cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25430/researchdata.cab.unipd.it.00000134
title: High velocity friction data and thermochemical modeling data of smectite-rich STx-1b (vacuum dry, room humidity and water partly saturated)

description: The dataset is described also in the readme.txt file as follows:
The columns are organized as follows:
* Time [milliseconds]: time 
* Normal [MPa]: normal stress 
* Slip [m]: equivalent displacement
* Vel [m/s]: equivalent tangential velocity
* Shear1 [MPa]: shear stress
* Mu1 []: friction coefficient = shear stress / normal stress
* Thick [mm]: thickness of the gouge layer measured with the low resolution LVDT
* Thick_high [mm]: thickness of the gouge layer measured with the high resolution LVDT

Models
Subfolder \model\
Each filename is the experiment’s name and is in .CSV format. The header for all the tables is in a separate file: header.CSV.
The columns are organized as follows:
* Time [seconds]: time
* U_1 [°C]: temperature in node 1
* U_20 [°C]: temperature in node 20
* Qso [°C/s]: temperature source
* Qsi1 [°C/s]: temperature sink of reaction 1 (smectite interlayer dehydration) 
* Qsi2 [°C/s]: temperature sink of reaction 2 (smectite dehydroxylation)
* U2_1 [MPa]: pressure in node 1
* U2_20 [MPa]: pressure in node 20
* Thpress [Pa/s]: pressure source for thermal pressurization
* Omega1 [Pa/s]: pressure source for reaction 1 (smectite interlayer dehydration)
* Omega2 [Pa/s]: pressure source for reaction 2 (smectite dehydroxylation)
* R1_1 []: reacted fraction for reaction 1 (smectite interlayer dehydration)
* R2_1 []: reacted fraction for reaction 2 (smectite dehydroxylation)
DOI: 10.5281/zenodo.2578060
title: Database of experiments on SFRC beams without stirrups failing in shear

description: This dataset contains the information of 488 experiments reported in the literature on steel fiber reinforced concrete beams without stirrups failing in shear.
DOI: 10.5281/zenodo.3253258
title: W and b arrays

description: W and b arrays of the proposed ANN - eq. (4) <strong>Check reference below:</strong> https://www.researchgate.net/publication/335946530_Neural_Nets_-_Steel_Fiber-Reinf_Concrete_-_Shear
DOI: 10.5523/bris.o4hx7jnmfqt01lyzf2n4rchg6
title: Bristol Egocentric Object Interactions Dataset
title: BEOID

description: The wearable gaze tracker hardware (ASL Mobile Eye XG) was used to collect Egocentric video with synchronised gaze for multiple operators around a common environment. After calibration, the scene images are synchronised with, if available, 2D gaze points. Six locations were chosen: kitchen (K), workspace (W), laser printer (P), corridor with a locked door (D), cardiac gym (G) and weight-lifting machine (M) (Fig. 3). For the first four locations (K, W, P, D), sequences from five different operators were recorded, and from three operators for the last two locations (G, M). The sequences, 3D maps, and 3D object ground-truth are available.
DOI: 10.6084/m9.figshare.8298860.v2
title: Data from: Hydrodynamics of metachronal paddling: effects of varying Reynolds number and phase lag

description: Data used in the paper by Ford et al. (2019). Readme.txt within every folder/subfolder contains information on data formatting.
DOI: 10.17632/t3nzgg2kk3.1
title: Corinaldesi  et al. Changes in coral forest microbiomes predict the impact of marine heatwaves on habitat-forming species down to mesophotic depths. 

description: Dataset S1.xls. Prokaryotic abundances of coral colonies collected at different temperatures and depths; CARD FISH data of Bacteria and Archaea in coral colonies collected at different temperatures and depths; Tables include read abundance counts per amplicon sequence variant; Taxonomic affiliation of ASVs.
DOI: 10.17632/4xhwkd3jfx.1
title: Pfs230 and Pfs25-specific VDJ sequences (Heavy chain)

description: Sequences from the data published by Patrick Duffy's lab at: Coelho et al. International Journal for Parasitology, 2021.
DOI: 10.17632/pr7y278yyd.1
title: Settling dynamics of cohesive sediments in a highly turbid tidal river

description: Variability of settling velocity od cohesive sediment in a highly turbid tidal river.
DOI: 10.5281/zenodo.7134119
title: Processed data for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"

description: This is the data used to reproduce the results from "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".
DOI: 10.5281/zenodo.7134118
title: Processed data for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"

description: This is the data used to reproduce the results from "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".
DOI: 10.6084/m9.figshare.20439309
title: Massive Monte Carlo simulations-guided interpretable learning of two-dimensional Curie temperature

description: This repository contains the datasets and ML models from the research work "Massive Monte Carlo simulations-guided interpretable learning of two-dimensional Curie temperature".
DOI: 10.5281/zenodo.7134684
title: Supplementary files for Chapter 3 of Kiselev PhD Thesis

description: Supplementary files for Chapter 3 of Kiselev PhD Thesis
DOI: 10.5281/zenodo.7134683
title: Supplementary files for Chapter 3 of Kiselev PhD Thesis

description: Supplementary files for Chapter 3 of Kiselev PhD Thesis
DOI: 10.5281/zenodo.7132930
title: Supplementary files for chapter 4 of Kiselev PhD Thesis

description: Supplementary dataset for chapter 4 of the thesis
DOI: 10.5281/zenodo.7132929
title: Supplementary files for chapter 4 of Kiselev PhD Thesis

description: Supplementary dataset for chapter 4 of the thesis
DOI: 10.5281/zenodo.7132972
title: Supplementary files for chapter 4 of Kiselev PhD Thesis

description: Supplementary dataset for chapter 4 of the thesis
DOI: 10.5281/zenodo.7134705
title: Supplementary files for chapter 4 of Kiselev PhD Thesis

description: Supplementary dataset for chapter 4 of the thesis
DOI: 10.5281/zenodo.7108766
title: Flow cytometry gating 2D: x (HIPC, pregnancy), y (SangerP2, HIPC, Pregnancy)

description: Processed data set.
DOI: 10.5281/zenodo.7116166
title: Flow cytometry gating 2D: x (HIPC, pregnancy), y (SangerP2, HIPC, Pregnancy)

description: Processed data set.
DOI: 10.5281/zenodo.7108765
title: Flow cytometry gating 2D: x (HIPC, pregnancy), y (HIPC, Pregnancy, sangerP2), nD x (pregnancy), nD y (pregnancy, HIPC, sangerP2)

description: Processed data set.
DOI: 10.5281/zenodo.7133191
title: Flow cytometry gating 2D: x (HIPC, pregnancy), y (HIPC, Pregnancy, sangerP2), nD x (pregnancy), nD y (pregnancy, HIPC, sangerP2)

description: Processed data set.
DOI: 10.5281/zenodo.7134723
title: GeoWaVe Cytometry Benchmark Data

description: Contained within this folder are six benchmark datasets (Levine13, Levine32, Samusik, Sepsis, and PD) used for the evaluation of the GeoWaVe ensemble clustering algorithm, part of the cytocluster (https://github.com/burtonrj/CytoCluster) package. The data are compensated, arc-sine transformed, and debris and dead cells removed. See manuscript for details: https://doi.org/10.1101/2022.06.30.496829 Each dataset is available as a CSV file and includes two additional columns: UMAP1 and UMAP2. The UMAP columns contain embeddings generated using UMAP (2 components and n_neighbours=30) and were used for visualisation purposes. The column 'population' contains the original population labels generated using manual gating.
DOI: 10.5281/zenodo.7134722
title: GeoWaVe Cytometry Benchmark Data

description: Contained within this folder are six benchmark datasets (Levine13, Levine32, Samusik, Sepsis, and PD) used for the evaluation of the GeoWaVe ensemble clustering algorithm, part of the cytocluster (https://github.com/burtonrj/CytoCluster) package. The data are compensated, arc-sine transformed, and debris and dead cells removed. See manuscript for details: https://doi.org/10.1101/2022.06.30.496829 Each dataset is available as a CSV file and includes two additional columns: UMAP1 and UMAP2. The UMAP columns contain embeddings generated using UMAP (2 components and n_neighbours=30) and were used for visualisation purposes. The column 'population' contains the original population labels generated using manual gating.
DOI: 10.34740/kaggle/dsv/4274062
title: YouTube Daily
DOI: 10.5281/zenodo.7106159
title: How do Google News' top 100 sources visually represent the data centres' energy footprint?

description: <strong>By querying "data centres' energy footprint" on Google News in incognito mode, the candidate has selected and mapped the top 100 results according to the ranking on May 15, 2022. </strong>
DOI: 10.5281/zenodo.7106158
title: How do Google News' top 100 sources visually represent the data centres' energy footprint?

description: <strong>By querying "data centres' energy footprint" on Google News in incognito mode, the candidate has selected and mapped the top 100 results according to the ranking on May 15, 2022. </strong>
DOI: 10.25919/4qbc-pb93
title: Data for "Genetic data confirm the presence of fireweed Senecio madagascariensis in New Zealand"

description: Assembly target file, data matrix, partition files, and tree file for a study testing the utility of Angisperms353 sequence capture data as a marker system for the fireweed complex and confirming the presence of fireweed in New Zealand.
DOI: 10.5281/zenodo.7134778
title: How much data about the data centres' energy footprint is currently available globally?

description: <strong>By scraping the open database "Datacenter.rs", the candidate analysed and mapped more than 6.000 data centres worldwide on April 9, 2022.</strong>
DOI: 10.5281/zenodo.7134777
title: How much data about the data centres' energy footprint is currently available globally?

description: <strong>By scraping the open database "Datacenter.rs", the candidate analysed and mapped more than 6.000 data centres worldwide on April 9, 2022.</strong>
DOI: 10.34740/kaggle/dsv/4270691
title: Tomato Disease Multiple Sources
DOI: 10.21227/1gkm-b814
title: DATASET OF MY PAPER

description: 'XMD.mat' is the deterioration levels monitored before the failure of 10 components。'XMDS.mat' is the deterioration levels monitored before the failure of 10 components subjected to random shocks.
DOI: 10.6084/m9.figshare.20417667
title: Lipidomics analysis of alpha4 project

description: Lipidomics raw data of adipose tissues and serum from Control and Ai-α4KOmice. <br>
DOI: 10.6084/m9.figshare.20417667.v1
title: Lipidomics analysis of alpha4 project

description: Lipidomics raw data of adipose tissues and serum from Control and Ai-α4KOmice. <br>
DOI: 10.5281/zenodo.7134797
title: dhaaana/paper-ep: Dataset Jawaban Survei Penggunaan Media Sosial Di Kalangan Mahasiswa ITS Menggunakan Teori Uses and Gratifications

description: dataset hasil kuisioner paper Etika Profesi TI kelas C
DOI: 10.5281/zenodo.7134798
title: dhaaana/paper-ep: Dataset Jawaban Survei Penggunaan Media Sosial Di Kalangan Mahasiswa ITS Menggunakan Teori Uses and Gratifications

description: dataset hasil kuisioner paper Etika Profesi TI kelas C
DOI: 10.5281/zenodo.5904211
title: Variable Message Signal annotated images for object detection

description: <strong>If you use this dataset, please cite this paper: <em>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</em></strong> 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 <strong>1216</strong> instances, <strong>888</strong> positives, and <strong>328</strong> negatives, in <strong>1152</strong> jpg images with a resolution of 1280x720 pixels. These are divided into <strong>576</strong> real images and <strong>576</strong> 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: vms_dataset/ data.csv real_images/ imgs/ annotations/ data-augmentation/ imgs/ annotations/ In which: <strong>data.csv:</strong> 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. <strong>real_images:</strong> Images extracted directly from the videos. <strong>data-augmentation:</strong> Images created using data-augmentation <strong>imgs:</strong> Image files in .jpg format. <strong>annotations:</strong> Annotation files in .xml format.
description: Funding: This publication is part of the I+D+i projects with reference PID2019-104793RB-C32, PIDC2021-121517-C33, funded by MCIN/AEI/10.13039/501100011033/, S2018/EMT-4362/"SEGVAUTO4.0-CM" funded by Regional Government of Madrid and "ESF and ERDF A way of making Europe".
DOI: 10.5281/zenodo.5904177
title: Variable Message Signal annotated images for object detection

description: <strong>If you use this dataset, please cite this paper: <em>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</em></strong> 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 <strong>1216</strong> instances, <strong>888</strong> positives, and <strong>328</strong> negatives, in <strong>1152</strong> jpg images with a resolution of 1280x720 pixels. These are divided into <strong>576</strong> real images and <strong>576</strong> 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: vms_dataset/ data.csv real_images/ imgs/ annotations/ data-augmentation/ imgs/ annotations/ In which: <strong>data.csv:</strong> 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. <strong>real_images:</strong> Images extracted directly from the videos. <strong>data-augmentation:</strong> Images created using data-augmentation <strong>imgs:</strong> Image files in .jpg format. <strong>annotations:</strong> Annotation files in .xml format.
description: Funding: This publication is part of the I+D+i projects with reference PID2019-104793RB-C32, PIDC2021-121517-C33, funded by MCIN/AEI/10.13039/501100011033/, S2018/EMT-4362/"SEGVAUTO4.0-CM" funded by Regional Government of Madrid and "ESF and ERDF A way of making Europe".
DOI: 10.5281/zenodo.6407460
title: Roundabout Aerial Images for Vehicle Detection

description: <strong>If you use this dataset, please cite this paper: <em>Puertas, E.; De-Las-Heras, G.; Fernández-Andrés, J.; Sánchez-Soriano, J. Dataset: Roundabout Aerial Images for Vehicle Detection. Data 2022, 7, 47. https://doi.org/10.3390/data7040047 </em></strong> This publication presents a dataset of Spanish roundabouts aerial images taken from an UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2,262 trucks, 7,008 buses and 2,208 empty roundabouts, in 61,896 1920x1080px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flows and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research on computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection. <strong>Roundabout (scenes)</strong> <strong>Frames</strong> <strong>Car</strong> <strong>Truck</strong> <strong>Cycle</strong> <strong>Bus</strong> <strong>Empty</strong> 1 (00001) 1,996 34,558 0 4229 0 0 2 (00002) 514 743 0 0 0 157 3 (00003-00017) 1,795 4822 58 0 0 0 4 (00018-00033) 1,027 6615 0 0 0 0 5 (00034-00049) 1,261 2248 0 550 0 81 6 (00050-00052) 5,501 180,342 1420 120 1376 0 7 (00053) 2,036 5,789 562 0 226 92 8 (00054) 1,344 1,733 222 0 150 222 <strong>Total</strong> 15,474 236,850 2,262 4,899 1,752 552 <strong>Data augmentation</strong> x4 x4 x4 x4 x4 x4 <strong>Total</strong> 61,896 947,400 9048 19,596 7,008 2,208
description: Funding: This publication is part of the I+D+i projects with reference PID2019-104793RB-C32, PIDC2021-121517-C33, funded by MCIN/AEI/10.13039/501100011033/, S2018/EMT-4362/"SEGVAUTO4.0-CM" funded by Regional Government of Madrid and "ESF and ERDF A way of making Europe".
DOI: 10.5281/zenodo.6362360
title: Roundabout Aerial Images for Vehicle Detection

description: <strong>If you use this dataset, please cite this paper: <em>Puertas, E.; De-Las-Heras, G.; Fernández-Andrés, J.; Sánchez-Soriano, J. Dataset: Roundabout Aerial Images for Vehicle Detection. Data 2022, 7, 47. https://doi.org/10.3390/data7040047 </em></strong> This publication presents a dataset of Spanish roundabouts aerial images taken from an UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2,262 trucks, 7,008 buses and 2,208 empty roundabouts, in 61,896 1920x1080px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flows and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research on computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection. <strong>Roundabout (scenes)</strong> <strong>Frames</strong> <strong>Car</strong> <strong>Truck</strong> <strong>Cycle</strong> <strong>Bus</strong> <strong>Empty</strong> 1 (00001) 1,996 34,558 0 4229 0 0 2 (00002) 514 743 0 0 0 157 3 (00003-00017) 1,795 4822 58 0 0 0 4 (00018-00033) 1,027 6615 0 0 0 0 5 (00034-00049) 1,261 2248 0 550 0 81 6 (00050-00052) 5,501 180,342 1420 120 1376 0 7 (00053) 2,036 5,789 562 0 226 92 8 (00054) 1,344 1,733 222 0 150 222 <strong>Total</strong> 15,474 236,850 2,262 4,899 1,752 552 <strong>Data augmentation</strong> x4 x4 x4 x4 x4 x4 <strong>Total</strong> 61,896 947,400 9048 19,596 7,008 2,208
description: Funding: This publication is part of the I+D+i projects with reference PID2019-104793RB-C32, PIDC2021-121517-C33, funded by MCIN/AEI/10.13039/501100011033/, S2018/EMT-4362/"SEGVAUTO4.0-CM" funded by Regional Government of Madrid and "ESF and ERDF A way of making Europe".
DOI: 10.5281/zenodo.7134841
title: Komentar_pada_Akun_Instagram_Kemenkominfo_Pasca_Kasus_Kebocoran_Data

description: No description provided.
DOI: 10.5281/zenodo.7134840
title: Komentar_pada_Akun_Instagram_Kemenkominfo_Pasca_Kasus_Kebocoran_Data

description: No description provided.
DOI: 10.5281/zenodo.7134870
title: Relationship of thermal treatment and antioxidant capacity in cooked foods

description: This is supplemental information of the paper "Relationship of thermal treatment and antioxidant capacity in cooked foods", submitted for publication to Antioxidants.
DOI: 10.5281/zenodo.7134869
title: Relationship of thermal treatment and antioxidant capacity in cooked foods

description: This is supplemental information of the paper "Relationship of thermal treatment and antioxidant capacity in cooked foods", submitted for publication to Antioxidants.
DOI: 10.5281/zenodo.4502885
title: Islamic Traditions [IO Islamic 641] الجامع الصحیح (part of)

description: <strong>Tradition.</strong> <strong>This manuscript is now IO Islamic 641 in the India Office collections.</strong> <strong>[metadata:</strong> <strong>Otto Loth, </strong><strong><em>A Catalogue of the Arabic Manuscripts in the Library of the India Office</em>, (volume 1), no. 132 here with further notations and hyperlinks]</strong>. 132. 641. Size 9<sup>1/2</sup> in. by 6<sup>1/2</sup> in.; foll. 280. Twenty-three lines in a page. The Second Part of the الجامع الصحیح or Collection of Traditions of MUSLIM b. al-Ḥajjâj Ḳushairî Nîsâbûrî (d. A. H. 261). Cf. Ḥ. Kh. ii. 541; Cat. Mus. Brit. 112 and 719; Aumer, Hdss. Münch. 25. Printed at Calcutta, A. H. 1265. This part contains from کتاب الصوم to کتاب الاضاحی. Well written, by جلویه بن محمد شاه بن محمد بن محمود شاه الحاجب التبریزی. Collated with another MS. in Sha’bân, 791. Coloured lines round the pages. Foll. 1-37 have been supplied by a modern hand. Foll. 95, 157, and 235 have been misplaced in binding; they should stand after foll. 86, 154, and 227 respectively. [Tippu.]
DOI: 10.5281/zenodo.4502884
title: Islamic Traditions [IO Islamic 641] الجامع الصحیح (part of)

description: <strong>Tradition.</strong> <strong>This manuscript is now IO Islamic 641 in the India Office collections.</strong> <strong>[metadata:</strong> <strong>Otto Loth, </strong><strong><em>A Catalogue of the Arabic Manuscripts in the Library of the India Office</em>, (volume 1), no. 132 here with further notations and hyperlinks]</strong>. 132. 641. Size 9<sup>1/2</sup> in. by 6<sup>1/2</sup> in.; foll. 280. Twenty-three lines in a page. The Second Part of the الجامع الصحیح or Collection of Traditions of MUSLIM b. al-Ḥajjâj Ḳushairî Nîsâbûrî (d. A. H. 261). Cf. Ḥ. Kh. ii. 541; Cat. Mus. Brit. 112 and 719; Aumer, Hdss. Münch. 25. Printed at Calcutta, A. H. 1265. This part contains from کتاب الصوم to کتاب الاضاحی. Well written, by جلویه بن محمد شاه بن محمد بن محمود شاه الحاجب التبریزی. Collated with another MS. in Sha’bân, 791. Coloured lines round the pages. Foll. 1-37 have been supplied by a modern hand. Foll. 95, 157, and 235 have been misplaced in binding; they should stand after foll. 86, 154, and 227 respectively. [Tippu.]
DOI: 10.34740/kaggle/dsv/4274394
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.6067/xcv8438077
title: New Boston Air Force Cultural Artifact Dataset

description: Data set containing list of artifacts and associated information for projects completed within the New Boston Air Force Station during the years of 2013-2015 for initial SDSFIE transition. Legacy data.
DOI: 10.6084/m9.figshare.20731900
title: Supplementary_Material

description: The <em>Supplementary material</em> and <em>Appendix document A </em> are designed for the article "Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery"