cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7340143
title: Entrain_example_brain_dataset

description: Single-cell RNA sequencing dataset of the developing mouse brain at E10 and E11, produced by [1], and processed by [2]. Data has been subset to E10 and E11 and subsampled to 5% of original cells. RNA Velocity matrix has been run on this dataset, using scvelo [3] workflow. Also attached, for convenience, is the required NicheNet database on intercellular regulation [4]. [1] La Manno G, Siletti K, Furlan A, Gyllborg D, Vinsland E, Mossi Albiach A, Mattsson Langseth C, Khven I, Lederer AR, Dratva LM, et al: <strong>Molecular architecture of the developing mouse brain.</strong> <em>Nature </em>2021, <strong>596:</strong>92-96. [2] Abdelaal T, Lelieveldt BPF, Reinders MJT, Mahfouz A: <strong>SIRV: Spatial inference of RNA velocity at the single-cell resolution.</strong> <em>bioRxiv </em>2021<strong>:</strong>2021.2007.2026.453774. [3] Bergen V, Lange M, Peidli S, Wolf FA, Theis FJ: <strong>Generalizing RNA velocity to transient cell states through dynamical modeling.</strong> <em>Nature Biotechnology </em>2020, <strong>38:</strong>1408-1414. [4] Browaeys R, Saelens W, Saeys Y: <strong>NicheNet: modeling intercellular communication by linking ligands to target genes.</strong> <em>Nat Methods </em>2020, <strong>17:</strong>159-162
DOI: 10.5281/zenodo.7787522
title: Entrain_example_brain_dataset

description: Single-cell RNA sequencing dataset of the developing mouse brain at E10 and E11, produced by [1], and processed by [2]. Data has been subset to E10 and E11 and subsampled to 5% of original cells. RNA Velocity matrix has been run on this dataset, using scvelo [3] workflow. Also attached, for convenience, is the required NicheNet database on intercellular regulation [4]. [1] La Manno G, Siletti K, Furlan A, Gyllborg D, Vinsland E, Mossi Albiach A, Mattsson Langseth C, Khven I, Lederer AR, Dratva LM, et al: <strong>Molecular architecture of the developing mouse brain.</strong> <em>Nature </em>2021, <strong>596:</strong>92-96. [2] Abdelaal T, Lelieveldt BPF, Reinders MJT, Mahfouz A: <strong>SIRV: Spatial inference of RNA velocity at the single-cell resolution.</strong> <em>bioRxiv </em>2021<strong>:</strong>2021.2007.2026.453774. [3] Bergen V, Lange M, Peidli S, Wolf FA, Theis FJ: <strong>Generalizing RNA velocity to transient cell states through dynamical modeling.</strong> <em>Nature Biotechnology </em>2020, <strong>38:</strong>1408-1414. [4] Browaeys R, Saelens W, Saeys Y: <strong>NicheNet: modeling intercellular communication by linking ligands to target genes.</strong> <em>Nat Methods </em>2020, <strong>17:</strong>159-162
DOI: 10.5281/zenodo.7196701
title: Entrain_example_brain_dataset

description: Single-cell RNA sequencing dataset of the developing mouse brain at E10 and E11, produced by [1], and processed by [2]. Data has been subset to E10 and E11 and subsampled to 5% of original cells. RNA Velocity matrix has been run on this dataset, using scvelo [3] workflow. Also attached, for convenience, is the required NicheNet database on intercellular regulation [4]. [1] La Manno G, Siletti K, Furlan A, Gyllborg D, Vinsland E, Mossi Albiach A, Mattsson Langseth C, Khven I, Lederer AR, Dratva LM, et al: <strong>Molecular architecture of the developing mouse brain.</strong> <em>Nature </em>2021, <strong>596:</strong>92-96. [2] Abdelaal T, Lelieveldt BPF, Reinders MJT, Mahfouz A: <strong>SIRV: Spatial inference of RNA velocity at the single-cell resolution.</strong> <em>bioRxiv </em>2021<strong>:</strong>2021.2007.2026.453774. [3] Bergen V, Lange M, Peidli S, Wolf FA, Theis FJ: <strong>Generalizing RNA velocity to transient cell states through dynamical modeling.</strong> <em>Nature Biotechnology </em>2020, <strong>38:</strong>1408-1414. [4] Browaeys R, Saelens W, Saeys Y: <strong>NicheNet: modeling intercellular communication by linking ligands to target genes.</strong> <em>Nat Methods </em>2020, <strong>17:</strong>159-162
DOI: 10.5281/zenodo.7874401
title: Entrain_example_brain_dataset

description: Single-cell RNA sequencing dataset of the developing mouse brain at E10 and E11, produced by [1], and processed by [2]. Data has been subset to E10 and E11 and subsampled to 5% of original cells. RNA Velocity matrix has been run on this dataset, using scvelo [3] workflow. Also attached, for convenience, is the required NicheNet database on intercellular regulation [4]. [1] La Manno G, Siletti K, Furlan A, Gyllborg D, Vinsland E, Mossi Albiach A, Mattsson Langseth C, Khven I, Lederer AR, Dratva LM, et al: <strong>Molecular architecture of the developing mouse brain.</strong> <em>Nature </em>2021, <strong>596:</strong>92-96. [2] Abdelaal T, Lelieveldt BPF, Reinders MJT, Mahfouz A: <strong>SIRV: Spatial inference of RNA velocity at the single-cell resolution.</strong> <em>bioRxiv </em>2021<strong>:</strong>2021.2007.2026.453774. [3] Bergen V, Lange M, Peidli S, Wolf FA, Theis FJ: <strong>Generalizing RNA velocity to transient cell states through dynamical modeling.</strong> <em>Nature Biotechnology </em>2020, <strong>38:</strong>1408-1414. [4] Browaeys R, Saelens W, Saeys Y: <strong>NicheNet: modeling intercellular communication by linking ligands to target genes.</strong> <em>Nat Methods </em>2020, <strong>17:</strong>159-162
DOI: 10.5281/zenodo.8170404
title: Entrain_example_brain_dataset

description: Single-cell RNA sequencing dataset of the developing mouse brain at E10 and E11, produced by [1], and processed by [2]. Data has been subset to E10 and E11 and subsampled to 5% of original cells. RNA Velocity matrix has been run on this dataset, using scvelo [3] workflow. Also attached, for convenience, is the required NicheNet database on intercellular regulation [4]. [1] La Manno G, Siletti K, Furlan A, Gyllborg D, Vinsland E, Mossi Albiach A, Mattsson Langseth C, Khven I, Lederer AR, Dratva LM, et al: <strong>Molecular architecture of the developing mouse brain.</strong> <em>Nature </em>2021, <strong>596:</strong>92-96. [2] Abdelaal T, Lelieveldt BPF, Reinders MJT, Mahfouz A: <strong>SIRV: Spatial inference of RNA velocity at the single-cell resolution.</strong> <em>bioRxiv </em>2021<strong>:</strong>2021.2007.2026.453774. [3] Bergen V, Lange M, Peidli S, Wolf FA, Theis FJ: <strong>Generalizing RNA velocity to transient cell states through dynamical modeling.</strong> <em>Nature Biotechnology </em>2020, <strong>38:</strong>1408-1414. [4] Browaeys R, Saelens W, Saeys Y: <strong>NicheNet: modeling intercellular communication by linking ligands to target genes.</strong> <em>Nat Methods </em>2020, <strong>17:</strong>159-162
DOI: 10.5281/zenodo.8170412
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Johannes Johannessen, http://www.wikidata.org/entity/Q109345602. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q109345602
DOI: 10.5281/zenodo.8170411
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Johannes Johannessen, http://www.wikidata.org/entity/Q109345602. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q109345602
DOI: 10.25919/24fg-2q32
title: 1986 Annaburroo Experimental Grassland Fire Data

description: In 1986 CSIRO conducted a very large series of fire experiments in grasslands at Annaburroo Station in the Northern Territory. This work was supported by a number of rural fire authorities with the objective of quantifying the effect of fuel load on the behaviour and spread of free-burning grass fires. This collection collates the data collected from this experimental series which were used to develop the CSIRO Grassland Fire Spread Model published in 1998. Data include measurements and observations of fuel, weather and fire behaviour
DOI: 10.21947/1991102
title: Site A1 - PNNL Surface Flux Station / Raw Data

description: Raw time series of fast response wind and virtual sonic temperature data (nominal 20Hz) for calculation of the turbulent fluxes; measured at 4 mAGL
DOI: 10.21947/1991103
title: Site A1 - PNNL Surface Flux Station / Daily Fluxes

description: Preliminary processed turbulent fluxes; compilation of 30-minute estimates for one day
DOI: 10.21947/1991104
title: Site A1 - PNNL Surface Flux Station / Processed Fluxes

description: Preliminary processed turbulent fluxes and relevant statistics; 30-minute estimates
DOI: 10.21947/1991106
title: Site H - PNNL Surface Flux Station / Raw Data

description: Raw time series of fast response wind and virtual sonic temperature data (nominal 20Hz) for calculation of the turbulent fluxes; measured at 4 mAGL
DOI: 10.21947/1991107
title: Site H - PNNL Surface Flux Station / Processed Fluxes

description: Preliminary processed turbulent fluxes and relevant statistics; 30-minute estimate
DOI: 10.21947/1991108
title: Site H - PNNL Surface Flux Station / Daily Fluxes

description: Preliminary processed turbulent fluxes; compilation of 30-minute estimates for one day
DOI: 10.5439/1991165
title: lblch1atm (c1)

description: lblch1atm
DOI: 10.5439/1991166
title: lblch2trailer (c1)

description: lblch2trailer
DOI: 10.5281/zenodo.8170442
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Victor Arthur Erich Daecke, http://www.wikidata.org/entity/Q113675503. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q113675503
DOI: 10.5281/zenodo.8170441
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Victor Arthur Erich Daecke, http://www.wikidata.org/entity/Q113675503. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q113675503
DOI: 10.5281/zenodo.8170403
title: datasets of submicron Spatial Resolution Pb-Pb Datingof Chang'e-5 Basalt

description: The ions image and Pb-Pb data of CE5 basalt using NanoSIMS.
DOI: 10.5281/zenodo.8170402
title: datasets of submicron Spatial Resolution Pb-Pb Datingof Chang'e-5 Basalt

description: The ions image and Pb-Pb data of CE5 basalt using NanoSIMS.
DOI: 10.5281/zenodo.8170446
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by John Cyrus Cahoon, http://www.wikidata.org/entity/Q109753746. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q109753746
DOI: 10.5281/zenodo.8170445
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by John Cyrus Cahoon, http://www.wikidata.org/entity/Q109753746. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q109753746
DOI: 10.5281/zenodo.8170447
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Sigvard Jacob Dannefaerd, http://www.wikidata.org/entity/Q96595340. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q96595340
DOI: 10.5281/zenodo.8170448
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Sigvard Jacob Dannefaerd, http://www.wikidata.org/entity/Q96595340. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q96595340
DOI: 10.25345/c5w37m58t
title: MassIVE MSV000092494 - Tea leaves in insect-attacked area
title: to find variations of compositions in highly and moderately insect-attacked leaves
DOI: 10.5281/zenodo.8170450
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by John Roscoe Hendrickson, http://www.wikidata.org/entity/Q21389860. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21389860
DOI: 10.5281/zenodo.8170449
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by John Roscoe Hendrickson, http://www.wikidata.org/entity/Q21389860. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21389860
DOI: 10.5281/zenodo.8170459
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Hugh Berthon Preston, http://www.wikidata.org/entity/Q56228996. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q56228996
DOI: 10.5281/zenodo.8170458
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Hugh Berthon Preston, http://www.wikidata.org/entity/Q56228996. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q56228996
DOI: 10.6084/m9.figshare.23722062
title: A novel time-dependent electric vehicle pickup and delivery problem with time windows in cold supply chains

description: This is the experimental data of the manuscript entitled “A novel time-dependent electric vehicle pickup and delivery problem with time windows in cold supply chains”
DOI: 10.6084/m9.figshare.23722062.v1
title: A novel time-dependent electric vehicle pickup and delivery problem with time windows in cold supply chains

description: This is the experimental data of the manuscript entitled “A novel time-dependent electric vehicle pickup and delivery problem with time windows in cold supply chains”
DOI: 10.5281/zenodo.8170370
title: ATLAS Database — Videos

description: We need affect-based stimuli specifically conceived to investigate architectural spaces. The RESONANCES project crafted ATLAS, a dATabase of visuaL Atmospheric Stimuli. It collects a series of spatial patterns born from a systematic selection of generators of atmosphere. Generators of atmosphere are architectural features designed to afford atmospheric effects (such as lights, colors, materials, and proportions). ATLAS is an open-access tool that supports researchers interested in studying emotional reactions to architectural features by providing reliable, standardized, and reproducible stimuli. In this dataset, ATLAS stimuli are presented as videos.
description: This database was developed within the Resonances project — Architectural Atmospheres: The Emotional Impact of Ambiances Measured through Conscious, Bodily, and Neural Responses. This project received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 101025132. The content of this work reflects only the authors' view. The European Research Executive Agency is not responsible for any use that may be made of the information it contains.
DOI: 10.5281/zenodo.8170369
title: ATLAS Database — Videos

description: We need affect-based stimuli specifically conceived to investigate architectural spaces. The RESONANCES project crafted ATLAS, a dATabase of visuaL Atmospheric Stimuli. It collects a series of spatial patterns born from a systematic selection of generators of atmosphere. Generators of atmosphere are architectural features designed to afford atmospheric effects (such as lights, colors, materials, and proportions). ATLAS is an open-access tool that supports researchers interested in studying emotional reactions to architectural features by providing reliable, standardized, and reproducible stimuli. In this dataset, ATLAS stimuli are presented as videos.
description: This database was developed within the Resonances project — Architectural Atmospheres: The Emotional Impact of Ambiances Measured through Conscious, Bodily, and Neural Responses. This project received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 101025132. The content of this work reflects only the authors' view. The European Research Executive Agency is not responsible for any use that may be made of the information it contains.
DOI: 10.6084/m9.figshare.23722047
title: Intelligent Data-Driven Acquisition Method for User Requirements

description: Consumer behavior has changed due to digitization. Online shoppers now refer to user reviews containing comprehensive data produced in real-time, which can be used to determine users’ needs. This paper combines Kansei engineering and natural language processing techniques to extract information on users’ needs from online reviews and provide guidance for subsequent product improvements and development. A crawler tool was used to collect a large number of online reviews for a target product. Frequency analysis was then applied to the text to filter out the product components worth analyzing. The results were categorized and aggregated by experts before sentiment analysis was performed on statements containing the selected adjectives. Finally, the user needs identified could be inputted to Kansei engineering for further product design. This paper verifies the merit of the above method when applied to the mountain bike product category on Amazon. The method proved to be a quick and efficient way to attain accurate product evaluations from end-users and thus represents a feasible approach to intelligently determining user preferences.
DOI: 10.6084/m9.figshare.23722047.v1
title: Intelligent Data-Driven Acquisition Method for User Requirements

description: Consumer behavior has changed due to digitization. Online shoppers now refer to user reviews containing comprehensive data produced in real-time, which can be used to determine users’ needs. This paper combines Kansei engineering and natural language processing techniques to extract information on users’ needs from online reviews and provide guidance for subsequent product improvements and development. A crawler tool was used to collect a large number of online reviews for a target product. Frequency analysis was then applied to the text to filter out the product components worth analyzing. The results were categorized and aggregated by experts before sentiment analysis was performed on statements containing the selected adjectives. Finally, the user needs identified could be inputted to Kansei engineering for further product design. This paper verifies the merit of the above method when applied to the mountain bike product category on Amazon. The method proved to be a quick and efficient way to attain accurate product evaluations from end-users and thus represents a feasible approach to intelligently determining user preferences.
DOI: 10.25442/hku.23662146
title: Supporting data for "Partitioning and catalysis in all-aqueous multiphase microfluidics: from interfacial phenomena to applications in analyte detection".

description: The thesis explores the use of microfluidics and liquid-liquid phase separation in virus detection. Specifically, it investigates a phase separation-assisted and microfluidics-based method for isolating and enriching target analytes from background signals. The dataset includes schematic figures and experimental raw data that support the thesis projects.
DOI: 10.25442/hku.23662146.v1
title: Supporting data for "Partitioning and catalysis in all-aqueous multiphase microfluidics: from interfacial phenomena to applications in analyte detection".

description: The thesis explores the use of microfluidics and liquid-liquid phase separation in virus detection. Specifically, it investigates a phase separation-assisted and microfluidics-based method for isolating and enriching target analytes from background signals. The dataset includes schematic figures and experimental raw data that support the thesis projects.
DOI: 10.6084/m9.figshare.23722101
title: Datailed farm task catalogue

description: Excel spreadsheet containing detailed descritpion of farm tasks
DOI: 10.6084/m9.figshare.23722101.v1
title: Datailed farm task catalogue

description: Excel spreadsheet containing detailed descritpion of farm tasks
DOI: 10.25442/hku.23666583
title: <em>Supporting data for</em> “Peroxidase- and Photosensitizer-Mediated Proximity Labeling for Mitochondria-associated Transcriptome Analysis in Live Cells”

description: Folder 1: Confocal microscope data Includes confocal images in tif format. Short description: The data were collected using confocal microscope (Leica) to examine the colocalization of labeling sites and commercial organelle trackers. <br> Folder 2: Nuclear magnetic resonance spectroscopy (NMR) data Includes proton (1H) and carbon-13 (13C) spectra in MNOVA format. Short description: NMR data were collected using Bruker Advanced spectrometers to characterize the purity of our probes, and all NMR spectra were analyzed using MestReNova reader software. <br> Folder 3: UV-Vis absorption spectra data Includes absorbance analysis of our probes in xlsx format. Short description: UV-Vis absorption spectra were collected using Varian Cary 50 spectrometer to provide information of excitation wavelengths. <br> Folder 4: Spectrofluorometer (FL) data Includes: FL spectra data in xlsx format. Short description: FL spectra data were collected using the FS5 spectrofluorometer (Edinburgh Instruments). Probes displayed the fluorescence intensities and peaks. <br> Folder 5: Dynamic light scattering (DLS) data Includes: DLS data in xlsx format. Short description: DLS (Zetasizer Pro, Malvern, Germany) was utilized to determine the size distribution of our probes. The ZS Xplorer software was used to analyse the hydrodynamic diameter of each test.