.

ger_validation.csv – The German validation set as CSV file.

en_test.csv – The English test set as CSV file.

en_train.csv – The English training set as CSV file.

en_validation.csv – The English validation set as CSV file.

splitting.py – The python code for splitting a dataset into train, test and validation set.

DataSetTrans_de.csv – The final German dataset as a CSV file.

DataSetTrans_en.csv – The final English dataset as a CSV file.

translation.py – The python code for translating the cleaned dataset.

FrugalMath Dataset: Easy Samples as Length Regularizers in Math RLVR

Paper: Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVR Base Model: Qwen/Qwen3-4B-Thinking-2507 Authors: Abdelaziz Bounhar et al. License: Apache 2.0

  Overview

The FrugalMath dataset was designed to study implicit length regularization in Reinforcement Learning with Verifiable Rewards (RLVR). Unlike standard pipelines that discard easy problems, this dataset… See the full description on the dataset page: https://huggingface.co/datasets/MBZUAI-Paris/frugal-maths-data-split-v1.

Data split for each class of each dataset for training and test.

yongjoongkim/X-ALMA-Parallel-Data-Split dataset hosted on Hugging Face and contributed by the HF Datasets community

Yearly citation counts for the publication titled "Rank-transformed subsampling: inference for multiple data splitting and exchangeable p-values".

Dataset

This dataset was created by DanielJamesdj08

Released under MIT

Contents

Historical Stock Splits API provides financial data users with a rapid access to historical stock splits data. Company executive boards of public companies very often aim for stock splitting when circumstances are favourable. Stock-splitting leads to an increased number of shares sold at lower prices. In this way, prospective investors or company shareholders purchase more shares at attractive prices. If you need historical stock splitting data for your financial project, try out Finnworlds Historical Stock Splits API. In case you want to learn more about it, please, visit the website. https://finnworlds.com/historical-stock-splits-api/

SPLIT 3

## Overview

SPLIT 3 is a dataset for object detection tasks - it contains SPLIT3 annotations for 7,306 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The increase in power electronic based generation sources require accurate modeling of inverters. Accurate modeling requires experimental data over wider operation range. We used 8.35 kW off-the-shelf grid following split phase PV inverter in the experiments. We used controllable AC supply and controllable DC supply to emulate AC and DC side characteristics. The experiments were performed at NREL's Energy Systems Integration Facility. Inverter is tested under 100%, 75%, 50%, 25% load conditions. In the first dataset, for each operating condition, controllable AC source voltage is varied from 0.9 to 1.1 per unit (p.u) with a step value of 0.025 p.u while keeping the frequency at 60 Hz. In the second dataset, under similar load conditions (100%, 75%, 50%, 25% ), the frequency of the controllable AC source voltage was varied from 59 Hz to 61 Hz with a step value of 0.2 Hz. Voltage and frequency range is chosen based on inverter protection. Voltages and currents on DC and AC side are included in the dataset.