Find research datasets worth reusing

opencc-by-4.0Feb 2023View details →

Chateau Champs - very long focal length dataset for Structure from Motion algorithms

This dataset contains a photogrammetric acquisition (99 images) of a sculpture head located in Château de Champs-sur-Marne, France. The images were taken with the full-frame Canon EOS 5D Mark II and a focal length of 1000mm.

PlasBin-flow: A flow-based MILP algorithm for plasmid contigs binning

PlasBin-flow is a method for detecting plasmid contigs bins from the assembly graph for a given bacterial sample. The method is based on a Mixed-Integer Linear Programming (MILP) formulation. The data shared consists of output files of PlasBin-flow as well as those of 5 other plasmid binning methods, namely, PlasBin, HyAsP, MOB-recon, plasmidSPAdes and gplas for 66 test samples. Details about each sample have been provided in the file samples.csv.    The output folder contains one folder per sample. Each sample folder contains the following files:   <ul> <li>PlasBin-flow was executed using 7 different weight combinations for the objective function of the MILP. For every weight combination \((a,b,c)\) , we have a file name plasbin_flow_a_b_c_bins.out.</li> <li>PlasBin: plasbin_contig_chains.csv,</li> <li>HyAsP: hyasp_plasmid_contigs.fasta,</li> <li>MOB-recon: mob_recon_contig_report.txt,</li> <li>plasmidSPAdes: plasmidspades_contigs.fasta,</li> <li>gplas: gplas_bins.tab.</li> </ul>

Instances and detailed results for the whole testbed of "The Storage Location Assignment and Picker Routing Problem: A Generic Branch-Cut-and-Price Algorithm"

This repository contains the instances and detailed results used for the computational experiments in the article "The Storage Location Assignment and Picker Routing Problem: A Generic Branch-Cut-and-Price Algorithm". Two sets of instances are used: The first set of instances comes from the paper "Integrating storage location and order picking problems in warehouse planning" authored by Allyson Silva, Leandro C. Coelho, Maryzam Darvish and Jacques Renaud. https://doi.org/10.1016/j.tre.2020.102003 Their instances are available on the following website: https://www.leandro-coelho.com/slot-assignment-and-order-picking/ The second set of instances comes from the paper "Storage assignment for newly arrived items in forward picking areas with limited open locations" authored by Xiaolong Guo, Ran Chen, Shaofu Du and Yugang Yu. https://doi.org/10.1016/j.tre.2021.102359 The set of small instances is made available on this repository, with the kind permission of the authors.

Datasets describing optimization the cutting regime in the turning of AISI 316L steel based on the NSAG-II and NSAG-III multicriteria algorithms.

This work shows the multi-criteria data analysis of the dry and MQL turning process of AISI 316L steel using the evolutionary algorithms of non-dominant class II and III (NSAG-II and NSAG-III). The wear of the cutting tool (VB), the energy consumption (E) and the machining time (t) are used as analysis variables, with the aim of minimizing the wear of the cutting tool based on the optimal selection of parameters. When comparing the results obtained from both methods, we found that NSAG-III was the best alternative for selecting parameters in the turning of specimens, with fewer tool wear and more efficient use of energy consumption. Interpretation of this data

opencc-byMay 2023View details →

Evaluation of a simple score-based Natural Language Processing (NLP) algorithm: Intermediary Result

The intermediary result of the experiment "Evaluation of a simple score-based Natural Language Processing (NLP) algorithm".

opencc-byMay 2023View details →

Evaluation of a simple score-based Natural Language Processing (NLP) algorithm: Category Confusion Matrix

Resulting category confusion matrix for the experiment "Evaluation of a simple score-based Natural Language Processing (NLP) algorithm".

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Evaluation of a simple score-based Natural Language Processing (NLP) algorithm: Result

The result for the experiment "Evaluation of a simple score-based Natural Language Processing (NLP) algorithm".

opencc-by-4.0May 2023View details →

Exploring the Diagnostic Markers of Essential Tremor: A study based on Machine Learning Algorithms

supplementary information files for the manuscript Exploring the Diagnostic Markers of Essential Tremor: A study based on Machine Learning Algorithms

opencc-by-4.0May 2023View details →

Biometeorological Dataset for 'Novel algorithms for high resolution prediction of canopy evapotranspiration in grapevine'

A head trained Vitis vinifera L. cv. Zinfandel vine was grafted on St. George rootstock (V. rupestris) then planted in a 1.1 m3 plastic container filled with Yolo County, CA sourced sandy loam. To estimate evapotranspiration, we measured the wind speed, air temperature and relative humidity in vine canopies by mounting each vine with a suite of research grade sensors. We measured wind speed (units m ᐧ s-1) inside the vine canopy using a single needle anemometer (East 30 Sensors; Pullman, WA) that took instantaneous wind speed measurements every 10 seconds and recorded the average of the previous 12 instantaneous measurements for every 2-minute interval. We measured temperature (units oC) and relative humidity (units %) using HMP60L sensors (Campbell Scientific; Logan, UT) mounted both inside and outside of each vine canopy and recorded instantaneous measurements at each 2-minute interval. We filtered all biometeorological data using a 3-hour moving average to remove noise without causing any significant over or under-approximation of daily maxima and minima. We automated all data collection using two CR1000 data loggers (Campbell Scientific; Logan, UT), with 1 or 2 vines and associated sensors per logger, using custom CR1 programs.  A single 30W solar cell and 12V lead acid battery powered the entire vine-sensor system. This dataset represents all sensor data from a single vine, as measured in August 2020. Columns are named accordingly and include units. Please Note: The column named 'load_cell_kg' is not named accurately. The values given are in units of millivolts, and need to be translated from millivolts to kilograms. The 2020 calibration coefficient is 0.00330693663 millivolts per kilogram.

opencc-by-4.0Jun 2023View details →

Challenges and Limitations in the Design and Implementation of Fair and Equitable Machine Learning Algorithms in Healthcare

We provide the programs in Python, a CSV file with references, images used in the article and a text corpus generated with Python.

opencc-byJun 2023View details →

Quasar Factor Analysis – An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis

Dataset used in Quasar Factor Analysis – An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis [<a href="https://arxiv.org/abs/2211.11784">arXiv: 2211.11784</a>]. This dataset will be helpful to validate different continuum prediction model and study absorption systems. The descriptions of individual files can be found here: <ul> <li><a href="/api/files/01c3b414-7572-4a24-9c67-fbab6ae05969/sdss-dr16.tar.gz?versionId=427c1aca-6f12-4e84-b91e-f9a3d678329e">sdss-dr16.tar.gz</a> : continuum prediction for ~100,000 quasar spectra from SDSS DR16, see Section 3.1 in <a href="https://arxiv.org/abs/2211.11784">arXiv:211.11784</a>;</li> <li><a href="https://zenodo.org/api/files/01c3b414-7572-4a24-9c67-fbab6ae05969/sdss-mock-with-dla-with-perturb.tar.gz?versionId=55bc466d-4cbf-43ba-9b83-209ffba4ec30">sdss-mock-with-dla-with-perturb.tar.gz </a> : ~150,000 mock quasar spectra to validate QFA performance with perturbation on  quasar continuum from PCA template, see Section 3.2 in <a href="https://arxiv.org/abs/2211.11784">arXiv:211.11784</a>;</li> <li><a href="https://zenodo.org/api/files/01c3b414-7572-4a24-9c67-fbab6ae05969/sdss-mock-with-dla-without-perturb.tar.gz?versionId=efa5d05a-2a40-4a43-801e-c95b29a4efb2">sdss-mock-with-dla-without-perturb.tar.gz</a>: ~150,000 mock quasar spectra to validate QFA performance with quasar continuum directly from PCA template, see Section 3.2 in <a href="https://arxiv.org/abs/2211.11784">arXiv:211.11784</a>.  </li> </ul>

opencc-by-4.0Jul 2023View details →

MAGARA: A Multi-Angle Geostationary Aerosol Retrieval Algorithm

MAGARA, NOAA bias-corrected, and AERONET datasets used for analysis of manuscipt titled "MAGARA: A Multi-Angle Geostationary Aerosol Retrieval Algorithm".

opencc-by-4.0Aug 2023View details →

Measured data of global fractional vegetation cover from 2013-2021 and algorithm code for calculating remote sensing products

These data come from "A new computationally efficient algorithm to generate global fractional vegetation cover from Sentinel-2 imagery at 10m resolution", these include: 1.  Measured data of global fractional vegetation cover from 2013-2021  2.   Algorithm code for calculating fractional vegetation cover, these codes are written by JavaScript in GEE (Google Earth Engine).

Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms - Datasets

Datasets for reproducibility of the results found in Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms. This study utilized data from the following 4 journals: Lake, B.B. et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat Commun 10, 2832 (2019). Liao, J., Yu, Z., Chen, Y. et al. Single-cell RNA sequencing of human kidney. Sci Data 7, 4 (2020). Menon, R. et al. Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker. JCI Insight 5, e133267 (2020). Wu, H. et al. Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response. J Am Soc Nephrol 29: 2069–2080 (2018). Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).

Automatic message sequence chart creation from simulation run of the Chandy-Lamport algorithm modeled by colored Petri net

Videos of two message sequence chart creation from simulation runs of the Chandy-Lamport algorithm modeled by colored Petri net using the CPN tool. Message Sequence Chart Of Model With Automatic Simulation Run_SuppInfo.mp4: This video shows the automatic generation of a message sequence chart of a proposed colored Petri net model of the Chandy-Lamport distributed global snapshot algorithm using the CPN tool version 4.0.1. The video has been generated by the authors' updated extension server of the CPN tool. The automatic simulation run of the model has been used to create this video. The CPN tool randomly selects the enabled transition in an automatic simulation run. Message Sequence Chart Of Model With Step-By-Step Simulation Run_SuppInfo.mp4: This video shows the automatic generation of a message sequence chart of the proposed colored Petri net model of the Chandy-Lamport algorithm in a step-by-step simulation run with our updated extension server of the CPN tools version 4.0.1. We fired our selected enabled transition of the model to create this video.

SDS_Benchmark: a testbed for shoreline mapping algorithms using satellite imagery

This is an archived copy of the following Github repository: https://github.com/SatelliteShorelines/SDS_Benchmark

Model checking of Chandy-Lamport algorithm modeled by colored Petri net

This video shows model checking of a proposed colored Petri net model of the Chandy-Lamport distributed global snapshot algorithm using the CPN tool version 4.0.1. It shows the functions and codes written in ML language and the result of calling them used for model checking the proposed model's state space graph. The last ML code at the end of the page, named state space, does whole model checking and operates using previously displayed codes and functions. This video aimed to demonstrate the steps of our proposed model checking.

Data from: Automatic patient-level recognition of four Plasmodium species on thin blood smear by a Real Time Detector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation

Automatic patient-level recognition of four Plasmodium species on thin blood smear by a Real Time Dectector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation Emilie Guemas, Baptiste Routier, Théo Ghelfenstein-Ferreira, Camille Cordier, Sophie Hartuis, Bénédicte Marion, Sébastien Bertout, Emmanuelle Varlet-Marie, Damien Costa, Grégoire Pasquier Abstract: Malaria remains a global health problem with 247 million cases and 619,000 deaths in 2021. Diagnostic of Plasmodium species is important for administering the appropriate treatment. The gold-standard diagnosis from accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a RT-DETR (Real-Time Detection Transformer)object detection algorithm to discriminate Plasmodium species on thin blood smears images. The algorithm was trained and validated on a dataset consisting in 24,720 images from 475 thin blood smears corresponding to 2,002,597 labels. Performances were calculated with a test dataset of 4,508 images from 170 smears corresponding to 358,825labels coming from six French university hospital. At the patient level, the RT-DETR algorithm exhibited an overall accuracy of 79.4% (135/170) with a recall of 74% (40/54) and 81.9% (95/116) for negative and positive smears, respectively. Among Plasmodium positive smears, the global sensitivity was 82.7% (91/110) with a sensitivity of 90% (38/42), 81.8% (18/22) and 76.1% (35/46) for P. falciparum, P. malariae and P. ovale/vivax, respectively. The YOLOv5 model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices such as a smartphone and could be suitable for deployment in low-resource setting areas where microscopy experts are lacking. Data collection: The training and validation dataset included 24,720 pictures taken from 475 manually May Grunwald-Giemsa (MGG)-stained thin blood smears from the Montpellier University Hospital collection and for a smaller part from the Toulouse University Hospital collection. In Montpellier, the pictures were taken with a Flexcam C1 microscope camera (Leica) attached to a Leica DM 2000 microscope and Leica DF450C microscope camera adapted with a Leica DM2500 microscope at X1000 magnification. Labelling of pictures was performed manually, and then automatically with manual correction with a Computer Visual Annotation Tools (CVAT) free software. Nine categories of labels were used: white blood cells (n=3,338), red blood cells (n=1,887,781), platelets (n=48,520), Trypanosoma brucei (n=2,773), and red blood cells infected by P. falciparum (n=43,545), P. ovale (n=4,651), P. vivax (n=4,115), P. malariae (n=2,849) and Babesia divergens (n=5,142). The test dataset included 4,508 pictures taken from 170 thin blood smears from the same number of patients from the Parasitology laboratories of University Hospitals of Montpellier, Toulouse, Rouen, Lille, Nantes and Saint-Louis in Paris (Table 1). Among these 170 patients, 54 were not infected, including two patients with Howell-Jolly bodies, and 116 were infected with hematozoa. For each patient, between 20 and 30 photos were taken from one thin blood smear with at least one hematozoan parasite per picture for infected patients. Accurate species diagnostic was made by a senior parasitologist, and for recent smears, it was confirmed by specific PCR, either performed locally (Toulouse) or at the Malaria French National Reference Center (Montpellier, Saint Louis, Rouen, Lille, Nantes).