Find research datasets worth reusing

opencc-by-sa-4.0Sep 2024View details →

LENS: A LEO Satellite Network Measurement Dataset - 202407 - Part 4

LENS dataset 2024-07 Part 4 Please see https://github.com/clarkzjw/LENS for the complete description of the dataset.

DCP-MTL: Vectorization of Agricultural Cultivation Field Parcels via Boundary-Parcel Multi-Task Learning Network in Ultra-High-Resolution Remote Sensing Images

This paper introduces the first UHR UAV dataset specifically for CFP, designed to evaluate the performance of the proposed model in identifying these parcels. The dataset offers ultra-high spatial resolution, various field parcel types, and broad geographic coverage. Figure 8 shows the spatial distribution of the study data. Jilin Province is the primary region for training and evaluating the model, while Hebei, Henan, Anhui, Zhejiang, and Hainan are auxiliary regions for testing the model's transferability.

Angiosperm NLR Synteny Network Databases

PEP and BED Files of 124 Angiosperm Employed in the Study 'Deciphering Plant NLR Genomic Evolution: A Synteny-Informed Classification Reveals Insights into TNL Gene Loss'

CRISPRi with barcoded expression reporters dissects regulatory networks in human cells

Genome-wide CRISPR screens have emerged as powerful tools for uncovering the genetic underpinnings of diverse biological processes. Incisive screens often depend on directly measuring molecular phenotypes, such as regulated gene expression changes, provoked by CRISPR-mediated genetic perturbations. Here, we provide quantitative measurements of transcriptional responses in human cells across genome-scale perturbation libraries by coupling CRISPR interference (CRISPRi) with barcoded expression reporter sequencing (CiBER-seq). To enable CiBER-seq in mammalian cells, we optimize the integration of highly complex, barcoded sgRNA libraries into a defined genomic context. CiBER-seq profiling of a nuclear factor kappa B (NF-κB) reporter delineates the canonical signaling cascade linking the transmembrane TNF-alpha receptor to inflammatory gene activation and highlights cell-type-specific factors in this response. Importantly, CiBER-seq relies solely on bulk RNA sequencing to capture the regulatory circuit driving this rapid transcriptional response. Our work demonstrates the accuracy of CiBER-seq and its potential for dissecting genetic networks in mammalian cells with superior time resolution.

opencc-by-4.0Jun 2024View details →

Phyloformer: Fast, accurate and versatile phylogenetic reconstruction with deep neural networks

This record is composed of:  <ul> <li>The <code>results.tar.gz</code> file which  contains all the output files necessary to reproduce the figures and tables from the linked paper</li> <li>The 3 datasets used to fine tune different versions of Phylofofmer: <ol> <li><code>cherry_fine_tune.tar.xz</code> used to fine tune Phyloformer on the CherryML model</li> <li><code>LG_fine_tune_mre.tar.xz</code> used to fine tune Phyloformer on LG+GC data with an MRE loss</li> <li><code>pastek_fine_tune.tar.xz</code> used to fine tune Phyloformer on the SelReg model</li> </ol> </li> <li>The <code>paper_test_sets.tar.xz</code> file contains the test sets used to generate data in <code>results.tar.gz</code>, with simulated tree/msa pairs and trees inferred by different methods</li> </ul>

Activation and connectivity maps - A chronometric relationship between circuits underlying learning and error monitoring in the basal ganglia and salience network

Activation and connectivity maps of the study "A chronometric relationship between circuits underlying learning and error monitoring in the basal ganglia and salience network". <ul> <li>Error-correct.nii corresponds to the statistical map of group-level differences in BOLD signal between correct and erroneous responses shown in figure 4;</li> <li>Late-initial.nii corresponds to the statistical map of group-level differences in BOLD signal between the initial and late learning periods shown in figure 5;</li> <li>Conn_error-correct_dACC.nii corresponds to the results from the seed-to-voxel gPPI analysis, using the dACC as seed region, showing areas of higher functional connectivity in erroneous compared to correct responses, shown in figure 8.</li> </ul>

opencc-by-4.0Jun 2024View details →

Supplementary Data and Software for "Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance"

The file includes supplementary data for "Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance". If you would like to re-use the software provided please cite the following two items. R. Klus, J. Talvitie, J. Equi, G. Fodor, J. Torsner, and M. Valkama, “Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance,” IEEE Transactions on Vehicular Technology, 2024. R Klus et al. (2024). Supplementary materials for “Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance”. version v1, 25.06.2024, [Online]. Available: https://doi.org/10.5281/zenodo.12204892 If you have any questions about this package, please do not hesitate to contact Roman Klus (roman.klus@tuni.fi).

Dataset of "Gender analysis and co-authorship networks in the scientific production of oncology in Spain (2011–2021)"

Open the record for dataset details and reuse information.

Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Oulu facility

This data set covers core network and end-to-end measurements at the Oulu facility, part of the 6G-SANDBOX infrastructure.

Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Malaga facility

This data set covers core network and end-to-end measurements at the Malaga facility, part of the 6G-SANDBOX infrastructure.

Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Berlin facility

This data set covers core network and end-to-end measurements at the Berlin facility, part of the 6G-SANDBOX infrastructure.

Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Athens facility

This data set covers core network and end-to-end measurements at the Athens facility, part of the 6G-SANDBOX infrastructure.

Dataset for "On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates"

<h1>Code and Dataset for "On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates"</h1> This dataset accompanies the research paper titled "On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates", currently under review for the AGU Journal GRL. The study introduces a novel Regional Climate Model (RCM) emulator focusing on high-resolution climate downscaling for the New Zealand region. For additional insights and access to the codebase utilized in this research, please refer to our <a href="https://github.com/nram812/On-the-Extrapolation-of-Generative-Adversarial-Networks-for-downscaling-precipitation-extremes">Github Repository</a>. The code can also be found as a ".zip" file: *On-the-Extrapolation-of-Generative-Adversarial-Networks-for-downscaling-precipitation-extremes-main.  <h2>Aims</h2> Our study focuses on two important gaps in the literature regarding the extrapolation of empirical downscaling algorithms. First, we examine how well relationships learned from a historical period extrapolate to future unobserved climates. We compare two widely used algorithms, a GAN and a deterministic CNN baseline, that use a similar architecture (i.e. convolutional layers) trained in a model-as-truth framework to downscale daily precipitation over New Zealand. We evaluate their accuracy in capturing climate change signals in mean and extreme precipitation. Second, we explore whether training on future vs. only historical periods combined with different-sized training datasets can improve extrapolation skill.  <h2>Geographic Focus</h2> Our research focuses only on the New Zealand Region (165°E-184°W, 33°S-51°S).   <h2>Data Overview</h2> <h3>Training and Evaluation Data</h3> The training data used in this study (for our RCM emulator) spans the historical period and future period (SSP370) of simulation. It comprises daily accumulated precipitation as the primary target variable, alongside large-scale predictor variables.  <ul> <li> Resolution: The target variable is presented at a 12km resolution, reflecting the highest resolution face of RCM for the New Zealand region. Predictor variables are coarsened to a 1.5-degree resolution from original CCAM outputs using conservative interpolation.  </li> <li> Period Coverage: <ul> <li>Training Data: 1960-2100 (Depending on Experiment, see Table 1 for list of experiment configurations)</li> <li>Validation Data: 1985-2014 + 2070-2099 (to compute the climate change signal)</li> </ul> </li> <li> Models: <ul> <li>Training on: ACCESS-CM2</li> <li>Validated on: EC-Earth3, NorESM2-MM, CNRM-CM6-1, AWI-MR-1 </li> </ul> </li> </ul> <h3>File Structure</h3> <ul> <li> Training Data: <ul> <li>Target/Ground Truth (Y): <code>target_ACCESS-CM2_hist_ssp370_pr.nc</code></li> <li>Predictor (X): <code>predictor_ACCESS-CM2_hist_ssp370.nc</code></li> </ul> </li> <li> Evaluation Data: All other GCMs can be accessed in one single file, predictor and target variables have the dimensions (time, lat, lon, GCM). <ul> <li>Target/Ground Truth (Y): <code>Other_GCMs_hist_SSP370_target_fields_pr.nc</code></li> <li>Predictor (X): <code>Other_GCMs_hist_SSP370_predictor_fields.nc</code></li> </ul> </li> </ul> <h2>Methodological Insights</h2> <ul> <li> Regional Climate Model, Our Regional Climate Model training data is from the Conformal Cubic Atmospheric Model (CCAM) which is a global non-hydrostatic atmospheric model renowned for its variable-resolution cubic grid. . For more information about CCAM, please see the following <a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023JD038530">paper</a>. </li> <li> Predictor and Target Variables: Daily-averaged large-scale prognostic variables, including zonal wind, meridional wind, temperature, and specific humidity, are employed as predictors at the 500mb and 850mb pressure levels. These are normalized (see the GitHub repository for the mean and standard deviation fields). Precipitation is taken as is from CCAM and accumulated for each given day. Static predictors are also used in our model, which is stored in a GitHub repository. </li> <li> Training Framework: Our dataset benefits from the "perfect framework" training strategy, which uses CCAM-coarsened predictor variables. For more information about the perfect and imperfect training frameworks, see the following <a title="review" href="https://journals.ametsoc.org/view/journals/aies/3/2/AIES-D-23-0066.1.xml">review</a> </li> </ul> <table> <tbody> <tr> <td> Algorithm </td> <td> Training Data </td> <td> Period </td> </tr> <tr> <td> Deterministic Baseline </td> <td> Historical </td> <td> 1960-2014 (~21,000 days) </td> </tr> <tr> <td> Deterministic Baseline </td> <td> Future (SSP370) </td> <td> 2044-2099 (~21,000 days) </td> </tr> <tr> <td> Deterministic Baseline </td> <td> Historical and Future (SSP370) </td> <td> 1960-2099 (~51,000 days) </td> </tr> <tr> <td> Residual GAN </td> <td> Historical </td> <td> 1960-2014 </td> </tr> <tr> <td> Residual GAN </td> <td> Future (SSP370) </td> <td> 2044-2099 </td> </tr> <tr> <td> Residual GAN </td> <td> Historical and Future (SSP370) </td> <td> 1960-2099 </td> </tr> </tbody> </table> Table 1: The six RCM emulator experiments performed in this study.

openmit-licenseSep 2024View details →

A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology

Source Data and Supplementary Data associated with the paper “A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology” (DOI: https://doi.org/10.1101/2023.03.06.531441).

Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Model Simulation Data

WRF-Chem simulated daily mean PM2.5 concentrations for: 1) with fires  2) without fires simulations.

Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Purple Air data

Daily mean PM2.5 concentrations collected by Purple Air sensors between 2023-08-16 and 2023-12-01. Concentrations have been RH adjusted using the Nilson et al (2022) adjustment.

opencc-by-4.0May 2024View details →

Dataset for the paper "Network-Based Differential Abundance Analysis: Bridging Community Interactions and Host-Microbiome Dynamics."

The files with extension rds are files that contain simulated data and the tsv files contain original data along with their meta data.

Surfaces/regoliths used in the training and testing of the deep neural network for surface reconstruction from simulated exospheric measurements

This dataset contains the surfaces/regoliths in terms of elemental surface composition used in v2.0 - v2.5 of the paper collection: "Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury’s exosphere".

Inputs and outputs for exospheric simulations used in the deep neural network for surface reconstruction from simulated exospheric measurements

This dataset contains the inputs and outputs of the exospheric simulations performed for v2.0 - v2.5 of the paper collection: "Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury’s exosphere".

Inputs and outputs for the training and testing of a deep neural network for surface reconstruction from simulated exospheric measurements

This dataset contains inputs (datasets) and outputs (trainings and tests) used in v2.0 - v2.5 of the paper collection: "Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury’s exosphere".