Find research datasets worth reusing

New parameterization scheme for modeling ozone-caused damage to vegetation in process-based models: data

gmd-2024-6: Quantifying the role of ozone-caused damage to vegetation in the Earth system: A new parameterization scheme for photosynthetic and stomatal responses (Fang Li et al., 2024); https://gmd.copernicus.org/preprints/gmd-2024-6/. It includes three directory: input (O3 concentration), observations (collected PODY-An and PODY-gs), and simulations. It is co-supported by the National Natural Science Foundation of China (41875137) and Guangdong Major Project of Basic and Applied Basic Research (2021B0301030007).

Pre-processed functional data for the github repo: gecthomas/Spectral_DCM_in_PD_VH/tree/spm_course

This repository was created for the purposes of a practical demonstration for the May 2024 SPM course at UCL. This upload contains subject-level anonymised functional data that have undergone the following pre-processing: <ul> <li>first 5 volumes discarded</li> <li>spatial realingment</li> <li>unwarping</li> <li>normalisation to MNI space</li> <li>smoothing</li> <li>denoising with ICA-AROMA</li> </ul> There are also pre-processed SPM GLM files. These data are to be used in conjuction with the code in the repositry found <a href="https://github.com/gecthomas/Spectral_DCM_in_PD_VH/tree/spm_course" target="_blank" rel="noopener">here</a>. They only contain a subset of the participants included in the full analysis and are for demonstrattion purposes only.

Data for "Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression"

The dataset for the manuscript titled "Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression". <blockquote> Oh, G. and Austin, P. H.: Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-352, 2024. </blockquote>   The dataset contains input parameters used for the SAM LES model run (Khairoutdinov and Randall, 2003) based on the CGILS S2 parameters (CGILS.zip) and the main raw output used for the manuscript (clusters.zip). The latter contains a list of Parquet files for the 12-hour model run, sampled every minute. The raw output consists of the coordinates of all cloudy cells in the model domain, which is used to construct the cloud size distribution as described in the manuscript.

Supplementary Data from manuscript entitled: Cloud processing dominates the vertical aerosol profiles in marine air masses over the Great Barrier Reef

The data used in the manuscript "Cloud processing dominates the vertical aerosol profiles in marine air masses over the Great Barrier Reef" is available. It contains information from different instruments on board the research aircraft. The manuscript describes the instrumentation details. There is a read_me file in each folder with further information.

A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing

<h1>Overview</h1> This dataset is derived from the <a href="https://quijote-simulations.readthedocs.io/en/latest/bsq.html">Big Sobol Sequence (BSQ) of the Quijote simulations</a>, a collection of N-body simulations designed for machine learning applications. Each simulation consists of a point cloud (points in space, with 3D coordinates attached to them) generated under a varying set of cosmological parameters. Each point represents a simulated galaxy and is accompanied by associated properties such as velocity and mass. The cardinality of each point cloud is 5000 points. The dataset is split into 11,200 simulations in the training set, 608 in the validation set, and 576 in the test set.  <h1>File structure</h1> The dataset is provided in <a href="https://www.tensorflow.org/tutorials/load_data/tfrecord">TFRecord format</a>. The training simulations are split across 50 TFRecord files following the naming convention <code>halos_train_.tfrecord</code>. The validation and test sets are provided in <code>halos_val_1.tfrecord</code> and <code>halos_test_1.tfrecord</code>, respectively. Each dataset can be loaded using TensorFlow as shown in the code example below: <div> <blockquote> <div>import tensorflow as tf files = tf.io.gfile.glob(f"halos*train*.tfrecord") # replace 'train' with 'val' or 'test'</div> <div>dataset = tf.data.TFRecordDataset(files)</div> </blockquote> </div> <h2>TFRecord structure</h2> Each record (corresponding to a point cloud) in a TFRecord file contains the following feature fields: <ul> <li><code>"x"</code>: (tensor of shape [5000], dtype=float) - Position along x axis</li> <li><code>"y"</code>: (tensor of shape [5000], dtype=float) - Position along y axis</li> <li><code>"z"</code>: (tensor of shape [5000], dtype=float) - Position along z axis</li> <li><code>"v_x"</code>: (tensor of shape [5000], dtype=float) - Velocity along x axis</li> <li><code>"v_y"</code>: (tensor of shape [5000], dtype=float) - Velocity along y axis</li> <li><code>"v_z"</code>: (tensor of shape [5000], dtype=float) - Velocity along z axis</li> <li><code>"J_x"</code>: (tensor of shape [5000], dtype=float) - Angular momentum along x axis</li> <li><code>"J_y"</code>: (tensor of shape [5000], dtype=float) - Angular momentum along y axis</li> <li><code>"J_z"</code>: (tensor of shape [5000], dtype=float) - Angular momentum along z axis</li> <li><code>"M200c"</code>: (tensor of shape [5000], dtype=float) - Virial mass</li> <li><code>"Omega_m"</code>: (float) - Matter density</li> <li><code>"Omega_b"</code>: (float) - Baryon density</li> <li><code>"h"</code>: (float) - Hubble parameter</li> <li><code>"n_s"</code>: (float) - Density perturbation spectral index</li> <li><code>"sigma_8"</code>: (float) - RMS matter fluctuation amplitude on a scale of 8 Mpc/h</li> <li><code>"tpcf"</code>: (tensor of shape [24], dtype=float) - Two-point correlation function</li> </ul>

Raw and post-processed data from the study "Mapping the spectrotemporal regions influencing perception of French stop consonants in noise"

The current dataset contains all data needed to replicate the study from Carranante et al. "Mapping the spectrotemporal regions influencing perception of French stop consonants in noise" Use these data: <ol> <li>Download all these data, place them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="http://github.com/aosses-tue/fastACI">GitHub</a>) you can recreate the figures of our paper.</li> <li>After initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB) and then type either of the following commands, to recreate the figure you want. To recreate the figures in the main text: <pre><code>publ_carranante2024_figs('fig1','zenodo'); </code></pre> To generate the figures of the supplementary materials: <pre><code>publ_carranante2024_figs('fig1_suppl','zenodo');</code></pre> </li> </ol> References: <ul> <li>Paper not published yet</li> <li>fastACI toolbox: Alejandro Osses, Léo Varnet. fastACI toolbox: the MATLAB toolbox for investigating auditory perception using reverse correlation (v1.2). Zenodo. doi:<a href="https://doi.org/10.5281/zenodo.7314014">10.5281/zenodo.7314014</a>. Supplement to: <a href="http://github.com/aosses-tue/fastACI/tree/v1.2">https://github.com/aosses-tue/fastACI/tree/v1.2</a></li> </ul>

Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression

Open the record for dataset details and reuse information.

Appendix to "Process Mining Pipelines with Controlled Sharing of Data and Algorithms"

Abstract: Process mining leverages execution traces within an organisation's IT systems to gain insights into its processes. Despite being a mature discipline in academia and industry, setting up process mining pipelines is still a complex task and involves programming, manual steps, and considerations of privacy and intellectual property. This paper introduces a platform based on a distributed architecture that helps define, deploy, and execute process mining pipelines across organisations. The requirements for this distributed architecture and platform are derived from a set of process mining scenarios, whose relevance is validated through a survey. Furthermore, this paper introduces a prototype for an initial version of the platform, demonstrating feasibility and supporting the specified requirements. This development is a major step in advancing process mining, offering simpler and more efficient ways of implementing and managing complex process mining pipelines on a larger scale. Description: This dataset presents the support for non-functional requirements identified in the paper "Process Mining Pipelines with Controlled Sharing of Data and Algorithms" by existing process mining platforms. Legend: Green cells indicate complete fulfilment. Yellow indicates partial fulfilment. Blue cells indicate uncertain fulfilment. Red indicates no fulfilmnet.

Processed data for the manuscript: Promoting Multi-Task Learning as a General Approach for Deep-Learning-based Hydrological Models

<div> <div>Below is a brief overview of the processed data in this repository:</div> <div>- camels_streamflow: This directory contains streamflow data for CAMELS basins covering the period from January 1, 2015, to December 31, 2021. We have not included the original CAMELS dataset, which contains attributes, meteorological forcing, and streamflow data from January 1, 1980, to December 31, 2014, as it can be easily downloaded from the CAMELS website (https://gdex.ucar.edu/dataset/camels.html) and is too large for us to upload to Zenodo.</div> <div>- modiset4camels: This directory includes multiple versions of basin-mean Evapotranspiration (ET) data retrieved from the MOD16A2 data product. The dataset spans from January 1, 2001, to December 31, 2021, with an 8-day temporal resolution.</div> <div>- nldas4camels: This directory contains basin-mean daily meteorological forcing data from the NLDAS-2 dataset, obtained via Google Earth Engine (GEE). The dataset covers the period from January 1, 2001, to December 31, 2021.</div> <div>- smap4camels: This directory features basin-mean Soil Moisture (SSM) data from the NASA-USDA Enhanced SMAP Global Soil Moisture dataset, covering the period from April 2, 2015, to October 3, 2021. The dataset provides SSM measurements at a 5 cm depth. Additionally, we provide basin-mean daily SMAP L4 data spanning from April 1, 2015, to December 31, 2023.</div> </div>

Processed single cell lienage data in scLTdb

Single-cell lineage tracing (scLT) is a powerful technique that integrates DNA barcoding systems with single-cell sequencing technologies. This approach enables the simultaneous measurement of cell lineage and transcriptome or epigenomic profiles at the single-cell level. scLT technologies have been extensively employed in studying lineage relationships and cell fate heterogeneity across various biological systems. However, there is no comprehensive database available to users for searching, analyzing, visualizing, and downloading scLT data. Therefore, we developed scLTdb (scltdb.com), the first scLT database, containing 106 datasets across 36 scLT technologies. The database encompasses approximately 2.7 million cells across 13 tissues from 3 species. All datasets in scLTdb were manually curated and analyzed through a uniform workflow. scLTdb provides users with three distinct online modules designed specifically for visualizing and analyzing different modalities of scLT datasets. These modules include the Single Cell Module, which focuses on the analysis of the single cell modality; the Barcode Module, which is dedicated to the analysis of clone modality; and the Integrative Module, which facilitates the analysis of both single cell and clone modalities. Furthermore, scLTdb provides users with online tools to analyze user-provided scLT data. With these features, scLTdb aims to serve as a highly versatile platform, enabling users to conveniently explore and analyze published scLT datasets. Ultimately, scLTdb will play a vital role in promoting the advancement of modern biology.

Code and data for Oxygen dimerization as a defect-driven process in bulk LiNiO2

Data and code required to generate figures in the article "Oxygen dimerization as a defect-driven process in bulk LiNiO2". Available as a preprint at 10.26434/chemrxiv-2024-lcmk

opencc-by-4.0Feb 2024View details →

Processed Seurat objects from scRNA-seq data of the aging subventricular zone (SVZ) neurogenic niche with partial reprogramming

This repository contains the processed Seurat objects from the publication "Restoration of neuronal progenitors by partial reprogramming in the aged neurogenic niche" (https://doi.org/10.1038/s43587-024-00594-3). Raw sequencing data is available at the Gene Expression Omnibus (GEO) under accession number GSE224438. Code used to process and analyze the data is available on GitHub (https://github.com/gitlucyxu/SVZreprogramming).  These Seurat objects are filtered to high-quality singlets for samples included in the publication. Descriptions and notable metadata: <ul> <li>svz_iOSKM_cohort1_toshare.rds - SVZ after whole-body partial reprogramming, cohort 1 <ul> <li>Celltype - cell type annotation</li> <li>Treatment - condition <ul> <li>untr: old control</li> <li>2Dox0: old+OSKM</li> </ul> </li> <li>hash.ID - mouse ID (biological replicate)</li> </ul> </li> <li>svz_iOSKM_cohort2_toshare.rds - SVZ after whole-body partial reprogramming, cohort 2 <ul> <li>Celltype - cell type annotation</li> <li>Age_Treatment - condition <ul> <li>young_untr: young control</li> <li>old_untr: old control</li> <li>old_2Dox0: old+OSKM</li> </ul> </li> <li>hash.ID - mouse ID (biological replicate)</li> </ul> </li> <li>svz_ciOSKM_toshare.rds - SVZ after SVZ-targeted partial reprogramming <ul> <li>Celltype - cell type annotation</li> <li>Age_Treatment - condition <ul> <li>young_untr: young control</li> <li>old_untr: old control</li> <li>old_Dox: old+OSKM(SVZ)</li> </ul> </li> <li>MULTI_classification_rescued - mouse ID (biological replicate)</li> </ul> </li> </ul>   Updated 2024/07/01 (v2): replaced a corrupted file.

opencc-by-4.0Jan 2024View details →

Replication data for: Processed food intake assortativity in the personal networks of older adults

This is the replication data for the scientific paper titled "Processed food intake assortativity in the personal networks of older adults." For details on how to use the data files, please consider the "Supplementary Material" file and the paper preprint where the context of the study and the variables of interest are presented.

opencc-by-nc-4.0Dec 2023View details →

Data for: Exploring Emerging Social Media: Acquiring, Processing, and Visualizing Data with Python and OSoMe Web Tools

Data collected from Bluesky and Mastodon via streaming covering the period between 2024-06-25 and 2024-07-02. Entries contains any the of following terms: biden, trump, or debate. Data also contains embedding precalculated for each dataset. The data also contains embeddings pre calculcated for the datasets.

opencc-by-4.0Jul 2024View details →

Statistical data - Assessing the State and Impact Towards Just Transition Process in the Energy Sector in North Macedonia

The dataset represents statistical data collected and interpreted by the authors in the framework of the research titled 'Assessing the State and Impact Towards Just Transition Process in the Energy Sector in North Macedonia'. The research was developed in the framework of the <a href="https://greenforcetwinning.net/">GreenFORCE</a> project and is available under Deliverables 4.5_1st Research Study Report; and 4.6_2nd Research Study Report.    The dataset contains tables and graphs that are employed within the research and may be accessed by the general public in compliance with the Creative Commons Attribution license. The datasheets include:  1- Primary energy production data 2005-2022  2- Energy balance 2018-2022 3- Electricity production by domestic producers 2017-2022 4- GDP by production approach 2017-2022 5- GDP components 2019-2021 6- BDP components 2019 7- GDP per capita 2015-2020 8- BDP per sector and region 2021 9- Employment per sector and regions 2017-2021 10- Population data 2021 11- Input-Output analysis data 12- Environmental Investment statistics 13- GHG emissions (+ projections) 2015-2025 14- Environmental taxation (2016-2021)

dryad32/100

Data from: A case-control study evaluating CT signs of xiphoid process associated with xiphodynia

Objectives:To investigate whether CT signs of the xiphoid process, such as xiphisternal angle and evidence of soft tissue compression, are useful for diagnosing xiphodynia. Design:A case-control study within a cohort. Setting/Participants:Participants included 1560 individuals who visited a small urban hospital in Japan for chest or abdominal pain between January 2021 and September 2023. Those who underwent CT examinations including the xiphoid process were selected. Nine individuals diagnosed with xiphodynia were assigned to the study group, while 321 individuals diagnosed with other causes of pain were assigned to the control group. Interventions:The xiphisternal angle, evidence of soft tissue compression anterior to the xiphoid process, anatomical features at the tip of the xiphoid process, and anatomical morphology of the xiphoid process were compared between the two groups. Results:There was no significant difference in the xiphisternal angle between the two groups. No significant differences were observed in evidence of soft tissue compression anterior to the xiphoid process or anatomical features at the tip of the xiphoid process. New anatomical signs reveal that in approximately 70% of cases, the xiphoid process curves forward and then backward.Xi Conclusions:The xiphoid process sternal angle is not useful for diagnosing xiphodynia. The curvature of the xiphoid process is frequently observed regardless of the presence of xiphodynia.

opencc-zeroJul 2024View details →

opencc-by-4.0Jul 2024View details →

Assisted Data Annotation for Business Process Information Extraction from Textual Documents

Open the record for dataset details and reuse information.

opencc-by-4.0Mar 2018View details →

Apache POI pre-processed data for the first DocGen challenge at DySDoc 3

Apache POI pre-processed data for the first DocGen challenge The pre-processed data for First Software Documentation Generation Challenge (DocGen), hosted at the <a href="https://dysdoc.github.io/">Third International Workshop on Dynamic Software Documentation (DySDoc 3)</a>, includes the following datasets for <a href="https://poi.apache.org/">Apache POI 3.17</a>: Call graph between method and classes. File: call-graph-poi-3.17-all.zip CSV file with the call graph between methods and between classes. Class A calls class B if there exists a call between amethod of class A and a method of class B. The call graph was produced by the tool <a href="https://github.com/gousiosg/java-callgraph/">java-callgraph</a>.  The CSV file contains the following columns: <ul> <li>call_type: call between (C)lasses or (M)ethods</li> <li>caller: the Fully Qualified Name (FQN) of the caller</li> <li>method_call_type: the type of method call: <ul> <li>M for invokevirtual calls</li> <li>I for invokeinterface calls</li> <li>O for invokespecial calls</li> <li>S for invokestatic calls</li> <li>D for invokedynamic calls</li> </ul> </li> <li>callee: the FQN of the callee</li> </ul> For more details about the format and each type of method call, check the tool <a href="https://github.com/gousiosg/java-callgraph/">README</a>. Inheritance hierarchy File: poi-3.17-inheritance.zip A CSV file with the inheritance hierarchy of POI, which was extracted using bcel 6.2 The CSV file contains the following columns: <ul> <li>record_id: sequential number</li> <li>parent_class: the parent class</li> <li>child_class: the child class</li> <li>relationship_type: the type of relationship between classes, i.e., the child class 'extends' or 'implements' the parent class</li> </ul> Issues File: bugzilla-poi-dump.zip CSV file with the list of issues of <a href="https://bz.apache.org/bugzilla/buglist.cgi?product=POI">Apache POI</a> (timestamp: Tue Feb 27, 2018, 18.41.40 UTC) The CSV file contains the following columns: <ul> <li>record_id: sequential number</li> <li>issue_id: the ID that identifies the issue in the issue tracker</li> <li>issue_url: the URL of the issue in the issue tracker</li> <li>issue_title: the title of the issue</li> <li>xml_path: the path to the XML of the issue, which contains all the issue information provided by the issue tracker</li> </ul> All the issues in XML format can be found in the "poi" folder in the ZIP file Commits File: poi-commits.zip A JSON file with commit information for POI 3.17 (until revision 219dff00e6, on Sept. 8, 2017). The information was extracted using the tools <a href="https://dl.acm.org/citation.cfm?doid=2024445.2024463">Historage</a> and <a href="https://dl.acm.org/citation.cfm?doid=2597073.2597125">Kataribe</a>. For each commit, we provide: <ul> <li>Commit hash</li> <li>Parent commit hash (if exists)</li> <li>Commit message</li> <li>Commit time</li> <li>Committer name</li> <li>Method-level changes (addition/deletion/modification/renaming and method FQN). <ul> <li>The FQN contains information about the class (CN) and method (MT) or constructor (CS)</li> </ul> </li> </ul> StackOverflow posts File: apache-poi-SO.zip JSON file with all 6,299 Stack Overflow threads with the <code>apache-poi</code> tag,

opencc-by-4.0Apr 2018View details →

Data supplementing article "Role of baroclinic processes on flushing characteristics in a highly stratified estuarine system, Mobile Bay, Alabama" submitted to Journal of Geophysical Research: Oceans

The dataset uploaded includes the model inputs and outputs, as well as the time-series of dye mass for each of the 16 numerical experiments that are tested in this study.