cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e189942v1
title: Data and Code for: Self-Preferencing at Amazon: Evidence from Search Rankings

description: We study whether Amazon engages in self-preferencing on its marketplace by favoring its own brands (e.g., Amazon Basics) in search. To address this question, we collect new micro-level consumer search data using a custom browser extension installed by a panel of study participants. Using this methodology, we observe search positions, search behavior, and product characteristics. We find that Amazon branded products are indeed ranked higher than observably similar products in consumer search results. The prominence given to Amazon brands is 30% to 60% of the prominence granted to sponsored products.
DOI: 10.6084/m9.figshare.23056598
title: Differential circRNA expression in QKI KO vs QKI WT hearts

description: Differential circular RNA (circRNA) expression in the heart of 5 QKI KO and 5 wildtype mice
DOI: 10.6084/m9.figshare.23056598.v1
title: Differential circRNA expression in QKI KO vs QKI WT hearts

description: Differential circular RNA (circRNA) expression in the heart of 5 QKI KO and 5 wildtype mice
DOI: 10.5519/qd.l2t6od17
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (16 records)
DOI: 10.5281/zenodo.7957080
title: Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)

description: The dataset contains: i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input ii. the model output and skill produced for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)
DOI: 10.5281/zenodo.7957079
title: Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)

description: The dataset contains: i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input ii. the model output and skill produced for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)
DOI: 10.6084/m9.figshare.23036876
title: The computed data of dam break flows with a two-phase SPH model

description: The computed data obtained by a two-phase SPH model includes the flood propagation and topography in both two- and three- dimensional dam break flood cases, the structure of the bed load layer, as well as the mechanical energy evolution in dam break flow.
DOI: 10.5281/zenodo.7957057
title: Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)

description: The dataset contains: i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input ii. the model output and skill produced for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)
DOI: 10.5281/zenodo.7957056
title: Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)

description: The dataset contains: i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input ii. the model output and skill produced for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)
DOI: 10.5255/ukda-sn-856516
title: Tombstone Cost and Longevity: The San Pedro Cemetery Museum in Medellín in Colombia, 2022-2023

description: Studies in the West suggest that tombstone cost is associated with longevity. The objective of this observational study was to investigate the association between tombstone cost and longevity in a large cemetery in Latin America. Age at death was obtained from 2,273 consecutive death certificates held at the San Pedro Cemetery Museum in Medellín in Colombia. Subjects died in 2022, 2021, or 2020. Tombs are arranged in galleries in the cemetery and tombstone cost was based on the material from which the tombstone was made, its position in the gallery, and its ornamentation. Analysis of variance was used and the assumption of equal variance was not violated. Approximately 77% of tombstones were of low cost, 21% of medium cost, and 2% of high cost. Data from 1,751 subjects were used to investigate differences in longevity according to tombstone cost while controlling for sex, civil status, violent death, and year of death. Longevity was similar in the low-cost group and medium-cost group: 64.3 years (63.2, 65.3) versus 63.3 years (61.3, 65.3) [estimated mean (95% confidence interval)]. Longevity was lower in the high-cost group: 47.0 years (40.1, 53.9). The inverse association between tombstone cost and longevity would suggest that people in Medellín are inclined to spend more on tombstones when commemorating the tragic death of a young person.
DOI: 10.6084/m9.figshare.22572841
title: Metabolics Data: Proportional Myoelectric Control of Dephy ExoBoots

description: Raw metabolics data for walking inside on a treadmill and outside overground with robotic ankle exoskeletons under proportional myoelectric control. Files Included: Data from a Cosmed K5 metabolics system for 12 participants: Metabolics_Data.zip Map from Google which shows one lap of the outdoor course: terrain.jpg Subject sex, height, weight, age, and walking speeds: Subject_Information.xlsx Readme: informmation on the conditions and system. <br>
DOI: 10.6084/m9.figshare.22572841.v3
title: Metabolics Data

description: Raw metabolics data for walking inside on a treadmill and outside overground with robotic ankle exoskeletons.
DOI: 10.5281/zenodo.7957033
title: Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)

description: The dataset contains: i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input ii. the model output and skill produced for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)
DOI: 10.5281/zenodo.7957034
title: Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)

description: The dataset contains: i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input ii. the model output and skill produced for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)
DOI: 10.5281/zenodo.7956975
title: Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)

description: The dataset contains: i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input ii. the model output and skill produced for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)
DOI: 10.5281/zenodo.7956976
title: Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)

description: The dataset contains: i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input ii. the model output and skill produced for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)
DOI: 10.5281/zenodo.3491003
title: Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation [Models]

description: This file contains the pretrained models and the evaluation of the pipelines described in the paper <em>Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation</em>. Source code can be downloaded at: https://github.com/aramis-lab/AD-DL Also, single files can be obtained at: https://aramislab.paris.inria.fr/clinicadl/files/models/v0.0.1/ The structure of the compressed file is as follows: clinicadl_models/<br> ├── 2D_slice<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── AD_CN_dataleakage<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ └── AD_CN<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_patch<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_ROI_based<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_subject<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── autoencoders<br> │ ├── 3D_patch<br> │ │ ├── baseline<br> │ │ │ └── best_model<br> │ │ └── longitudinal<br> │ │ └── best_model<br> │ ├── 3D_ROI_based<br> │ │ ├── baseline<br> │ │ │ └── best_model<br> │ │ └── longitudinal<br> │ │ └── best_model<br> │ └── 3D_subject<br> │ └── baseline<br> │ ├── extensive<br> │ └── minimal<br> └── svm<br> ├── baseline<br> │ ├── AD_CN<br> │ │ ├── all_subjects.tsv<br> │ │ └── classifier<br> │ └── sMCI_pMCI<br> │ ├── all_subjects.tsv<br> │ └── classifier<br> └── longitudinal<br> ├── AD_CN<br> │ ├── all_subjects.tsv<br> │ └── classifier<br> └── sMCI_pMCI<br> ├── all_subjects.tsv<br> └── classifier We provide the pretrained CNN models for the frameworks 3D subject-level, 3D ROI-based, 3D patch-level and 2D slice-level. This models can be found as a <strong><em>.pth.tar</em> </strong>file (<em>Pytorch</em> format) inside the <em>best_model</em> folder for each framework (and for each fold). We also provide the autoencoders that initialize the training stage of the CNN networks. The <em>performances </em>folder contains the computed metrics for the correponding model (ACC, BA, etc). <em> </em> For the svn classification, we provide files with the dual coefficients, the support vector indices and the weights. Also, <em>tsv</em> files with the subject list.
DOI: 10.5281/zenodo.3491002
title: Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation [Models]

description: This file contains the pretrained models and the evaluation of the pipelines described in the paper <em>Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation</em>. Source code can be downloaded at: https://github.com/aramis-lab/AD-DL Also, single files can be obtained at: https://aramislab.paris.inria.fr/clinicadl/files/models/v0.0.1/ The structure of the compressed file is as follows: clinicadl_models/<br> ├── 2D_slice<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── AD_CN_dataleakage<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ └── AD_CN<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_patch<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_ROI_based<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_subject<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── autoencoders<br> │ ├── 3D_patch<br> │ │ ├── baseline<br> │ │ │ └── best_model<br> │ │ └── longitudinal<br> │ │ └── best_model<br> │ ├── 3D_ROI_based<br> │ │ ├── baseline<br> │ │ │ └── best_model<br> │ │ └── longitudinal<br> │ │ └── best_model<br> │ └── 3D_subject<br> │ └── baseline<br> │ ├── extensive<br> │ └── minimal<br> └── svm<br> ├── baseline<br> │ ├── AD_CN<br> │ │ ├── all_subjects.tsv<br> │ │ └── classifier<br> │ └── sMCI_pMCI<br> │ ├── all_subjects.tsv<br> │ └── classifier<br> └── longitudinal<br> ├── AD_CN<br> │ ├── all_subjects.tsv<br> │ └── classifier<br> └── sMCI_pMCI<br> ├── all_subjects.tsv<br> └── classifier We provide the pretrained CNN models for the frameworks 3D subject-level, 3D ROI-based, 3D patch-level and 2D slice-level. This models can be found as a <strong><em>.pth.tar</em> </strong>file (<em>Pytorch</em> format) inside the <em>best_model</em> folder for each framework (and for each fold). We also provide the autoencoders that initialize the training stage of the CNN networks. The <em>performances </em>folder contains the computed metrics for the correponding model (ACC, BA, etc). <em> </em> For the svn classification, we provide files with the dual coefficients, the support vector indices and the weights. Also, <em>tsv</em> files with the subject list.
DOI: 10.5281/zenodo.7957084
title: Realistic afforestation scenarios in Great Britain at a 1 km scale to run with the land surface model JULES

description: Afforestation scenarios created in the work of Buechel et al. (XXXX) which cover Great Britain at a 1 km spatial resolution and attempt to represent potential realistic broadleaf afforestation. The datasets represent a 50% and 100% afforestation scenario. The netCDF files are designed so that may be run with the Joint UK Land Environment Simulator (JULES), a community land surface model. The dataset is structured similar to the CHESS-land dataset (Martinez-De La Torre, 2018) where each grid contains information on the fractional coverage of eight different land cover types: Broadleaf woodland, needleleaf woodland, grassland, shrubland, crops, bare soil, urban areas and inland water. This dataset was created as part of the NERC doctoral training partnerships (grant number NE/L002612/1). Martinez-de la Torre, A.., Blyth, E.M.. M. and Robinson, E.L.. L. (2018) ‘Water, carbon and energy fluxes simulation for Great Britain using the JULES Land Surface Model and the Climate Hydrology and Ecology research Support System meteorology dataset (1961-2015) [CHESS-land]’. NERC Environmental Information Data Centre. doi:10.5285/c76096d6-45d4-4a69-a310-4c67f8dcf096.
DOI: 10.5281/zenodo.7957083
title: Realistic afforestation scenarios in Great Britain at a 1 km scale to run with the land surface model JULES

description: Afforestation scenarios created in the work of Buechel et al. (XXXX) which cover Great Britain at a 1 km spatial resolution and attempt to represent potential realistic broadleaf afforestation. The datasets represent a 50% and 100% afforestation scenario. The netCDF files are designed so that may be run with the Joint UK Land Environment Simulator (JULES), a community land surface model. The dataset is structured similar to the CHESS-land dataset (Martinez-De La Torre, 2018) where each grid contains information on the fractional coverage of eight different land cover types: Broadleaf woodland, needleleaf woodland, grassland, shrubland, crops, bare soil, urban areas and inland water. This dataset was created as part of the NERC doctoral training partnerships (grant number NE/L002612/1). Martinez-de la Torre, A.., Blyth, E.M.. M. and Robinson, E.L.. L. (2018) ‘Water, carbon and energy fluxes simulation for Great Britain using the JULES Land Surface Model and the Climate Hydrology and Ecology research Support System meteorology dataset (1961-2015) [CHESS-land]’. NERC Environmental Information Data Centre. doi:10.5285/c76096d6-45d4-4a69-a310-4c67f8dcf096.
DOI: 10.6084/m9.figshare.23057000
title: Test Data.rar

description: Two CCTA data， DICOM format.
DOI: 10.6084/m9.figshare.23057000.v1
title: Test Data.rar

description: Two CCTA data， DICOM format.
DOI: 10.5066/p9kvvx0q
title: Molecular, morphological, and distributional data supporting the recognition of an undescribed freshwater mussel endemic to the Edwards Plateau in the Colorado River basin

description: The widespread distribution of Strophitus undulatus, as well as high intraspecific morphological variation, has led previous authors to doubt the taxon is representative of a single species. Here we provide detailed information for all the freshwater mussels and associated DNA sequence data utilized to test species boundaries in S. undulatus. These molecular data, in combination with morphological data and results from species distribution models, support the recognition of an undescribed freshwater mussel species endemic to streams along the Edwards Plateau in the Colorado River basin in Texas, USA.
DOI: 10.5281/zenodo.7957395
title: Example data, notebook script and output

description: Raw, azimuthally resolved XRD on SRM660a. Correction and integration script. Output integrator descriptions and processed 1 and 2D data.
DOI: 10.5281/zenodo.7957394
title: Example data, notebook script and output

description: Raw, azimuthally resolved XRD on SRM660a. Correction and integration script. Output integrator descriptions and processed 1 and 2D data.
DOI: 10.6084/m9.figshare.22572868
title: Electromyography and ExoBoot Proportional Myoelectric Control Data

description: Electromyography data, exoskeleton control data from Dephy ExoBoots under a custom proportional myoelectric controller. Data from an experimental paradigm measuring metabolic cost of walking both inside on a treadmill and outside overground. Files Included: Data from Dephy ExoBoots using a custom proportional myoelectric controller: EMG_EXO_DATA.zip Map from Google which shows one lap of the outdoor course: terrain.jpg Subject sex, height, weight, age, and walking speeds: Subject_Information.xlsx Redme with mroe information on files and data in the excel sheets.
DOI: 10.6084/m9.figshare.22572868.v2
title: EMG and control data

description: Raw EMG data and control currents for walking inside and outside with a robotic ankle exoskeleton
DOI: 10.6084/m9.figshare.23057558
title: Supplementary Spreadsheet 1.xlsx for Randi et al, Signal propagation atlas, arXiv 2023 https://arxiv.org/abs/2208.04790v3

description: Supplementary Spreadsheet 1 listing putative peptide and receptors expressed in unc-31 dependent signaling neuron pairs. From Randi et al., Signal propagation atlas, arXiv 2023. https://arxiv.org/abs/2208.04790v3
DOI: 10.6084/m9.figshare.23057558.v1
title: Supplementary Spreadsheet 1.xlsx for Randi et al, Signal propagation atlas, arXiv 2023 https://arxiv.org/abs/2208.04790v3

description: Supplementary Spreadsheet 1 listing putative peptide and receptors expressed in unc-31 dependent signaling neuron pairs. From Randi et al., Signal propagation atlas, arXiv 2023. https://arxiv.org/abs/2208.04790v3
DOI: 10.5281/zenodo.7612915
title: Datasets and Supporting Materials for the IPIN 2022 Competition Track 3 (Smartphone-based, off-site)

description: This package contains the datasets and supplementary materials used in the IPIN 2022 Competition. <strong>Contents:</strong> Track-3_TA-2022.pdf: Technical annex describing the competition (Version 2) 01 Logfiles: This folder contains a subfolder with the 89 training trials a subfolder with the 24 testing trials (validation), and a subfolder with the 3 blind scoring trials (test) as provided to competitors. 02 Supplementary_Materials: This folder contains the matlab/octave parser, the raster maps, the files for the matlab tools and the trajectory visualization. 03 Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 31|61|61 evaluation points. It requires the Matlab Mapping Toolbox. The ground truth is also provided as 3 csv files. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT files include the closest timestamp matching the timing provided by competitors for the 3 evaluation logfiles. It contains samples of reported estimations and the corresponding results. <strong>Please, cite the following works when using the datasets included in this package:</strong> Torres-Sospedra, J.; et al. Datasets and Supporting Materials for the IPIN 2022 Competition Track 3 (Smartphone-based, off-site), Zenodo 2022. http://dx.doi.org/10.5281/zenodo.7612915
description: We would like to thank the Urban Computing Lab at the University of Minho for their support in collecting the datasets and the ISTI-CNR for managing the competition and finding sponsors for the winner's award. We are also grateful to Francesco Potortì, Sangjoon Park and the ISTI-CNR team for their invaluable commitment to organizing and promoting the IPIN competition. This work has been done within the framework of the IPIN 2022 Conference in Beijing. Parts of this work were carried out with the financial support received from projects and grants: ORIENTATE (H2020-MSCA-IF-2020, Grant Agreement 101023072); GeoLibero (from CYTED); INDRI (MICINN, ref. PID2021-122642OB-C42, PID2021-122642OB-C43, PID2021-122642OB-C44, MCIU/AEI/FEDER UE); MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE); TARSIUS (TIN2015-71564-C4-2-R, MINECO/FEDER); SmartLoc(CSIC-PIE Ref.201450E011); LORIS (TIN2012-38080-C04-04)