Find research datasets worth reusing

opencc-by-4.0Sep 2022View details →

AnnData object for case study tutorials

This dataset works for the Filter, Plot & Explore tutorial: https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.html

opencc-by-4.0Nov 2021View details →

Data for 'Affectedness and Differential Object Marking in Turkish and Uzbek', published in Linguistics

Data (CSV) and scripts (R) underlying the analysis presented in 'Affectedness and Differential Object Marking in Turkish and Uzbek', published in Linguistics; see the publication for details.

opencc-by-4.0Dec 2021View details →

Dataset for paper "Do Performance Aspirations Matter for Guiding Software Configuration Tuning? An Empirical Investigation under Dual Performance Objectives"

This contains the dataset matched with the code on the GitHub page: https://github.com/ideas-labo/aspiration-study.

opencc-by-4.0May 2021View details →

The Grammatical Nature of Sino-Vietnamese "Verb-Object Compounds"

This study investigates the grammatical nature of Sino-Vietnamese words borrowed from “verb-object compounds”. I selected 47 verb-object compounds borrowed into Vietnamese as target and investigated whether they maintain their dual identities (i.e., word and phrase) as in Chinese. The results show that functional words cannot be inserted between the constituents of the target words with one exception, and that a number of target words can be followed by an object. Based on the results, I argue that Sino-Vietnamese “verb-object compounds” have almost lost their phrasal identity and that some items have been reinterpreted as transitive verbs.

opencc-by-4.0Dec 2021View details →

Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images

Abstract: Invasive alien species reduce biodiversity. In southern Brazil, the genus Pinus is considered invasive, and its dispersal by humans has resulted in this species reaching ecosystems that are more sensitive and less suitable for cultivation, as it was the case for the restingas on the island of Santa Catarina. Invasion control requires persistent efforts to identify and treat each new invasion case as a priority. In this study, areas invaded by Pinus sp. in restingas were mapped using images taken by a remotely piloted aircraft system (RPAS, or drone) to identify the invasion areas in great detail, enabling management to be planned for the most recently invaded areas, where management is simpler, more effective, and less costly. Geographic object-based image analysis (GEOBIA) was applied on images taken from a conventional RGB camera embedded in a RPAS, which resulted in a global accuracy of 89.56%, a mean kappa index of 0.86, and an F-score of 0.90 for the Pinus sp. Processing was conducted with open source software to reduce operational costs.

Seurat object subset of mouse liver scRNAseq data (Guilliams et al., Cell 2022)

Seurat object containing a subset of the mouse liver scRNAseq data (Guilliams et al., Cell 2022) Data used only for demonstration purpose. Namely, to demonstrate the Differential NicheNet pipeline: https://github.com/saeyslab/nichenetr/blob/master/vignettes/differential_nichenet.md

openother-openSep 2021View details →

MaskUKF: An Instance Segmentation Aided Unscented Kalman Filter for 6D Object Pose and Velocity Tracking - Accompanying Data

Dataset and evaluation data associated to the publication "MaskUKF: An Instance Segmentation Aided Unscented Kalman Filter for 6D Object Pose and Velocity Tracking".

Data for manuscript on objective phenotyping of alfalfa roots

These are images for the manuscript, "Objective phenotyping of root system architecture using image augmentation and machine learning in alfalfa." Alfalfa_root_ML_segmented.zip contains the root images segmented from the background using RootPainter and that were used as input for RhizoVision Explorer. Alfalfa_root_ML_color_photographs.zip contains the original color photographs of the roots, except that the circular scale and ID tag have been cropped out and filled with black to aid image analysis.

Variable Message Signal annotated images for object detection

If you use this dataset, please cite this paper: Puertas, E.; De-Las-Heras, G.; Sánchez-Soriano, J.; Fernández-Andrés, J. Dataset: Variable Message Signal Annotated Images for Object Detection. Data 2022, 7, 41. https://doi.org/10.3390/data7040041 This dataset consists of Spanish road images taken from inside a vehicle, as well as annotations in XML files in PASCAL VOC format that indicate the location of Variable Message Signals within them. Also, a CSV file is attached with information regarding the geographic position, the folder where the image is located, and the text in Spanish. This can be used to train supervised learning computer vision algorithms, such as convolutional neural networks. Throughout this work, the process followed to obtain the dataset, image acquisition, and labeling, and its specifications are detailed. The dataset is constituted of 1216 instances, 888 positives, and 328 negatives, in 1152 jpg images with a resolution of 1280x720 pixels. These are divided into 576 real images and 576 images created from the data-augmentation technique. The purpose of this dataset is to help in road computer vision research since there is not one specifically for VMSs. The folder structure of the dataset is as follows: <ul> <li>vms_dataset/ <ul> <li>data.csv</li> <li>real_images/ <ul> <li>imgs/</li> <li>annotations/</li> </ul> </li> <li>data-augmentation/ <ul> <li>imgs/</li> <li>annotations/</li> </ul> </li> </ul> </li> </ul> In which: <ul> <li>data.csv: Each row contains the following information separated by commas (,): image_name, x_min, y_min, x_max, y_max, class_name, lat, long, folder, text.</li> <li>real_images: Images extracted directly from the videos.</li> <li>data-augmentation: Images created using data-augmentation</li> <li>imgs: Image files in .jpg format.</li> <li>annotations: Annotation files in .xml format.</li> </ul>

Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps - data

Data from the article Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps published in Journal of Vision. code: https://zenodo.org/record/5999215 data: https://zenodo.org/record/5999046 Marek A. Pedziwiatr marek.pedziwi@gmail.com February 2022

Dataset for "Evaluation Methods and Replicability of Software Architecture Research Objects"

# Content In this package, please find the  following content: * Investigated Papers.bib     A BibTeX file with all papers investigated in the paper "Evaluation Methods and Replicability of Software Architecture Research Objects" * Raw-Data-Table Content-Data.html and Raw-Data-Table Meta-Data.html     Tables with the raw data as extracted during the systematic literature review * Colection of Data Visualizations.pdf     Multiple visualizations of the raw data for analysis. A copy of summary.pdf as described below. * Data and Visualization     Contains:     - The data as CSV files,     - scripts for creating visualizations         - *.awk -- Awk scripts are used to create the corresponding of the *.csv files in data         - *.rb -- Ruby scripts to build the respective figures in figs as *.tex files         - make-all.sh -- A script to call all other scripts for creating diagrams and the summary         - make-paper-figures.sh -- A script to build "paper-figures.pdf" with all diagrams used in the accompanying paper     - A documentation of the contained scripts (Data and Visualization/README.md)     - summary.pdf -- A collection of diagrams (as built by make-all.sh)     - paper-figures.pdf -- A collection of all diagrams as used in the accompanying paper (as built by make-paper-figures.sh and make-all.sh) * Wiki/     A copy of the wiki used during data extraction.     Constains:     - descriptions of all data items     - the process description     - the taxonomy used for data extraction # Reproduction You can reproduce the visualizations with the following commands in a UNIX command line environment. > cd "Data and Visualization" > ./make-paper-figures.sh > ./make-all.sh The requirements are: * A UNIX command line environment (e.g., bash) with awk installed * Ruby (>2.5) * latex (e.g., tex-live) The command "./make-paper-figures.sh" produces the file “paper-figures.pdf”, which contains all diagrams that are used in the paper. The command "./make-all.sh" produces the file "summary.pdf", which contains diagrams used for data analysis, and the file "paper-figures.pdf". All figures describing the results in the paper are also in "summary.pdf". These commands each take about 2 ("/make-paper-figures.sh") / 8 ("./make-all.sh") minutes to run on current standard laptop (Intel i5-8250U, 16 GB memory). Calling the commands produces many log statements (information and warnings), which show the progress and can be ignored.

artificial dataset of small engine parts for object detection-segmentation

This dataset contains images and masks of parts used in engine assembly. The images were generated artificially from CAD models using gazebo simulator. The dataset consists of three classes: Large Bolt, Small Bolt and Rocker Arm. Annotations are represented as mask images with the same name as corresponding RGB images. 1080 images for each class, 3240 images total. Resolution of each image: 640x480 pixels. CAD models for each part are also included.

DLOFTBs - Deformable Linear Objects Tracking with B-splines - videos

Videos of the experiments described in the paper "DLOFTBs – Fast Tracking of Deformable Linear Objects with B-splines". Includes 2 videos: <ul> <li>3d_real.mp4 - 3D tracking of real-life DLO</li> <li>3d_art.mp4 - 3D tracking of artificially generated DLO</li> </ul>

opencc-byFeb 2022View details →

Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder

This is the public dataset of synthetic trajectories and MOT17-3T, which is used in paper <a href="https://arxiv.org/abs/2202.09315">Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder</a>. The source code can be found <a href="https://gitlab.inria.fr/xilin/dvae-umot-release">here</a>.

opencc-by-4.0Apr 2022View details →

BIM Learning Objects

BIM Learning Objects.

opencc-by-4.0Jun 2022View details →

Customizable Visualization of Quality Metrics for Object-Oriented Variability Implementations - Artifact

This archive contains all the README files documenting the artifact (<a href="https://doi.org/10.5281/zenodo.6644449">https://doi.org/10.5281/zenodo.6644449</a>) to reproduce the results presented in the "Customizable Visualization of Quality Metrics for Object-Oriented Variability Implementations" paper presented at SPLC 2022. It also contains: <ul> <li>the codebases of JFreeChart before and after the maintenance actions presented in section 5.2, with the corresponding diff file, as well as excerpts of the SonarQube analysis of both projects showing the information presented in table 3;</li> <li>the Excel file used to obtain the data presented in the table 2.</li> </ul> An exhaustive description of the archive can be found in the README.md file.

opencc-by-4.0Jun 2022View details →

Processed Seurat Object of scRNAseq data from wildtype and CaMKK2 KO immune infiltrate of CT2a preclinical murine glioma

This repository contains the processed Seurat objects generated from the raw data deposited at the Gene Expression Omnibus (GEO) under GSE197879. Details about the experiment and sequencing are available under GSE197879. Information on how the Seurat objects were created can be found in this GitHub repository https://github.com/wht10/CT2A_scRNAseq_CaMKK2KOvWT . Notable metadata within each Seurat object: 1. Processed_CD45_Live_Fig2b.rds <ul> <li>Genotype - whether the cell is from a WT or CaMKK2 KO mouse</li> <li>HTO_maxID - The biological replicate that the cell came from (4 biological replicates per genotype)</li> <li>MouseID - A concatenation between the genotype and HTO_maxID, providing a unique identifier for each biological replicate</li> <li>Cell.Type - The cell type annotations for each cell. Can be assigned to "Idents()" to change the name of the cell identities.</li> <li>Geno.Ident - A concatenation between Genotype and Cell.Type. By re-assigning this to "Idents()"  "FindMarkers()" can be used to investigate differentially expressed genes within a cell-type between genotypes. </li> </ul> 2. Reclustered_TILs_Fig3a.rds <ul> <li>Genotype - whether the cell is from a WT or CaMKK2 KO mouse</li> <li>HTO_maxID - The biological replicate that the cell came from (4 biological replicates per genotype)</li> <li>MouseID - A concatenation between the genotype and HTO_maxID, providing a unique identifier for each biological replicate</li> <li>Celltype - The cell type annotations for each cell. Can be assigned to "Idents()" to change the name of the cell identities.</li> <li>Geno_Ident - A concatenation between Genotype and cell-type. By re-assigning this to "Idents()"  "FindMarkers()" can be used to investigate differentially expressed genes within a cell-type between genotypes. </li> </ul>

opencc-by-4.0Mar 2022View details →

Additional data for 'High-speed acoustic holography with arbitrary scattering objects'

Raw data used to create the plots in the paper, specifically for (a) Fig. 2a, (b) Figs. 2c, S6a and S6b, (c) Fig. S5, (d) Fig. S7, (e) Fig. S8, (f) Fig. S8b, (g) Figs. S9a and S9b, (h) Fig. S11b, and (i) Fig. S12b.

Data in support of "Self-adaptive multi-objective climate policies align mitigation and adaptation strategies"

The simulation model, the characterization of uncertainties, the calibration of the surrogate econometric damages model, and the simulation version of the FAIR climate model are available together with the optimization outputs and the scripts for replicating the figures of the paper.