Find research datasets worth reusing

Reproducible Evaluation of Open-Source Tools for Prostate Segmentation on Public Datasets

Segmentation of the prostate and surrounding regions is important for a variety of clinical and research applications. Our goal is to evaluate the generalizability of publicly available state-of-the-art AI models on publicly available datasets. To compare the AI generated segmentations to the available manually annotated ground-truth, quantitative measures such as Dice Coefficient and Hausdorff distance, along with shape radiomics features, were analyzed. Our study also aims to show how cloud-based tools can be used to analyze, store, and visualize evaluation results.  Three open-source pre-trained AI prostate segmentation tools were evaluated against expert annotations, on three publicly available MRI prostate collections, available in NCI Imaging Data Commons[1]. Two pre-trained models originate from the nnU-Net framework[2], the last pre-trained model originates from Prostate158 paper[4]. ProstateX[5], QIN-Prostate-Repeatability[6] and PROSTATE-MRI-US-Biopsy[7]. Expert annotations of the the whole prostate gland, peripheral zone (PZ) and transition zone (TZ) of the prostate  are available for ProstateX collection, whole prostate gland and PZ for  QIN-Prostate-Repeatability collection, and whole prostate gland for PROSTATE-MRI-US-Biopsy collection. We rely on the DICOM standard to encode our segmentation and radiomics results. The DICOM standard aims to achieve interoperability and FAIR[10] principles. Encoding our results in DICOM representation allows us to leverage DICOM-reliant tools, such as Google Cloud Computing tools for storage,computation, analysis and visualization. Open-source DICOM-based visualization tools such as OHIF[8] viewer can also be used to look qualitatively at the AI and expert annotations and the referenced images.. DICOM Segmentation objects are used to encode the AI models predictions, using dcmqi[11], DICOM Structured Reports on the other hand are used to encode radiomics features[3] extracted from the AI and expert annotations, using dcmqi and highdicom[12].   This dataset is organized in three parts:  AI_SEGMENTATIONS_DICOM.zip, AI_STRUCTURED_REPORTS_DICOM.zip and EXPERT_SRUCTURED_REPORTS_DICOM..zip. All zip files contain DICOM objects only, sorted based on DICOM attributes, following this pattern: PatientID/     └───Modality-%StudyInstanceUID/           └───%SeriesInstanceUID-%SeriesDescription.dcm. AI_SEGMENTATIONS_DICOM.zip contains all the pre-trained AI models evaluated segmentation results, encoded as DICOM Segmentation objects. AI_STRUCTURED_REPORTS_DICOM..zip contains firstorder and shape radiomics features extracted for the AI segmentation results, such as Segmentation Volume, encoded as DICOM Structured Reports. EXPERT_SRUCTURED_REPORTS_DICOM.zip contains firstorder and shape radiomics features extracted for the expert annotations (for ProstateX, QIN-Prostate-Repeatability and PROSTATE-MRI-US-Biopsy collections) stored a DICOM Structured Reports objects. Code repository containing evaluation cloud-based notebooks and results/metadata .csv tables is available here: <a href="https://github.com/ImagingDataCommons/idc-prostate-mri-analysis">https://github.com/ImagingDataCommons/idc-prostate-mri-analysis</a> <h2>Additional Notes</h2>  This project has been funded in whole or in part with Federal funds from the NCI, NIH, under task order no. HHSN26110071 under contract no. HHSN261201500003l. https://portal.imaging.datacommons.cancer.gov/ nnU-Net: <a href="https://github.com/MIC-DKFZ/nnUNet">https://github.com/MIC-DKFZ/nnUNet</a> Prostate158: <a href="https://github.com/Project-MONAI/model-zoo/tree/dev/models/prostate_mri_anatomy">https://github.com/Project-MONAI/model-zoo/tree/dev/models/prostate_mri_anatomy</a> Pyradiomics: <a href="https://github.com/AIM-Harvard/pyradiomics">https://github.com/AIM-Harvard/pyradiomics</a> Highdicom: <a href="https://github.com/herrmannlab/highdicom">https://github.com/herrmannlab/highdicom</a> DCMQI: <a href="https://github.com/QIICR/dcmqi">https://github.com/QIICR/dcmqi</a> Github repo: <a href="https://github.com/ImagingDataCommons/idc-prostate-mri-analysis">https://github.com/ImagingDataCommons/idc-prostate-mri-analysis</a> <h2>Related information</h2> ProstateX - <a href="https://doi.org/10.7937/K9TCIA.2017.MURS5CL">https://doi.org/10.7937/K9TCIA.2017.MURS5CL </a> QIN-Prostate-Repeatability - <a href="https://doi.org/10.7937/K9/TCIA.2018.MR1CKGND">https://doi.org/10.7937/K9/TCIA.2018.MR1CKGND</a> PROSTATE-MRI-US-BIOPSY - <a href="https://doi.org/10.7937/TCIA.2020.A61IOC1A">https://doi.org/10.7937/TCIA.2020.A61IOC1A</a> <h2>References </h2> [1] Fedorov A, Longabaugh WJ, Pot D, Clunie DA, Pieper S, Aerts HJ, Homeyer A, Lewis R, Akbarzadeh A, Bontempi D, Clifford W. NCI imaging data commons. Cancer research. 2021 Aug 8;81(16):4188. [2] Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods. 2021 Feb;18(2):203-11. [3] Van Griethuysen JJ, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RG, Fillion-Robin JC, Pieper S, Aerts HJ. Computational radiomics system to decode the radiographic phenotype. Cancer research. 2017 Nov 1;77(21):e104-7. [4] Adams, Lisa C., Marcus R. Makowski, Günther Engel, Maximilian Rattunde, Felix Busch, Patrick Asbach, Stefan M. Niehues, et al. 2022. “Prostate158 - An Expert-Annotated 3T MRI Dataset and Algorithm for Prostate Cancer Detection.” Computers in Biology and Medicine 148 (September): 105817. [5] Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2020.A61IOC1A [6]  Fedorov, A; Schwier, M; Clunie, D; Herz, C; Pieper, S; Kikinis, R; Tempany, C; Fennessy, F. (2018). Data From QIN-PROSTATE-Repeatability. The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2018.MR1CKGND [7]  Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2020.A61IOC1A [8] Open Health Imaging Foundation Viewer: An Extensible Open-Source Framework for Building Web-Based Imaging Applications to Support Cancer Research. Erik Ziegler, Trinity Urban, Danny Brown, James Petts, Steve D. Pieper, Rob Lewis, Chris Hafey, and Gordon J. Harris [9] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. Journal of digital imaging. 2013 Dec;26(6):1045-57. [10] Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data. 2016 Mar 15;3(1):1-9. [11] Herz C, Fillion-Robin JC, Onken M, Riesmeier J, Lasso A, Pinter C, Fichtinger G, Pieper S, Clunie D, Kikinis R, Fedorov A. DCMQI: an open source library for standardized communication of quantitative image analysis results using DICOM. Cancer research. 2017 Nov 1;77(21):e87-90. [12] Bridge CP, Gorman C, Pieper S, Doyle SW, Lennerz JK, Kalpathy-Cramer J, Clunie DA, Fedorov AY, Herrmann MD. Highdicom: A python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology. Journal of Digital Imaging. 2022 Aug 22:1-9.

Dataset used in publication titled " Dependence of climate and carbon cycle response in net zero emission pathways on the magnitude and duration of positive and negative emission pulses"

Essential model data use to produce figures and tables for the publication titled " Dependence of climate and carbon cycle response in net zero emission pathways on the magnitude and duration of positive and negative emission pulses"

opencc-by-4.0Mar 2024View details →

Annotated Dataset for Bilingual Code-Mixed English-Malay Sentiment Analysis and Sarcasm Detection in Public Security Domain

Tweets from X, and post with comment from TikTok was acquired from 11 September until 21 September 2022. Data from both platforms was merged and selected. Three annotators manually labelling the selected data for sentiment and sarcasm. Sentiment labels are ‘positive’, ‘negative’, and ‘neutral’. Sarcasm label is ‘sarcastic’ and ‘not sarcastic’. Majority voting is considered for each label. Language identification label produced for each data.

Dataset on urinary concentrations of bisphenols from public servants in Galicia (NW Spain)

Dataset of concentrations of bisphenols measured in urine of public servants from Galicia (NW Spain). Data is provided in MS Excel (xlsx) and CSV formats and contains information on individual sociodemographic (questionnaire anonymized) data, concentrations and translation to human intake. Further details are provided in the associated publication: A. Estévez-Danta, R. Rodil, J.B. Quintana, R. Montes. Determination of the urinary concentrations of six bisphenols in public servants by online solid-phase extraction-liquid chromatography-tandem mass spectrometry. Analytical and Bioanalytical Chemistry 2024, In press. DOI: 10.1007/s00216-024-05386-7. <a href="https://doi.org/10.1007/s00216-024-05386-7" target="_blank" rel="noopener">https://doi.org/10.1007/s00216-024-05386-7</a> <div>The data is deposited in ZENODO:</div> <div><a href="../doi/10.5281/zenodo.10477935">https://zenodo.org/doi/10.5281/zenodo.10477935</a></div> <div> </div> <div>If you reuse the data, please cite the publication and ZENODO deposit mentioned above</div>

Data for publication: Si metasurface supporting multiple quasi-BICs for degenerate four-wave mixing

Open the record for dataset details and reuse information.

AI as a driver of quality education in public universities. A bibliometric review

Artificial intelligence (AI) is transforming teaching in public universities, enabling improvements and aiming for quality education. The purpose of this bibliometric research was to analyse the impact of AI on educational quality in public universities. The methodology used was a qualitative approach at a descriptive level, which included terms related to artificial intelligence and quality education in public universities; analysing 488 Scopus documents between 1987 and 2024, through the Vosviewer and Bibliometrix tools; and a review of 43 authors cited between 2020 and 2024 in the introduction and literature. The results reveal a growing interest in AI to personalise learning, provide high-quality resources and promote sustainability. Its potential to differentiate teaching and address specific needs of diverse learners is highlighted. It is concluded that AI can improve educational quality and equity, but strategies for ethical and responsible implementation are required, addressing challenges such as teacher training and change management.

Figure 3 in Distribution of tick species parasitizing livestock in Sirumalai, The Eastern Ghats of Tamil Nadu, South India and its implications for public health

Figure 3. Images of ticks collected from Sirumalai foot hill villages.

Figure 2 in Distribution of tick species parasitizing livestock in Sirumalai, The Eastern Ghats of Tamil Nadu, South India and its implications for public health

Figure 2. Tick infestation rate in cattle, dogs and sheep.

Figure 1. A in Distribution of tick species parasitizing livestock in Sirumalai, The Eastern Ghats of Tamil Nadu, South India and its implications for public health

Figure 1. A map of the Sirumalai ticks collected areas.

Data for publication titled "Effect of ion irradiation on superconducting thin films"

Here is the data to support the findings in the article titled "Effect of ion irradiation on superconducting thin films". There is a .txt file showing the room temperature sheet resistance measurement results for different wafers (measured by a four point probe in multiple positions of the wafer and the value in the table is the average of those measurements). The .csv files show resistance of the films scaled by the room temperature resistance value (R_RT) as a function of temperature. The first column is temperature (K) and the second column is R(T)/R_RT. For some wafers, there are multiple measurement results, labeled _a/_b/_c. The TEM images are the .tif files and FFT graphs derived from some of the TEM images are shared as .png files (titles show which TEM image has been used). EDS data is also shared for some wafers. The corresponding wafer is in the file names, and the data contains a .rpl, a .raw and a .png file with same main names.

Publication data of Examining holistic processing strategies in dogs and humans through gaze behavior

This version, compared to the version 1, further includes the figures and tables used in the publication.  There is an error in the Table 3 file uploaded. The locations of the column names 'else upper half' and 'else lower half' or the images in the two column cells are switched. Please check the Figure 4 of PloS one version of the paper for correct information.   <a href="https://doi.org/10.1371/journal.pone.0317455">https://doi.org/10.1371/journal.pone.0317455</a>   bioRXiv version: Data of Holistic Processing Strategy in Cross-Species Face Perception between Dogs and Humans  <a href="https://doi.org/10.1101/2024.06.21.599532">https://doi.org/10.1101/2024.06.21.599532</a> & PloS one version: Data of Examining holistic processing strategies in dogs and humans through gaze behavior  <a href="https://doi.org/10.1371/journal.pone.0317455">https://doi.org/10.1371/journal.pone.0317455</a>

Data for publication of "Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes"

Implementation of Gaussian process regression-based Bayesian optimisation (G-BO) using the emcee package (<a href="https://emcee.readthedocs.io/en/stable/" rel="nofollow">https://emcee.readthedocs.io/en/stable/</a>). For more information about the implementation of G-BO in optimising the Weather Research and Forecasting (WRF) model parameters, please refer to the paper - <a href="https://essopenarchive.org/doi/full/10.22541/essoar.171292045.52489731" rel="nofollow">Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes</a>. <code>G-BO_script.ipynb</code> implements the GPR-based Bayesian optimisation using the Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler. <ul> <li>QMC_sobol_samples: This file contains the 128 parameter samples across the parameter space of three sensitive parameters utilizing the Quasi Monte-Carlo (QMC) Sobol sequence design.</li> <li>nmae_all_128_ens_T_Rh: This file contains the normalised mean absolute error (NMAE) values of temperature (T) and relative humidity (Rh) of the 128 parameter sample WRF simulations. For more details, please refer to <a href="https://essopenarchive.org/doi/full/10.22541/essoar.171292045.52489731" rel="nofollow">this link</a>.</li> </ul>

opencc-by-4.0Mar 2024View details →

Dataset for publication "Tuning germanane bandgap via cyanoethyl functionalization for cutting-edge photoactive cathodes: photo-enhanced hybrid zinc-ion capacitor evaluation"

This is a dataset for a paper "Tuning germanane bandgap via cyanoethyl functionalization for cutting-edge photoactive cathodes: photo-enhanced hybrid zinc-ion capacitor evaluation". All details about the data are included in the readme file.

Harmonized metadata of Hexa-X-II enablers of Hexa-X-II D2.3 public deliverable

Harmonized metadata of Hexa-X-II enablers of Hexa-X-II D2.3 public deliverable, used in the enabler selection methodology used in the chapter 7 of the deliverable - based on enebaler data collected from the contributors of Hexa-X-II project.

Analysis of public single-cell sequencing database of COVID lung samples

Lung endothelial cells from three published scRNA-seq datasets (GSE122960, GSE149878, GSE171668) of healthy subjects and COVID-19 patients were collected for further integrative analyses. The endothelial cells were classified into three sub-groups according to their distinguished expression of IL7R, DKK2, and EDNRB. For differential analysis of gene expression, counts per million of aggregated UMIs in each group were adopted in Wilcoxon rank-sum test.

Dataset to accompany publication "Precision of Radiation Chemistry Networks: Playing Jenga with Kinetic Models for Liquid-Phase Electron Microscopy"

<h2>Dataset description</h2> This dataset displays the raw data for the manuscript "Precision of Radiation Chemistry Networks: Playing Jenga with Kinetic Models for Liquid-Phase Electron Microscopy" published in Precision Chemistry on 06 December 2023 (DOI: <a title="DOI URL" href="https://doi.org/10.1021/prechem.3c00078">10.1021/prechem.3c00078</a>).

Source data for publication "A multimodal atlas of hepatocellular carcinoma reveals convergent evolutionary paths and 'bad apple' effect on clinical trajectory" in Journal of Hepatology

Processed genomic and transcriptomic data for the publication <a href="https://doi.org/10.1016/j.jhep.2024.05.017">https://doi.org/10.1016/j.jhep.2024.05.017</a>. cnv_segmentation.tsv: CNV segmentation file from Sequenza. cnv_arm.tsv: Significant arm level CNV events called by GISTIC, from broad_values_by_arm.txt file. cnv_gene.tsv: Gene level CNV events called by GISTIC, from all_threshold_by_genes.txt file.  RNA_raw_counts.tsv: Raw RNA-seq read counts from featureCounts. snv_indel.tsv: All SNV and Indel called with annotation from Funcotator.

Forging the Path to FAIR Data Through Data Repository and Journal Publication Community Partnerships

Open the record for dataset details and reuse information.

Raw data and data accompanying publication: https://doi.org/10.1016/j.antiviral.2024.105946

Raw data and data accompanying publication: Baliga-Gil, A., Soszynska-Jozwiak, M., Ruszkowska, A., Szczesniak, I., Kierzek, R., Ciechanowska, M., Trybus, M., Jackowiak, P., Peterson, J.M., Moss, W.N., Kierzek, E., Targeting sgRNA N secondary structure as a way of inhibiting SARS-CoV-2 replication, Antiviral Research. (2024) 228, 105946. https://doi.org/10.1016/j.antiviral.2024.105946

Dataset for a publication: "Silver-enriched Microdomain Patterns as Advanced Bactericidal Coatings for Polymer-based Medical Devices"

The data set contains the data that were used within the article "Silver-enriched Microdomain Patterns as Advanced Bactericidal Coatings for Polymer-based Medical Devices".