cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14979763
title: BIO407 Group3 Image processing of live cells, treated with DFX

description: ## Study:This data is part of the practical course BIO407 2025 at the University of Zurich, titled 'Advanced Microscopy: From preparation to data and visualization'.The effects of Deferasirox (DFX) on mitochondria morphology were examined using different microscopy techniques.The data is originally from the paper:Gottwald EM, Schuh CD, Dr點ker P, Haenni D, Pearson A, Ghazi S, Bugarski M, Polesel M, Duss M, Landau EM, Kaech A, Ziegler U, Lundby AKM, Lundby C, Dittrich PS, Hall AM. The iron chelator Deferasirox causes severe mitochondrial swelling without depolarization due to a specific effect on inner membrane permeability. Sci Rep. 2020 Jan 31;10(1):1577. doi: 10.1038/s41598-020-58386-9. PMID: 32005861; PMCID: PMC6994599.

## Study Component:Job: Image ProcessingTask: 4) Timelapse of mitochondria in deferasirox (DFX) treated cellsTimelapse data was analyzed: Mitochondria were segmented, their morphology and signal intensity were measured over time, and plotted.

## Biosample:Opossum kidney (OK) cells (kind gift from the group of Prof O. Devuyst (Physiology, University of Zurich))

## Specimen:DFX treatment: 200uMDyes:-Mitochondria-GFP BacMam 2.0-TMRM (mitochondrial membrane potential dependent dye)

## Image Acquisition:Images were acquired using a Leica SP8 inverse STED 3x.Acquired channels:Channel 1: Mitochondria-GFP, 488nm excitation, 493nm-548nm emissionChannel 2: TMRM, 553nm excitation, 564nm-650nm emissionObjective: HC PL APO CS2 100x/1.40 OIL

## Image Data:Raw timelapse data:    Raw data dataset 5 > 151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression.lifSegmentation masks of mitochondria:    Segmetation mask.tifdocumentation of the analysis steps:    Analysis process.txtMeasurements of segmented mitochondria at different timepoints:    Measurements.csvPlot of mean mitochondria circularity over time:    Mitochondria circularity plot.pngPlot of mean GFP signal in mitochondria over time:    Mitochondria mean signal intensity plot.pngPlot of segmented mitochondria count over time:    Mitochondria count plot.png

## Image CorrelationSegmentation mask.tif contains the segmentation masks for the timelapse in 151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression.lifMeasurements.csv contains the measurements gotten by applying the steps described in Analysis process.txt the raw data file 151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression.lifall plots were generated from the Measurements.csv data

## Image AnalysisSee Analysis process.txt
DOI: 10.5281/zenodo.14979764
title: BIO407 Group3 Image processing of live cells, treated with DFX

description: ## Study:This data is part of the practical course BIO407 2025 at the University of Zurich, titled 'Advanced Microscopy: From preparation to data and visualization'.The effects of Deferasirox (DFX) on mitochondria morphology were examined using different microscopy techniques.The data is originally from the paper:Gottwald EM, Schuh CD, Dr點ker P, Haenni D, Pearson A, Ghazi S, Bugarski M, Polesel M, Duss M, Landau EM, Kaech A, Ziegler U, Lundby AKM, Lundby C, Dittrich PS, Hall AM. The iron chelator Deferasirox causes severe mitochondrial swelling without depolarization due to a specific effect on inner membrane permeability. Sci Rep. 2020 Jan 31;10(1):1577. doi: 10.1038/s41598-020-58386-9. PMID: 32005861; PMCID: PMC6994599.

## Study Component:Job: Image ProcessingTask: 4) Timelapse of mitochondria in deferasirox (DFX) treated cellsTimelapse data was analyzed: Mitochondria were segmented, their morphology and signal intensity were measured over time, and plotted.

## Biosample:Opossum kidney (OK) cells (kind gift from the group of Prof O. Devuyst (Physiology, University of Zurich))

## Specimen:DFX treatment: 200uMDyes:-Mitochondria-GFP BacMam 2.0-TMRM (mitochondrial membrane potential dependent dye)

## Image Acquisition:Images were acquired using a Leica SP8 inverse STED 3x.Acquired channels:Channel 1: Mitochondria-GFP, 488nm excitation, 493nm-548nm emissionChannel 2: TMRM, 553nm excitation, 564nm-650nm emissionObjective: HC PL APO CS2 100x/1.40 OIL

## Image Data:Raw timelapse data:    Raw data dataset 5 > 151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression.lifSegmentation masks of mitochondria:    Segmetation mask.tifdocumentation of the analysis steps:    Analysis process.txtMeasurements of segmented mitochondria at different timepoints:    Measurements.csvPlot of mean mitochondria circularity over time:    Mitochondria circularity plot.pngPlot of mean GFP signal in mitochondria over time:    Mitochondria mean signal intensity plot.pngPlot of segmented mitochondria count over time:    Mitochondria count plot.png

## Image CorrelationSegmentation mask.tif contains the segmentation masks for the timelapse in 151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression.lifMeasurements.csv contains the measurements gotten by applying the steps described in Analysis process.txt the raw data file 151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression.lifall plots were generated from the Measurements.csv data

## Image AnalysisSee Analysis process.txt
DOI: 10.15468/dl.8nrzu2
title: Occurrence Download

description: A dataset containing 911 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Australia",
 "GadmGid is AUS.9_1",
 "TaxonKey is Oreixenica ptunarra Couchman, 1953"
 ]
}

 The dataset includes 911 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004118-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xvwaya
title: Occurrence Download

description: A dataset containing 3538808 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-65.3132 -23.47919,-67.40175 -30.85875,-60.71838 -32.73845,-57.86402 -25.21965,-65.3132 -23.47919))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 3538808 records from 725 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004032-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j286ac
title: Occurrence Download

description: A dataset containing 1916 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((-37.52191 30.05064,-20.30281 30.05064,-20.30281 46.27634,-37.52191 46.27634,-37.52191 30.05064))",
 "TaxonKey is Viburnum treleasei Gand.",
 "Year 1975-2025"
 ]
}

 The dataset includes 1916 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004121-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t5wdd8
title: Occurrence Download

description: A dataset containing 749 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "TaxonKey is Lactuca watsoniana Trel.",
 "Year 1975-2025"
 ]
}

 The dataset includes 749 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004122-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15089578
title: Combining observational data and numerical simulations for seasonal cycle of snow and ice mass balance at MOSAiC: scripts, input datasets and model output

description: This is a combined repository of:

1) Python scripts to generate model input from observational data (tools), 

2) model input from observational data (input)

3) model output (results)

4) Python scripts for output visualization (tools)

These scripts and data and simulations were used in:

Itkin, P. and Liston, G. E.: Combining observational data and numerical models to obtain a seamless high temporal resolution seasonal cycle of snow and ice mass balance at the MOSAiC Central Observatory, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3402, 2024. 

The source observational data is published in several PANGEA repositories as documented in the paper. All the data was collected at the MOSAiC expedition (https://mosaic-expedition.org/) between October 2019 and July 2020.

The numerical model code is documented in:

Mower, R., Gutmann, E. D., Liston, G. E., Lundquist, J., and Rasmussen, S.: Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel), Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, 2024.

and the numerical model code is published in: rmower90. (2024). NCAR/Parallel-SnowModel: Parallel-SnowModel (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.11168392
DOI: 10.5281/zenodo.15089579
title: Combining observational data and numerical simulations for seasonal cycle of snow and ice mass balance at MOSAiC: scripts, input datasets and model output

description: This is a combined repository of:

1) Python scripts to generate model input from observational data (tools), 

2) model input from observational data (input)

3) model output (results)

4) Python scripts for output visualization (tools)

These scripts and data and simulations were used in:

Itkin, P. and Liston, G. E.: Combining observational data and numerical models to obtain a seamless high temporal resolution seasonal cycle of snow and ice mass balance at the MOSAiC Central Observatory, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3402, 2024. 

The source observational data is published in several PANGEA repositories as documented in the paper. All the data was collected at the MOSAiC expedition (https://mosaic-expedition.org/) between October 2019 and July 2020.

The numerical model code is documented in:

Mower, R., Gutmann, E. D., Liston, G. E., Lundquist, J., and Rasmussen, S.: Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel), Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, 2024.

and the numerical model code is published in: rmower90. (2024). NCAR/Parallel-SnowModel: Parallel-SnowModel (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.11168392
DOI: 10.15468/dl.nx9c8v
title: Occurrence Download

description: A dataset containing 14830 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (Parasitic Platyhelminthes Collections)"
 ]
}

 The dataset includes 14830 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004124-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y37m6b
title: Occurrence Download

description: A dataset containing 169352181 records in GBIF matching the query:
SELECT catalognumber, scientificname, publishingorgkey, institutioncode, datasetkey, gbifid, decimallatitude, decimallongitude, 
 recordedby, identifiedby, eventdate, kingdomkey, phylumkey, classkey, orderkey, familykey, genuskey, subgenuskey, specieskey
 FROM occurrence WHERE
 classkey = 212 AND orderkey = 7192402 
 AND taxonomicstatus = 'ACCEPTED' //newly added 12Feb2025
 AND hascoordinate = 1
 AND hasgeospatialissues = 0
 AND specieskey IS NOT NULL
 AND occurrencestatus = 'PRESENT'
 -- AND (
 -- basisofrecord = 'HUMAN_OBSERVATION'
 -- OR basisofrecord = 'MACHINE_OBSERVATION'
 -- OR basisofrecord = 'OCCURRENCE'
 -- OR basisofrecord = 'LIVING_SPECIMEN'
 -- OR basisofrecord = 'MATERIAL_SAMPLE'
 -- )
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE') AND datasetkey NOT IN ('040c5662-da76-4782-a48e-cdea1892d14c') 

 The dataset's 169352181 records were derived from 7330 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004093-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hmzjd8
title: Occurrence Download

description: A dataset containing 10268 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cordyline australis)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 10268 records from 94 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005906-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13145/bacdive147587.20251217.10
title: Campylobacter jejuni

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147588.20250331.9.3
title: Helicobacter pullorum

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0135602.10fu
title: SH0135602.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.13145/bacdive147588.20251217.10
title: Helicobacter pullorum

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147589.20241212.9.2
title: Pasteurella canis

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0135603.10fu
title: SH0135603.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0135604.10fu
title: SH0135604.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.13145/bacdive147589.20251217.10
title: Pasteurella canis

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147590.20241212.9.2
title: Moraxella nonliquefaciens

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147590.20251217.10
title: Moraxella nonliquefaciens

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147591.20241212.9.2
title: Bordetella holmesii

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0135605.10fu
title: SH0135605.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.13145/bacdive147591.20251217.10
title: Bordetella holmesii

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147592.20241212.9.2
title: Streptococcus salivarius

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0135606.10fu
title: SH0135606.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.13145/bacdive147592.20251217.10
title: Streptococcus salivarius

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0135607.10fu
title: SH0135607.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.13145/bacdive147593.20241212.9.2
title: Haemophilus parainfluenzae

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147593.20251217.10
title: Haemophilus parainfluenzae

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0135608.10fu
title: SH0135608.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.13145/bacdive147594.20241212.9.2
title: Haemophilus ducreyi

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147594.20251217.10
title: Haemophilus ducreyi

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.