cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/8p4vkw38y3.1
title: Data for JASP

description: Dataset of the study titled "Effect of learning to ski with an indoor skiing carpet compared to the real snow setting on alpine skiing technique, autotelic experience, and anxiety levels in university students. A pilot randomized controlled trial"
DOI: 10.17632/6ywjszh6f2.1
title: Ferret Exploratory Behavior

description: Dataset containing behavior data from ferrets during mate choice assay
DOI: 10.17632/9dmhxwxv9x.1
title: LDHU3_07.0590

description: acyl-CoA dehydrogenase - mitochondrial precursor
Leishmania donovani (HU3 strain)
DOI: 10.17632/rbsm8z5jxd.1
title: Zhang et al WPO wtCaCO3%

description: The data file provides coordinates, depth, wtCaCO3% of the surface/coretop marine sediments in the West Pacific Ocean and the sources for the data.
DOI: 10.17632/z2v2t6dhd6.1
title: Accident data

description: The accident data of South-East Asian region
DOI: 10.17632/mfw3t7vyfr.1
title: Sample Response

description: The survey was conducted on 65 seafarers .
DOI: 10.17632/pv5t9kr58z.1
title: Microneedling plus topical dutasteride solution for androgenetic alopecia: a randomized placebo-controlled study.

description: Microneedling plus topical dutasteride solution for androgenetic alopecia: a randomized placebo-controlled study. JAAD Supplemental Figure 1.A-B.
DOI: 10.17632/sjwb9hymhn.2
title: The LaMIT database: a read speech corpus for acoustic studies of the Italian language toward lexical access based on the detection of landmarks and other acoustic cues to features

description: The LaMIT database consists in recordings of 100 Italian sentences. The sentences in the database were designed so to include all phonemes of the Italian language, and also take into account the typical frequency of each phoneme in written Italian.
Four native adult speakers of Standard Italian, raised and living in Rome, Italy, two female and two male, pronounced the sentences in two different recording sessions; two repetitions for each sentence per speaker were therefore collected, for a total of 800 recordings.
The database was specifically created for application in the LaMIT project, that focuses on the application to the Italian language of the Lexical Access model proposed by Ken Stevens for American English. The model relies on the detection of specific acoustic discontinuities – called landmarks – and cues correlated to acoustic features that characterize each phoneme. Each recording was thus processed to generate a set of labeling files that identify both predicted landmarks and cues, based on the phonemes in the written sentence and actual landmarks/cues detected in the recording. The labeling files, redacted according to the labeling syntax used in the Praat speech processing software, are also made available as part of the LAMIT database.
DOI: 10.17632/2dnb4mw99c.1
title: Violência obstétrica à luz da Teoria da Diversidade e Universalidade do Cuidado Cultural

description: Esse dados fazem parte da pesquisa intitulada Violência obstétrica à luz da Teoria da Diversidade e Universalidade do Cuidado Cultural, na qual objetivou-se analisar relatos de puérperas sobre violência obstétrica à luz da Teoria da Diversidade e Universalidade do Cuidado Cultural. Materiais e Método: Estudo transversal, abordagem qualitativa, desenvolvido em estratégias de Saúde da Família com 10 puérperas. A coleta de dados foi realizada através de entrevista semi-estruturada cujos resultados foram organizados e adaptados ao modelo Sunrise.
DOI: 10.17632/m5jc7sdmn5.1
title: export performance determinants in agro-processing

description: Data set of 108 responses from surveyed owner/managers of medium-scale agro-processing firms in the Philippines. Gathered on August and September 2021 (six weeks total). Five latent variables: Export performance (EP), Dynamic capabilities (DC), Entrepreneurial orientation (EO), Environmental hostility (EH), and Environmental munificence (EM). General research hypothesis: environmental determinants (EH and EM), and firm internal resources (EO and DC) influence export performance (EP). Data can be analyzed using PLS-SEM (through analysis of direct and indirect effects). Data is ordinal type from 1 (strongly disagree) to 7 (strongly agree).
DOI: 10.17632/7dg99h9khs.1
title: RealVelocityMeasuringDevice_2_E_Brauns

description: Regarding a third (submitted) publication in Elsevier Optik (as a follow-up publication of the preceding Elsevier Optik publications [1] and [2]), which is about the concept of a RVMD (Real Velocity Measuring Device), this Mendeley data set contains a conference mp4 video file about [1] and [2] and three scientific animation files (3 animated gif files). The mp4 video file EMDR16_E_Brauns_Physics_Forum_Frankfurt_2021_v2.mp4 is linked to an invited keynote presentation at the conference Physics_Forum (Frankfurt, December 2021), by the author regarding [1] and [2]. The 3 additional files are EMDR18_E_Brauns_Animation_vx_vy_Plane_xy.gif (reference [5] in the follow-up publication), EMDR17_E_Brauns_Animation_RVMDy.gif (reference [7] in the follow-up publication), EMDR19_E_Brauns_Animation_Theodolite.gif (reference [8] in the follow-up publication). ///// Reference [1] Etienne Brauns, On two thought experiments revealing two massive theoretical anomalies, proving both the contemporary “ray of light” paradigm to be flawed and the impossibility of a photon to inherit any velocity vector component from its source, Optik 230 (2021) 165858, https://doi.org/10.1016/j.ijleo.2020.165858 ////// Reference [2] Etienne Brauns, On a straightforward laser experiment, confirming the previously published irrevocable falsification of the Equivalence Principle paradigm for photon phenomena, Optik 242 (2021) 167178, https://doi.org/10.1016/j.ijleo.2021.167178
DOI: 10.17632/8dp9g6zsr5.1
title: 01 CA DMV Collision Reports - Summary

description: The raw reports are provided in PDF format that are not searchable. This spreadsheet is created to summarize the reports data in field format.
DOI: 10.17632/74kc4zngvf.1
title: 03 CA DMV Mileage and Disengagement Reports - Summary

description: The raw reports are provided in PDF format that are not searchable. This spreadsheet is created to summarize the reports data in field format.
DOI: 10.17632/zyyjdcn9kf.1
title: 02 CA DMV Collision Reports - Original

description: DMV collision reports for automated vehicles testing collected from the publicly accessible CA DMV site:
https://www.dmv.ca.gov/portal/vehicle-industry-services/autonomous-vehicles/

Note that older reports (> 2 years) are archived by the CA DMV. To obtain archived records, send an email to  "AVarchive@dmv.ca.gov". They normally respond within 1 business day.

The reports are organized here in Folders by their reported year.
DOI: 10.17632/m73rthmfbd.1
title: Supplementary Appendix - Demographic Factors and Disparate Outcomes in Mycosis Fungoides: Retrospective Analysis of a Racially Diverse 440 Patient Cohort from Detroit, MI

description: Supplementary Appendix - Demographic Factors and Disparate Outcomes in Mycosis Fungoides: Retrospective Analysis of a Racially Diverse 440 Patient Cohort from Detroit, MI
DOI: 10.17632/74s6nw7dk9.1
title: 04 CA DMV Mileage and Disengagement Reports - Original

description: DMV mileage and disengagement reports for automated vehicle testing collected from the publicly accessible CA DMV site: https://www.dmv.ca.gov/portal/vehicle-industry-services/autonomous-vehicles/

Note that older reports (> 2 years) are archived by the CA DMV. To obtain archived records, send an email to  "AVarchive@dmv.ca.gov". They normally respond within 1 business day.

The reports are organized here in Folders by their reported year.
DOI: 10.17632/jc86xfskzh.1
title: CELL-REPORTS-D-21-02987 Xie and Zhang et al

description: Original western blot and microscopy images in this study have been deposited and are publicly available as of the data of publication.
DOI: 10.17632/2nc6jhmhfw.1
title: Crack for Holder of MCF

description: This design was used to keep MCF under buffer
DOI: 10.17632/79rd727xs3.1
title: Holder for MCF - Microscopy

description: This design was used to hole MCF to observe bacterial motility under 3D printed microscopy.
DOI: 10.17632/ngnjxzy59n.1
title: Source Data for paper entitled: "RIF1 acts in DNA repair through phosphopeptide recognition of 53BP1"  by Setiaputra et al.  (Molecular Cell 2022)

description: The source data found below pertains to our work defining the mechanism by which 53BP1 interacts with, and promote recruitment of, RIF1. The analyzed data, including their visualization and discussion can be found in Setiaputra et al. (Molecular Cell 2022). The source data is organized according to the figure panels displayed in the manuscript.
DOI: 10.6084/m9.figshare.18569594
title: Viscosity_data.xlsx

description: Experimental viscosity data for ionic liquidsComputed molecular descriptors for corresponding ionic liquids in the data set
DOI: 10.6084/m9.figshare.17068160
title: Metadata record for: A crowdsourced global data set for validating built-up surface layers

description: This dataset contains key characteristics about the data described in the Data Descriptor A crowdsourced global data set for validating built-up surface layers. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.17068160.v1
title: Metadata record for: A crowdsourced global data set for validating built-up surface layers

description: This dataset contains key characteristics about the data described in the Data Descriptor A crowdsourced global data set for validating built-up surface layers. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.18571106
title: Untitled Item

description: Experimental conductivity data for ionic liquidsComputed molecular descriptors for the corresponding ionic liquids
DOI: 10.6084/m9.figshare.18571106.v1
title: Untitled Item

description: Experimental conductivity data for ionic liquidsComputed molecular descriptors for the corresponding ionic liquids
DOI: 10.6084/m9.figshare.18571526
title: Predicted_Viscosity_Ionic_Liquids

description: Predicted Viscosity for Ionic Liquids using Machine Learning
DOI: 10.6084/m9.figshare.18571526.v1
title: Predicted_Viscosity_Ionic_Liquids

description: Predicted Viscosity for Ionic Liquids using Machine Learning
DOI: 10.6084/m9.figshare.18571901
title: Predicted_Conductivity_Ionic_Liquids

description: Predicted Conductivity for Ionic Liquids using Machine Learning
DOI: 10.6084/m9.figshare.18571901.v1
title: Predicted_Conductivity_Ionic_Liquids

description: Predicted Conductivity for Ionic Liquids using Machine Learning
DOI: 10.6084/m9.figshare.18572882
title: Heatmap_Viscosity_Ionic_Liquids

description: Heatmap of predicted viscosity for ionic liquids
DOI: 10.6084/m9.figshare.18572882.v1
title: Heatmap_Viscosity_Ionic_Liquids

description: Heatmap of predicted viscosity for ionic liquids
DOI: 10.7303/syn26842165
title: dsadsad
DOI: 10.7303/syn26842165.1
title: dsadsad
DOI: 10.3886/e120321v11-106980
title: COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes

description: This project aims to present a large dataset for researchers to discover public conversation on Twitter surrounding the COVID-19 pandemic. From 28 January 2020 to 1 September 2021, we collected over 198 million Twitter posts from more than 25 million unique users using four keywords: “corona”, “wuhan”, “nCov” and “covid”. Leveraging topic modeling techniques and pre-trained machine learning-based emotion analytic algorithms, we labeled each tweet with seventeen semantic attributes, including a) ten binary attributes indicating the tweet’s relevance or irrelevance to the top ten detected topics, b) five quantitative emotion attributes indicating the degree of intensity of the valence or sentiment (from 0: very negative to 1: very positive), and the degree of intensity of fear, anger, happiness and sadness emotions (from 0: not at all to 1: extremely intense), and c) two qualitative attributes indicating the sentiment category (very negative, negative, neutral or mixed, positive, very positive) and the dominant emotion category (fear, anger, happiness, sadness, no specific emotion) the tweet is mainly expressing.
description: Twitter posts.
description: The latest version has data updated up to 1 Sep 2021, including additional csv downloads for 29 countries)
DOI: 10.5281/zenodo.5866721
title: Erythroxylum coca zanfona pass 3

description: .
DOI: 10.5281/zenodo.5866720
title: Erythroxylum coca zanfona pass 3

description: .
DOI: 10.5281/zenodo.5866744
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Terrence Demos, https://orcid.org/0000-0003-0522-3708. Claims were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.5281/zenodo.5552642
title: Annotated images from yeast cell lifespans - Trainingset - DetecDiv (id01)

description: This dataset has been generated by manual annotation from timelapse images of yeast cells dividing using the DetecDiv software (see below). It contains ~200 000 images from 200 cell lifespans (each lifespan is made of between 700 and 1000 images). Each image is classified between 6 classes: "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog", according to the subfolder of the image. Besides, this folder also contains a .mat file containing 200 timeseries of classes corresponding to the lifespan of the 200 cells. It has been used to train the network doi.org/10.5281/zenodo.5553862 from the software DetecDiv: github.com/gcharvin/DetecDiv biorxiv.org/content/10.1101/2021.10.05.463175v1 <strong>Data type</strong>: 3D microscopy images (3 stacks brightfield) (.tif) + annotation (.mat) <strong>Microscopy data type</strong>: Brightfield images with 3 stacks <strong>Imaging</strong>: 20x 0.45 NA brightfield, 6.5µm*6.5µm sCMOS <strong>Cell type</strong>: Budding yeast wild type cell (BY4742) <strong>File format</strong>: .tif (16-bit RGB, 1 color per z-stack) + .mat <strong>Image size</strong>: 60x60x1 (Pixel size: x,y: 325 nm, 3*z: 3*1325 nm) <strong>Author(s)</strong>: Théo, ASPERT <strong>Contact email</strong>: theo.aspert@gmail.com <strong>Affiliation</strong>: IGBMC, Université de Strasbourg <strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.
description: {"references": ["DetecDiv, a deep-learning platform for automated cell division tracking and replicative lifespan analysis Theo Aspert, Didier Hentsch, Gilles Charvin bioRxiv 2021.10.05.463175; doi: https://doi.org/10.1101/2021.10.05.463175", "ASPERT Th\u00e9o. (2021). Trained network for classification of images from yeast cell lifespans - DetecDiv (id01) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5553862"]}
DOI: 10.5281/zenodo.5553861
title: Trained network for classification of images from yeast cell lifespans - DetecDiv (id01)

description: Trained network for classification of images from yeast cell lifespans. Related to the dataset: doi.org/10.5281/zenodo.5552642 Testset: doi.org/10.5281/zenodo.5866747 <strong>------------------------------------------</strong> <strong>Author(s)</strong>: Théo, ASPERT <strong>Contact email</strong>: theo.aspert@gmail.com <strong>Affiliation</strong>: IGBMC, Université de Strasbourg <strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.
description: {"references": ["DetecDiv, a deep-learning platform for automated cell division tracking and replicative lifespan analysis Theo Aspert, Didier Hentsch, Gilles Charvin bioRxiv 2021.10.05.463175; doi: https://doi.org/10.1101/2021.10.05.463175", "ASPERT Th\u00e9o. (2021). Annotated images from yeast cell lifespans - DetecDiv (id01) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5552642"]}
DOI: 10.5281/zenodo.5592983
title: Dataset pHluorin cells experiencing entry into quiescence

description: Dataset relative to https://www.biorxiv.org/content/10.1101/2020.11.25.395608v2 <strong>Data type</strong>: 2D microscopy images(.tif) <strong>Microscopy data type</strong>: Brightfield images + 2 colors <strong>Imaging</strong>: 60x 1.40 NA brightfield, 6.0µm*6.0µm sCMOS <strong>Cell type</strong>: Budding yeast cells (BY4742) + pHluorin <strong>File format</strong>: .tif (16-bit RGB, 1 color per z-stack). Each folder is a channel <strong>Image size</strong>: 2000x2000 pixels, binned to 2x2 <strong>Author(s)</strong>: Théo, ASPERT; Basile, JACQUEL; Gilles CHARVIN <strong>Contact email</strong>: theo.aspert@gmail.com; charvin@igbmc.fr <strong>Affiliation</strong>: IGBMC, Université de Strasbourg <strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02. <strong>Additional information: </strong>Framerate is indicated in the .rtf file.
DOI: 10.5281/zenodo.5592955
title: Dataset pHluorin cells experiencing entry into quiescence

description: Dataset relative to https://www.biorxiv.org/content/10.1101/2020.11.25.395608v2 <strong>Data type</strong>: 2D microscopy images(.tif) <strong>Microscopy data type</strong>: Brightfield images + 2 colors <strong>Imaging</strong>: 60x 1.40 NA brightfield, 6.0µm*6.0µm sCMOS <strong>Cell type</strong>: Budding yeast cells (BY4742) + pHluorin <strong>File format</strong>: .tif (16-bit RGB, 1 color per z-stack). Each folder is a channel <strong>Image size</strong>: 2000x2000 pixels, binned to 2x2 <strong>Author(s)</strong>: Théo, ASPERT; Basile, JACQUEL; Gilles CHARVIN <strong>Contact email</strong>: theo.aspert@gmail.com; charvin@igbmc.fr <strong>Affiliation</strong>: IGBMC, Université de Strasbourg <strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02. <strong>Additional information: </strong>Framerate is indicated in the .rtf file.
DOI: 10.5281/zenodo.5866747
title: Annotated images from yeast cell lifespans - Testset- DetecDiv (id01)

description: This dataset has been generated by manual annotation from timelapse images of yeast cells dividing using the DetecDiv software (see below). It contains ~ 35 000 images from 50 cell lifespans (each lifespan is made of between 700 and 1000 images). Each image is classified between 6 classes: "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog", according to the subfolder of the image. Besides, this folder also contains a .mat file containing 50 timeseries of classes corresponding to the lifespan of the 250 cells. It is related to the trained network doi.org/10.5281/zenodo.5553862 from the software DetecDiv: github.com/gcharvin/DetecDiv biorxiv.org/content/10.1101/2021.10.05.463175v1 <strong>Data type</strong>: 3D microscopy images (3 stacks brightfield) (.tif) + annotation (.mat) <strong>Microscopy data type</strong>: Brightfield images with 3 stacks <strong>Imaging</strong>: 20x 0.45 NA brightfield, 6.5µm*6.5µm sCMOS <strong>Cell type</strong>: Budding yeast wild type cell (BY4742) <strong>File format</strong>: .tif (16-bit RGB, 1 color per z-stack) + .mat <strong>Image size</strong>: 60x60x1 (Pixel size: x,y: 325 nm, 3*z: 3*1325 nm) <strong>Author(s)</strong>: Théo, ASPERT <strong>Contact email</strong>: theo.aspert@gmail.com <strong>Affiliation</strong>: IGBMC, Université de Strasbourg <strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.
description: {"references": ["DetecDiv, a deep-learning platform for automated cell division tracking and replicative lifespan analysis Theo Aspert, Didier Hentsch, Gilles Charvin bioRxiv 2021.10.05.463175; doi: https://doi.org/10.1101/2021.10.05.463175", "ASPERT Th\u00e9o. (2021). Trained network for classification of images from yeast cell lifespans - DetecDiv (id01) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5553862"]}
DOI: 10.5281/zenodo.5866746
title: Annotated images from yeast cell lifespans - Testset- DetecDiv (id01)

description: This dataset has been generated by manual annotation from timelapse images of yeast cells dividing using the DetecDiv software (see below). It contains ~ 35 000 images from 50 cell lifespans (each lifespan is made of between 700 and 1000 images). Each image is classified between 6 classes: "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog", according to the subfolder of the image. Besides, this folder also contains a .mat file containing 50 timeseries of classes corresponding to the lifespan of the 250 cells. It is related to the trained network doi.org/10.5281/zenodo.5553862 from the software DetecDiv: github.com/gcharvin/DetecDiv biorxiv.org/content/10.1101/2021.10.05.463175v1 <strong>Data type</strong>: 3D microscopy images (3 stacks brightfield) (.tif) + annotation (.mat) <strong>Microscopy data type</strong>: Brightfield images with 3 stacks <strong>Imaging</strong>: 20x 0.45 NA brightfield, 6.5µm*6.5µm sCMOS <strong>Cell type</strong>: Budding yeast wild type cell (BY4742) <strong>File format</strong>: .tif (16-bit RGB, 1 color per z-stack) + .mat <strong>Image size</strong>: 60x60x1 (Pixel size: x,y: 325 nm, 3*z: 3*1325 nm) <strong>Author(s)</strong>: Théo, ASPERT <strong>Contact email</strong>: theo.aspert@gmail.com <strong>Affiliation</strong>: IGBMC, Université de Strasbourg <strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.
description: {"references": ["DetecDiv, a deep-learning platform for automated cell division tracking and replicative lifespan analysis Theo Aspert, Didier Hentsch, Gilles Charvin bioRxiv 2021.10.05.463175; doi: https://doi.org/10.1101/2021.10.05.463175", "ASPERT Th\u00e9o. (2021). Trained network for classification of images from yeast cell lifespans - DetecDiv (id01) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5553862"]}