cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27922329.v2
title: Data Set.sav

description: The rise of deepfakes, AI-generated synthetic media, poses significant threats to the political arena. This study investigates how motivated reasoning influences responses to political deepfakes, and whether AI disclosure labels can encourage systematic information processing. In a mixed experiment with attitude consistency (consistent vs. inconsistent) and AI label (present vs. absent) as between-subject factors and time (pre vs. post) as a within-subject factor, 233 Republican participants were randomly exposed to AI-manipulated speeches of Donald Trump expressing either conservative or liberal immigration views, with or without AI labels. Results showed that attitude consistency significantly reduced skepticism toward deepfakes, while AI labels neither increased skepticism nor moderated the relationship between attitude consistency and skepticism. Lower skepticism led to greater attitude change on immigration policies and increased intentions to share deepfakes. Additionally, skepticism mediated the relationship between attitude consistency and sharing intentions. Theoretical contributions and implications are discussed.<br>
DOI: 10.6084/m9.figshare.27922329.v1
title: Data Set.sav

description: The rise of deepfakes, AI-generated synthetic media, poses significant threats to the political arena. This study investigates how motivated reasoning influences responses to political deepfakes, and whether AI disclosure labels can encourage systematic information processing. In a mixed experiment with attitude consistency (consistent vs. inconsistent) and AI label (present vs. absent) as between-subject factors and time (pre vs. post) as a within-subject factor, 233 Republican participants were randomly exposed to AI-manipulated speeches of Donald Trump expressing either conservative or liberal immigration views, with or without AI labels. Results showed that attitude consistency significantly reduced skepticism toward deepfakes, while AI labels neither increased skepticism nor moderated the relationship between attitude consistency and skepticism. Lower skepticism led to greater attitude change on immigration policies and increased intentions to share deepfakes. Additionally, skepticism mediated the relationship between attitude consistency and sharing intentions. Theoretical contributions and implications are discussed.<br>
DOI: 10.15468/dl.knf7gw
title: Occurrence Download

description: A dataset containing 6522 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is Mexico",
 "TaxonKey is Urocyon cinereoargenteus (Schreber, 1775)"
 ]
}

 The dataset includes 6522 records from 114 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003342-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14232606
title: Electron Tomography of Inner Ear Ribbon Synapses

description: This dataset contains room-temperature electron tomograms of murine and rat inner ear ribbon synapses.It was used for training and evaluation of ribbon synapse structure segmentation in [1]. The data contains 6 tomograms from mice that were initially published in two different studies [2, 3], as well as 3 additional tomograms that were acquired following the same protocol as [3] but that were not included in that publication.It contains 19 tomograms from rats from [4].The data is organized into three different subfolders "rat", "tether" and "vesicle_pools", containing tomograms and annotations stored as hdf5 files.Each hdf5 file contains the following internal datasets:- raw: The tomogram data.- labels/PD: Annotations for the presynaptic density, annotated with IMOD and exported to a binary mask.- labels/PSD: Annotations for the postsynaptic density, annotated with IMOD and exported to a binary mask.- labels/membrane: Annotations for the membrane of the active zone, annotated with IMOD and exported to a binary mask.- labels/ribbon: Annotations for the synaptic ribbon, annotated with IMOD and exported to a binary mask.- labels/vesicles: Annotations for the vesicles close to the active zone. Annotated with IMOD and exported to instance masks.

[1] Muth, Moschref et al., 2024, Preprint to be published[2] Chakrabarti et al. Vesicle sub‐pool organization at inner hair cell ribbon synapses, EMBO reports, 2018, DOI: 10.15252/embr.201744937[3] Chakrabarti et al., Optogenetics and electron tomography for structure-function analysis of cochlear ribbon synapses, eLife, 2022, DOI: 10.7554/eLife.79494[4] Michanski, Kapoor et al., Piccolino is required for ribbon architecture at cochlear inner hair cell synapses and for hearing, EMBO press, 2023, DOI: 10.15252/embr.202256702
DOI: 10.5281/zenodo.14232607
title: Electron Tomography of Inner Ear Ribbon Synapses

description: This dataset contains room-temperature electron tomograms of murine and rat inner ear ribbon synapses.It was used for training and evaluation of ribbon synapse structure segmentation in [1]. The data contains 6 tomograms from mice that were initially published in two different studies [2, 3], as well as 3 additional tomograms that were acquired following the same protocol as [3] but that were not included in that publication.It contains 19 tomograms from rats from [4].The data is organized into three different subfolders "rat", "tether" and "vesicle_pools", containing tomograms and annotations stored as hdf5 files.Each hdf5 file contains the following internal datasets:- raw: The tomogram data.- labels/PD: Annotations for the presynaptic density, annotated with IMOD and exported to a binary mask.- labels/PSD: Annotations for the postsynaptic density, annotated with IMOD and exported to a binary mask.- labels/membrane: Annotations for the membrane of the active zone, annotated with IMOD and exported to a binary mask.- labels/ribbon: Annotations for the synaptic ribbon, annotated with IMOD and exported to a binary mask.- labels/vesicles: Annotations for the vesicles close to the active zone. Annotated with IMOD and exported to instance masks.

[1] Muth, Moschref et al., 2024, Preprint to be published[2] Chakrabarti et al. Vesicle sub‐pool organization at inner hair cell ribbon synapses, EMBO reports, 2018, DOI: 10.15252/embr.201744937[3] Chakrabarti et al., Optogenetics and electron tomography for structure-function analysis of cochlear ribbon synapses, eLife, 2022, DOI: 10.7554/eLife.79494[4] Michanski, Kapoor et al., Piccolino is required for ribbon architecture at cochlear inner hair cell synapses and for hearing, EMBO press, 2023, DOI: 10.15252/embr.202256702
DOI: 10.15468/dl.dbu6vr
title: Occurrence Download

description: A dataset containing 4302 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Solanum granulosoleprosum Dunal"
 ]
}

 The dataset includes 4302 records from 169 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003343-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cek73k
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Country is Belize",
 "DatasetKey is one of (Fairchild Tropical Botanic Garden (FTG), Tropicos MO Specimen Data, The New York Botanical Garden Herbarium (NY), Rapid Assessment Program (RAP) Biodiversity Survey Database, iNaturalist Research-grade Observations, Digitalización y Sistematización de las Colecciones Biológicas Nacionales del Instituto de Biología, UNAM (Plantas vasculares), NMNH Extant Specimen Records (USNM, US), University of Michigan Herbarium)",
 "OccurrenceStatus is Present",
 "TaxonKey is Reinhardtia gracilis (H.Wendl.) Burret"
 ]
}

 The dataset includes 21 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003344-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k7b9sr
title: Occurrence Download

description: A dataset containing 2254 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Continent is North America",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Quercus obtusata Bonpl."
 ]
}

 The dataset includes 2254 records from 71 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003345-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7tqvsy
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((0.7 51.6,0.8 51.6,0.8 51.7,0.7 51.7,0.7 51.6))",
 "OccurrenceStatus is Present",
 "TaxonKey is Pipistrellus pipistrellus (Schreber, 1774)"
 ]
}

 The dataset includes 19 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003117-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15139/s3/i5h43p
title: AMV

description: AMV Data
DOI: 10.15468/dl.rn388e
title: Occurrence Download

description: A dataset containing 7270 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is COLETA - IMAR/DOP-Uac reference collection from 1977 to 2012"
 ]
}

 The dataset includes 7270 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003118-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14917764
title: AMPERE Summary Plot Movies (2021-Jul)

description: 2021-Jul AMPERE summary plot movies. See included zenodo_summary_plot_movie_description document for details.
DOI: 10.5281/zenodo.14917765
title: AMPERE Summary Plot Movies (2021-Jul)

description: 2021-Jul AMPERE summary plot movies. See included zenodo_summary_plot_movie_description document for details.
DOI: 10.5281/zenodo.10262913
title: Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target

description: Microwell-based (BD Rhapsody) scRNA-seq of Hairy Cell Leukemia Patients published in 



Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target, Jan-Paul Bohn et al. Submitted.


The files will be made available upon publication. 

Description of the files



01_raw_counts: count matrices as CSV as generated by the BD Rhapsody WTA analysis pipeline

10_prepare_adata: Load BD Rhapsody WTA analysis pipeline outputs into AnnData objects and add metadata.

20_scrnaseq_qc: Use a nextflow pipeline (stored in lib/single-cell-analysis-nf) to perform threshold-based filtering of single-cell data and apply SOLO for doublet detection.

30_merge_adata: Merge samples into a single AnnData object, train a scVI model for batch effect removal, and annotate cell-types based on unsupervised clustering

40_cluster_analysis: Identify and investigate subclusters representing cell-states that go beyond the major cell-types

50_de_analysis: Generate pseudobulk and perform differential gene expression analysis using DESeq2 (based on a wrapper script stored in lib/deseq2_workflow)

70_downstream_analysis: Perform pathway analyses and generate figures for publication based on the data generated in the previous steps

containers: Conda environments used for the analysis packed up as singularity containers.
DOI: 10.5281/zenodo.10262912
title: Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target

description: Microwell-based (BD Rhapsody) scRNA-seq of Hairy Cell Leukemia Patients published in 



Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target, Jan-Paul Bohn et al. Submitted.


The files will be made available upon publication. 

Description of the files



01_raw_counts: count matrices as CSV as generated by the BD Rhapsody WTA analysis pipeline

10_prepare_adata: Load BD Rhapsody WTA analysis pipeline outputs into AnnData objects and add metadata.

20_scrnaseq_qc: Use a nextflow pipeline (stored in lib/single-cell-analysis-nf) to perform threshold-based filtering of single-cell data and apply SOLO for doublet detection.

30_merge_adata: Merge samples into a single AnnData object, train a scVI model for batch effect removal, and annotate cell-types based on unsupervised clustering

40_cluster_analysis: Identify and investigate subclusters representing cell-states that go beyond the major cell-types

50_de_analysis: Generate pseudobulk and perform differential gene expression analysis using DESeq2 (based on a wrapper script stored in lib/deseq2_workflow)

70_downstream_analysis: Perform pathway analyses and generate figures for publication based on the data generated in the previous steps

containers: Conda environments used for the analysis packed up as singularity containers.
DOI: 10.5281/zenodo.14917813
title: Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target

description: Microwell-based (BD Rhapsody) scRNA-seq of Hairy Cell Leukemia Patients published in 



Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target, Jan-Paul Bohn et al. Submitted.


The files will be made available upon publication. 

Description of the files



01_raw_counts: count matrices as CSV as generated by the BD Rhapsody WTA analysis pipeline

10_prepare_adata: Load BD Rhapsody WTA analysis pipeline outputs into AnnData objects and add metadata.

20_scrnaseq_qc: Use a nextflow pipeline (stored in lib/single-cell-analysis-nf) to perform threshold-based filtering of single-cell data and apply SOLO for doublet detection.

30_merge_adata: Merge samples into a single AnnData object, train a scVI model for batch effect removal, and annotate cell-types based on unsupervised clustering

40_cluster_analysis: Identify and investigate subclusters representing cell-states that go beyond the major cell-types

50_de_analysis: Generate pseudobulk and perform differential gene expression analysis using DESeq2 (based on a wrapper script stored in lib/deseq2_workflow)

70_downstream_analysis: Perform pathway analyses and generate figures for publication based on the data generated in the previous steps

containers: Conda environments used for the analysis packed up as singularity containers.
DOI: 10.5281/zenodo.14917844
title: Jumbo phages analysis

description: We collected 59,652,008 putative viral genomes from seven habitats by using 38 public metagenome datasets, an integrated public viral genome database (IGNR), and pig gut viral genome databases. We obtained 10,754 jumbo phage genomes with sizes ranging from 200 to 831 kb. This study provides an important resource and new knowledge for future studies on the interaction between jumbo phages and their bacterial or archaeal hosts, as well as how this interaction affects pig health and production traits.
DOI: 10.5281/zenodo.14917845
title: Jumbo phages analysis

description: We collected 59,652,008 putative viral genomes from seven habitats by using 38 public metagenome datasets, an integrated public viral genome database (IGNR), and pig gut viral genome databases. We obtained 10,754 jumbo phage genomes with sizes ranging from 200 to 831 kb. This study provides an important resource and new knowledge for future studies on the interaction between jumbo phages and their bacterial or archaeal hosts, as well as how this interaction affects pig health and production traits.
DOI: 10.5281/zenodo.14330167
title: Volcanic Information System (VIS) Dataset: global volcanic eruptive activity records using IMS infrasound detections

description: The Volcanic Information System (VIS) is a detection algorithm designed to identify and characterize explosive volcanic eruptions based on long-range infrasound observations (De Negri et al., 2024). VIS utilizes data from a single array to detect infrasound emitted by ongoing volcanic eruptions and applies normalized thresholds to parameterize the eruption. This process generates records of increased volcanic activity. The parameterization relies on the Infrasound Parameter (IP), a detection algorithm initially developed for local infrasound early warning systems (Ulivieri et al., 2013; Ripepe et al., 2018), and later adapted for long-range observations (Marchetti et al., 2019; LePichon et al., 2021; Gheri et al., 2023). The IP is the core component of VIS and enables the characterization of infrasound signals in terms of signal strength and persistency (see Gheri et al., 2023 and Gheri et al., in press).

This dataset presents the results from VIS applied to data collected from 16 International Monitoring System (IMS) arrays over a period of ten years (2010-2019), as detailed in Gheri et al. (in press). VIS is a binary system that reflects the status of a volcano based on long-range infrasound data over time. When the range-corrected IP exceeds a predefined threshold, a VIS record is generated, potentially indicating a volcanic eruption. Each VIS record is identified by its start time (tstart) and end time (tend), corresponding to the infrasound signals radiated by the volcano.

The dataset includes 419 VIS records associated with volcanic eruptions, based on comparisons with Global Volcanism Program (GVP) bulletin reports. These records correspond to volcanoes located within 2000 km of the IMS arrays. For each .mat file, the following information is provided:



tstart: onset time of the eruption

tend: end time of the eruption

Psmean: pressure amplitude at the source

Ndet: signal persistency

Range-corrected IP: Infrasound Parameter for the event.


This dataset provides valuable insights into volcanic activity, combining infrasound data with eruption timelines to enhance monitoring capabilities.

The University of Florence acknowledges the contribution of the National Recovery and Resilience Plan, Mission 4 Component 2 -- Investment 1.4 -- NATIONAL CENTER FOR HPC, BIG DATA AND QUANTUM COMPUTING -- funded by the European Union -- NextGenerationEU -- CUP B83C22002830001.
DOI: 10.5281/zenodo.14330168
title: Volcanic Information System (VIS) Dataset: global volcanic eruptive activity records using IMS infrasound detections

description: The Volcanic Information System (VIS) is a detection algorithm designed to identify and characterize explosive volcanic eruptions based on long-range infrasound observations (De Negri et al., 2024). VIS utilizes data from a single array to detect infrasound emitted by ongoing volcanic eruptions and applies normalized thresholds to parameterize the eruption. This process generates records of increased volcanic activity. The parameterization relies on the Infrasound Parameter (IP), a detection algorithm initially developed for local infrasound early warning systems (Ulivieri et al., 2013; Ripepe et al., 2018), and later adapted for long-range observations (Marchetti et al., 2019; LePichon et al., 2021; Gheri et al., 2023). The IP is the core component of VIS and enables the characterization of infrasound signals in terms of signal strength and persistency (see Gheri et al., 2023 and Gheri et al., in press).

This dataset presents the results from VIS applied to data collected from 16 International Monitoring System (IMS) arrays over a period of ten years (2010-2019), as detailed in Gheri et al. (in press). VIS is a binary system that reflects the status of a volcano based on long-range infrasound data over time. When the range-corrected IP exceeds a predefined threshold, a VIS record is generated, potentially indicating a volcanic eruption. Each VIS record is identified by its start time (tstart) and end time (tend), corresponding to the infrasound signals radiated by the volcano.

The dataset includes 419 VIS records associated with volcanic eruptions, based on comparisons with Global Volcanism Program (GVP) bulletin reports. These records correspond to volcanoes located within 2000 km of the IMS arrays. For each .mat file, the following information is provided:



tstart: onset time of the eruption

tend: end time of the eruption

Psmean: pressure amplitude at the source

Ndet: signal persistency

Range-corrected IP: Infrasound Parameter for the event.


This dataset provides valuable insights into volcanic activity, combining infrasound data with eruption timelines to enhance monitoring capabilities.

The University of Florence acknowledges the contribution of the National Recovery and Resilience Plan, Mission 4 Component 2 -- Investment 1.4 -- NATIONAL CENTER FOR HPC, BIG DATA AND QUANTUM COMPUTING -- funded by the European Union -- NextGenerationEU -- CUP B83C22002830001.
DOI: 10.5281/zenodo.14917852
title: Global Spiral Freezer Market 2025 To 2034

description: Spiral Freezer Market Size, Trends and Insights By Application (Meat Processing, Bakery, Seafood, Others), By Capacity (Small Capacity, Medium Capacity, Large Capacity), By Business (Aftermarket, OEM), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

Global Spiral Freezer Market is projected to experience significant growth from 2024 to 2033. The market is anticipated to grow at a Compound Annual Growth Rate (CAGR) of approximately 7.5% during the forecast period, with the market size estimated at USD 5.87 Billion in 2024 and expected to reach USD 8.92 Billion by 2033.

The demand for spiral freezers is driven by their efficiency in quick-freezing food products while maintaining their quality and texture. The growing demand for frozen food products, particularly in the food processing industry, is propelling market growth.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=56474
DOI: 10.5281/zenodo.14917853
title: Global Spiral Freezer Market 2025 To 2034

description: Spiral Freezer Market Size, Trends and Insights By Application (Meat Processing, Bakery, Seafood, Others), By Capacity (Small Capacity, Medium Capacity, Large Capacity), By Business (Aftermarket, OEM), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

Global Spiral Freezer Market is projected to experience significant growth from 2024 to 2033. The market is anticipated to grow at a Compound Annual Growth Rate (CAGR) of approximately 7.5% during the forecast period, with the market size estimated at USD 5.87 Billion in 2024 and expected to reach USD 8.92 Billion by 2033.

The demand for spiral freezers is driven by their efficiency in quick-freezing food products while maintaining their quality and texture. The growing demand for frozen food products, particularly in the food processing industry, is propelling market growth.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=56474
DOI: 10.5281/zenodo.14917831
title: AMPERE Summary Plot Movies (2021-Aug)

description: 2021-Aug AMPERE summary plot movies. See included zenodo_summary_plot_movie_description document for details.
DOI: 10.5281/zenodo.14917832
title: AMPERE Summary Plot Movies (2021-Aug)

description: 2021-Aug AMPERE summary plot movies. See included zenodo_summary_plot_movie_description document for details.