Find research datasets worth reusing

Our processed EndoVis2017 dataset in the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention

Our processed EndoVis2017 dataset is used for the paper "A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention".

Our processed Kvasir-Instrument dataset in the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention

Our processed Kvasir-Instrument dataset is used for the paper "A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention".

opencc-by-4.0Jun 2023View details →

Counting and Sequential Information Processing in Mechanical Metamaterials

This dataset contains images and driving protocols used in the paper: "Counting and Sequential Information Processing in Mechanical Metamaterials", published in Physical Review Letters. In this paper we demonstrate "beam counters"; metamaterials that count driving cycles. We demonstrate the counters sensitivity to various driving amplitudes and show how this might be used to infer more information from the applied driving and how to construct a "lock and key" metamaterial with an internal state that can only be reached with one unique input sequence. The data in this replication package is hierarchically organized by making use of a directory per figure in the paper. Contained in this replication package are a number of files used in the figure as described below. Experimental data from the measurement setup is stored in matched subdirectorys with a name, for example "001/" and a file "times_001.csv". Here the subdirectory contains images taken of the sample and the csv file contains the times at which the images were taken, the change in pixel intensity from one image to the next (sampled at a shorter interval than the saved images) and the position of the driving stage and a measured inductive position.   <ul> <li>fig1 <ul> <li>001/     - directory containing the original unedited figures comparing above and below D*</li> <li>001_cropped_selection/     - directory containing a cropped selection of the images used in the figure</li> <li>times_001.csv</li> </ul> </li> <li>fig2 <ul> <li>002/     - directory containing images of the ten counter being compressed with marked m-beams</li> <li>times_002.csv</li> <li>Kymograph.tif     - A kymograph image calculated from 002 by filtering the colored m-beams and taking stacking a single horizontal slice from all of the images.</li> <li>kymograph.svg     - The plotted horizontal position of the beam traces visible in Kymograph.tif</li> </ul> </li> <li>fig3 <ul> <li>000/     - directory containing images of the compression of counter with uncut a-beams</li> <li>000_cropped/</li> <li>times_000.csv</li> <li>004/     - directory containing images of the same counter with the a-beams slit cut</li> <li>004_cropped/</li> <li>times_004.csv</li> <li>comparison/     - directory containing a selection of cropped images at comparable driving used in the figure</li> </ul> </li> <li>fig4 <ul> <li>Alternative starting condition/ <ul> <li>001/     - directory containing images of the ten-counter compressed in an alternative starting state</li> <li>001_cropped/     - directory with cropped versions of the images in 001/ and further cropped versions in further subdirectories</li> <li>001_selection/</li> <li>times_001.csv</li> </ul> </li> <li>BBAC machine <ul> <li>006/     - directory containing images of the four counters making up the BBAC machine under a driving of BAC</li> <li>006_state/     - selection of images used in the paper with a subdirectory contining cropped versions</li> <li>times_006.csv</li> </ul> </li> </ul> </li> </ul>

A Large Dataset of Tweets on the 2023 Presidential Elections in Nigeria for Natural Language Processing Tasks

The dataset contains tweets related to the 2023 presidential elections in Nigeria. The data was retrieved from the social media network, Twitter (Now X) between February 4th, 2023 and April 4th, 2023. The hashtags from the official handles and other popular hashtags endorsed and/or representing the candidates of each party were considered for retrieving election related tweets using an API from Twitter social media platform. Three major political parties in Nigeria were considered and they have been labelled as Party A, Party L and Party P in this dataset. The party or group called "General" contains tweets from the Independent National Electoral Commission (INEC) hashtags such as @inecnigeria and #2023election which is not directly for any political party. The dataset has been pre-processed lightly to make it very useful to researcher for a wide range of natural language processing tasks like sentiment analysis, topic modelling, fake news detection, emotion detection, election stance, etc. Details of the dataset collection such as hashtags, retrieved tweets, duplicates removed, and the remaining unique tweets is presented in Table 1.    Table 1: Tweets collection and duplicates removal <table> <tbody> <tr> <td> S/N </td> <td> Party </td> <td> Hash tags </td> <td> Retrieved tweets </td> <td> Duplicates tweets </td> <td> Unique tweets </td> </tr> <tr> <td> 1 </td> <td>       X </td> <td> @inecnigeria #2023election </td> <td> 64,496 </td> <td> 47,275 </td> <td> 17,195 </td> </tr> <tr> <td> 2 </td> <td>       A   </td> <td> #TinubuIsComing #emilokan #jagabanarmy #RenewedHope #BATKSM2023 </td> <td> 263,870 </td> <td> 231,036 </td> <td> 32,832 </td> </tr> <tr> <td> 3 </td> <td>         L </td> <td> #VoteLP #NigeriaMustBeBright #PeterObiForPresident2023 #ObiDatti2023 #PeterObi </td> <td> 664,083 </td> <td> 310,857 </td> <td> 353,226 </td> </tr> <tr> <td> 4 </td> <td>   P </td> <td> #NigeriaDecides #VotePDP #AtikuOkowa2023 #FinalPushToVictory #RecoverNigeria </td> <td> 387,450 </td> <td> 318,425 </td> <td> 66,227 </td> </tr> <tr> <td>   </td> <td>   </td> <td>   </td> <td> 1,379,899 </td> <td> 907,593 </td> <td> 468,480 </td> </tr> </tbody> </table>   To encourage NLP tasks, we uploaded in this Version One the following files: <ol> <li>The combined dataset with pre-processed tweets and their meta data but with removed duplicates are in the file labelled “Combined Dataset Pre-processed without duplicates.csv”</li> <li>General statistics on each corpus is in the file labelled “Dataset Statistics.xlsx”  </li> <li>The preprocessed corpus from the general group with the tweet contents only is in file labelled “Preprocessed_Tweet only_GENERAL.xlsx”</li> <li>The preprocessed corpus from Party A with the tweet contents only is in file labelled “Preprocessed_Tweet only_Party A.xlsx”</li> <li>The preprocessed corpus from Party L with the tweet contents only is in file labelled “Preprocessed_Tweet only_Party L.xlsx”</li> <li>The preprocessed corpus from Party P with the tweet contents only is in file labelled “Preprocessed_Tweet only_Party P.xlsx”</li> <li>The top 100 frequent tokens are in the file labelled “Top 100 Tokens and weights.xlsx”</li> <li>The top frequent bigrams and their weights are in the file labelled “Top 100 Bigrams and weights.xlsx”</li> <li>The top frequent trigrams and their weights are in the file labelled “Top 100 Trigrams and weights.xlsx”</li> </ol>

opencc-zeroSep 2023View details →

dryad32/100

Simulations from four process-based ecosystem models describing primary productivity in a tallgrass prairie long-term irrigation experiment

To demonstrate current capabilities in modeling herbaceous ecosystems, we selected four different process-based models that vary in their representation of community change from no community representation to vegetation demographic models. These models were used to simulate a long-term irrigation experiment at a US tallgrass prairie (Konza Prairie Biological Station) following a standardized simulation protocol. Specifically, we were interested in how model output under a monotonic increase in water availability matched up to experimental findings of (1) herbaceous plant community change and (2) aboveground net primary productivity before and after the plant community change. The results of this simulation are included here.

Processed Stereo-seq data of mouse olfactory bulb data analyzed in STAMarker

Processed Stereo-seq data of mouse olfactory bulb data analyzed in STAMarker

H2020 ENODISE: UR3 Aeroacoustic processing Configuration A1

This database encompasses post-processing results derived from experiments conducted at the University of Bristol, specifically focusing on configuration A1. In this research, two distinct boundary layer thicknesses were taken into account, with an analysis comparing the performance of 3-bladed and 5-bladed propellers. The freestream velocity maintained a constant value of 20 m/s, and the chosen advance ratio (J) spanned the range of [0.56, 0.98]. The dataset includes information such as the Overall Sound Pressure Level, Haystacking Maps, and Spectra Scaling. These metrics are provided for four different scenarios, each involving variations in the number of blades and boundary layer thickness.

opencc-by-4.0Oct 2022View details →

Dataset for "Exploring the distribution of phylogenetic networks generated under a birth-death-hybridization process"

Contains all simulation scripts, simulated data, and supplemental materials

demonstrator_printing_process

A demonstrator case of the printing process.

Si/SiGe QuBus for single electron information-processing devices with memory and micron-scale connectivity function

Dataset and analysis scripts for the arXiv paper: Si/SiGe QuBus for single electron information-processing devices with memory and micron-scale connectivity function (submitted for peer-review)

opencc-by-4.0Aug 2023View details →

Data of: Data-driven and physics-based modelling of process behaviour and deposit geometry for friction surfacing

This dataset contains the data and models used in the research journal publication: "Data-driven and physics-based modelling of process behaviour and deposit geometry for friction surfacing " which was funded from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 101001567).

Processed Single-cell RNA-sequencing data from adult recurrent respiratory papillomatosis

.rds file (read in R) that contains processed single-cell RNA-sequencing data from 13 untreated adult recurrent respiratory papillomatosis clinical samples. The associated raw RNA sequencing data is available through The Database of Genotypes and Phenotypes (dbGaP), accession number phs003349.v1.p1. Sequences were also alsigned to a, HPV reference to allow quantification of HPV 6 or 11 gene expression.

opencc-by-4.0Aug 2023View details →

Figures for 'Global streamflow modelling using process-informed machine learning'

High-quality figures for the article 'Global streamflow modelling using process-informed machine learning' (<a href="https://doi.org/10.2166/hydro.2023.217">https://doi.org/10.2166/hydro.2023.217</a>).

Data Processing Pipeline and Products from the NANOGrav 12.5-Year Data Set: Dispersion Measure Mis-Estimation with Varying Bandwidths

This Zenodo dataset contains the data processing pipeline, as well as the data products, corresponding to the scientific journal paper "NANOGrav 12.5-Year Data Set: Dispersion Measure Mis-Estimation with Varying Bandwidths". The processing pipeline is structured as follows: <ol> <li>1_fit_dispersion_3terms.py  reads the .tim files (containing the times-of-arrival), creates the broadband and narrowband datasets, and fits a dispersion model to both datasets using three parameters.</li> <li>2_plot_fits_differences.py creates the residual plots showing the differences in the fitted values of the parameters.</li> <li>3_autocovariance.py calculates the autocovariance function of the differences in the fitted values, and creates the corresponding plots.</li> <li>All the files starting with "plot" are convenience scripts for creating the plots presented in the paper.</li> <li>All the files starting with "sophia" are utility functions created by the authors that are used in the main pipeline.</li> <li>The folder "NANOGrav_12yv4" contains the dataset analyzed in this work.</li> <li>The folder "NG_timing_analysis" contains utility functions created by the NANOGrav collaboration that are used in the main pipeline.</li> </ol> Please do not hesitate to send all your questions, concerns, or commentaries to sophia.sosa@nanograv.org

Benchmark tests imply the explicit correlation between chain-like stress transfer and interevent violation of Gutenberg-Richter law null hypothesis in fracturing process [DATA]

Data and metadata for article: Benchmark tests imply the explicit correlation between chain-like stress transfer and interevent violation of Gutenberg-Richter law null hypothesis in fracturing process

Hybrid analytical surrogate-based process optimization via Bayesian symbolic regression

Dataset associated with the publication "Hybrid analytical surrogate-based process optimization via Bayesian symbolic regression" by Sachin Jog, Daniel Vázquez, Lucas F. Santos, José A. Caballero, and Gonzalo Guillén-Gosálbez. The dataset includes the parameters used in the DACE BB models of case studies 1 and 2, described in sections 5.1.3 and 5.2.3 of the supplementary material, respectively.

ML System Development Processes Expert Interview Transcripts

Open the record for dataset details and reuse information.

A carbon, nitrogen, and multi-isotope study of basalt glasses near 14°N on the Mid-Atlantic Ridge. Part A: Degassing processes

All data appearing in the manuscript and any supplementary documents, figures, or tables of the paper "A carbon, nitrogen, and multi-isotope study of basalt glasses near 14°N on the Mid-Atlantic Ridge. Part A: Degassing processes" at Geochimica et Cosmochimica Acta by Bekaert et al., are available through this open access data repository.

Dispersion Engineered Metasurfaces for Broadband, High-NA, High-Efficiency, Dual-Polarization Analog Image Processing

This dataset contains tabulated versions of the experimental data shown in the pictures of the publication "Dispersion Engineered Metasurfaces for Broadband, High-NA, High-Efficiency, Dual-Polarization Analog Image Processing"Figure 2<ul><li>The file Fig_2_tpp.xlsx contains the raw data of Fig. 2g. The first sheet contains a 31 x 121 matrix. The second sheet contains a column vector with 121 entries, corresponding to the wavelength axis. The third sheet contains a column vector with 31 entries, corresponding to the angle theta axis.</li><li>The file Fig_2_tss.xlsx contains the raw data of Fig. 2i. The first sheet contains a 31 x 121 matrix. The second sheet contains a column vector with 121 entries, corresponding to the wavelength axis. The third sheet contains a column vector with 31 entries, corresponding to the angle theta axis.</li><li>The data of the plots in Fig. 2h and 2j can be extracted from the corresponding row/columns in the files Fig_2_tpp.xlsx and  Fig_2_tss.xlsx</li></ul>Figure 4<ul><li>The file Fig_4b_MSoff.xlsx contains the raw data of the first panel ('NO Metasurface') of Fig. 4b</li><li>The files Fig_4b_MSon_X.xlsx, with X = [1435, 1445, 1450, 1452, 1455:5:1480, 1483, 1485, 1490, 1495], contain the raw data of the other panels of Fig. 4b.</li><li>For all panels of Fig. 4b, each pixel corresponds to 0.2864 microns.</li><li>The data of the plots in Fig. 4c can be extracted from the corresponding row/columns of the plots in Fig. 4b</li></ul>Figure 5<ul><li>The file Fig_5a.xlsx contains the spectra shown in Fig. 5a</li><li>The files Fig_5b_MSoff.xlsx, Fig_5b_MSon_xpol.xlsx, Fig_5b_MSon_ypol.xlsx, Fig_5b_MSon_+45.xlsx, Fig_5b_MSon_-45.xlsx, Fig_5b_MSon_Lpol.xlsx, Fig_5b_MSon_Rpol.xlsx contains the raw data of the panels b, c, d, e, f, g, h, respectively</li><li>For all 2D images in Fig. 5, each pixel corresponds to 0.2864 microns.</li><li>The data of the plots in Fig. 5i can be extracted from the corresponding row/columns of the plots in Figs. 5c-5h</li></ul>