cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17605/osf.io/kxv5h
title: Enhanced error-related negativity (ERN) as a transdiagnostic risk marker of the anxiety and obsessive-compulsive spectrum.

description: Whenever humans commit an error, they are faced with potential harm on the one hand but on the other hand, they can also learn from an error and thus improve their upcoming behavior. Neuroscientific research often investigates neural correlates of error monitoring in humans via the event-related potential (ERP) called error-related negativity (ERN; Falkenstein et al., 1991; Gehring et al., 1993) peaking at approx. 50ms after erroneous responses. Another performance associated ERP is the correct-response negativity (CRN; Vidal et al., 2003; Vidal et al., 2000) occurring shortly after correct responses. 

Previous studies conceive error monitoring a transdiagnostic risk marker along the lines of internalizing and externalizing mental disorders (Pasion & Barbosa, 2019): ERN amplitudes are enhanced for anxiety (Moser et al., 2013; Saunders & Inzlicht, 2020) and obsessive-compulsive disorders (Riesel, 2019), while they are attenuated for substance use disorder (Luijten et al., 2014) and attention deficit hyperactivity disorder (Geburek et al., 2013; but see Kaiser et al., 2020). However, studies with subclinical levels of anxiety report less consistently enhanced ERN amplitudes (Härpfer, Carsten, Spychalski, et al., 2020; Saunders & Inzlicht, 2020; Seow et al., 2020). Within the anxiety and obsessive-compulsive spectrum, ERN amplitudes are elevated in generalized anxiety disorder (GAD; Meyer et al., 2012; Weinberg et al., 2010), social anxiety disorder (SAD; Endrass et al., 2014), health anxiety (Riesel et al., 2017), and obsessive-compulsive disorder (OCD; Riesel, 2019) but not in post-traumatic stress disorder (PTSD; Gorka et al., 2016; Rabinak et al., 2013) and specific phobia (Hajcak et al., 2003). This inconsistency might stem from differences in latent symptom dimension profiles of anxiety and obsessive-compulsive disorders (Cox et al., 2010; Krueger, 1999). Specifically, overactive error monitoring seems to be driven by anxious apprehension—as opposed to anxious arousal—which is supported by previous studies (Hajcak et al., 2003; Lin et al., 2015; Moran et al., 2012; Moser et al., 2012; but see Härpfer, Carsten, Spychalski, et al., 2020) as well as meta-analyses (Moser et al., 2013; Saunders & Inzlicht, 2020). These transdiagnostic findings might translate into diagnostic categories, whereby disorders characterized by anxious apprehension were more closely linked to ERN variations (e.g., GAD, SAD, and OCD compared to PTSD and specific phobia). 

Consequently, a dimensional and transdiagnostic conceptualization of the anxiety and obsessive-compulsive spectrum might better elucidate the anxiety dimensions (i.e., anxious apprehension and anxious arousal) that are associated with overactive error monitoring while broad disorder categories are likely to overshadow specific associations.
DOI: 10.25921/6at2-r476
title: Dissolved inorganic carbon, total alkalinity, pH on Total Scale, nutrients and other variables collected from profile and discrete sample observations using CTD, Niskin bottle and other instruments during NOAA Ship Gordon Gunter cruise GU1804 (EXPOCODE 33GG20180822) in the North Atlantic Ocean, Gulf of Maine, Georges Bank, and Mid-Atlantic Bight from 2018-08-22 to 2018-08-31 (NCEI Accession 0188876)

description: This dataset contains dissolved inorganic carbon, total alkalinity, pH on total scale, nutrients and other variables measured from profile discrete observations during NOAA Ship Gordon Gunter cruise GU1804 (EXPOCODE 33GG20180822) in the North Atlantic Ocean, Gulf of Maine, Georges Bank, and Mid-Atlantic Bight from 2018-08-22 to 2018-08-31. Increasing amounts of atmospheric carbon dioxide from human industrial activities are causing changes in global ocean carbon chemistry ultimately resulting in a reduction in pH, a process termed ocean acidification. Studies have demonstrated adverse effects on calcifying organisms, particularly some invertebrates, corals, sea urchins, pteropods, and coccolithophores, but also on species of commercial interest such as oysters or crabs. In support of the coastal monitoring and research objectives of the NOAA Ocean Acidification Program (OAP), the Ecosystem Monitoring Cruises (ECOMON) are utilized to collect water samples to measure water column inorganic carbon and hydrographic parameters including nutrients. Samples are collected at three depths (surface, mid-depth and near bottom) at select stations on a quarterly basis in the northeastern Atlantic continental shelf region off the United States. Water samples are sent to and analyzed by scientists at the Atlantic Oceanographic and Meteorological Laboratory (AOML) for dissolved inorganic carbon, pH and total alkalinity concentrations. Additional samples are analyzed for nutrient concentrations at the University of Maine. These data are used to monitor short-long term coastal ocean acidification trends.
DOI: 10.25921/dz61-vt20
title: Dissolved inorganic carbon, total alkalinity, pH on Total Scale, nutrients and other variables collected from profile and discrete sample observations using CTD, Niskin bottle and other instruments during the R/V Hugh R. Sharp cruise S11802 (EXPOCODE 33H520181102) in the North Atlantic Ocean, Gulf of Maine, Georges Bank, and Mid-Atlantic Bight from 2018-11-02 to 2018-11-12 (NCEI Accession 0189039)

description: This dataset contains dissolved inorganic carbon, total alkalinity, pH on total scale, nutrients and other variables measured from profile discrete observations during the R/V Hugh R. Sharp cruise S11802 (EXPOCODE 33H520181102) in the North Atlantic Ocean, Gulf of Maine, Georges Bank, and Mid-Atlantic Bight from 2018-11-02 to 2018-11-12. Increasing amounts of atmospheric carbon dioxide from human industrial activities are causing changes in global ocean carbon chemistry ultimately resulting in a reduction in pH, a process termed ocean acidification. Studies have demonstrated adverse effects on calcifying organisms, particularly some invertebrates, corals, sea urchins, pteropods, and coccolithophores, but also on species of commercial interest such as oysters or crabs. In support of the coastal monitoring and research objectives of the NOAA Ocean Acidification Program (OAP), the Ecosystem Monitoring Cruises (ECOMON) are utilized to collect water samples to measure water column inorganic carbon and hydrographic parameters including nutrients. Samples are collected at three depths (surface, mid-depth and near bottom) at select stations on a quarterly basis in the northeastern Atlantic continental shelf region of the United States. Water samples are sent to and analyzed by scientists at the Atlantic Oceanographic and Meteorological Laboratory (AOML) for dissolved inorganic carbon, pH and total alkalinity concentrations. Additional samples are analyzed for nutrient concentrations at the University of Maine. These data are used to monitor short-long term coastal ocean acidification trends.
DOI: 10.5281/zenodo.17933884
title: An Integrated Analysis of Single-Cell and Spatial Transcriptomics Identifies Key Signaling Pathways Governing Merkel Cell Generation

description: In this study, we utilized an integrated analytical approach combining single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics to investigate the developmental process of Merkel cells (MCs). scRNA-seq provides resolution at the individual cell level, enabling the identification of cellular heterogeneity and distinct cell states within a population. Spatial transcriptomics, on the other hand, preserves the original spatial positioning of cells within the tissue, allowing for the analysis of gene expression patterns within their authentic tissue context. By integrating these two complementary technologies, we successfully identified key signaling pathways associated with Merkel cell formation. Specifically, our analysis revealed that IGF1R is a potential regulator of MC generation, likely mediating its effects through the AKT signaling pathway. Furthermore, the study validated the important functional role of FGFR2 signaling in MC generation. This integrated analytical platform, combining single-cell and spatial transcriptomic data, provides a powerful technical foundation for gaining deeper insights into the biology of Merkel cells, the mechanisms of skin development, and for advancing stem cell research and disease modeling.
DOI: 10.5281/zenodo.17933885
title: An Integrated Analysis of Single-Cell and Spatial Transcriptomics Identifies Key Signaling Pathways Governing Merkel Cell Generation

description: In this study, we utilized an integrated analytical approach combining single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics to investigate the developmental process of Merkel cells (MCs). scRNA-seq provides resolution at the individual cell level, enabling the identification of cellular heterogeneity and distinct cell states within a population. Spatial transcriptomics, on the other hand, preserves the original spatial positioning of cells within the tissue, allowing for the analysis of gene expression patterns within their authentic tissue context. By integrating these two complementary technologies, we successfully identified key signaling pathways associated with Merkel cell formation. Specifically, our analysis revealed that IGF1R is a potential regulator of MC generation, likely mediating its effects through the AKT signaling pathway. Furthermore, the study validated the important functional role of FGFR2 signaling in MC generation. This integrated analytical platform, combining single-cell and spatial transcriptomic data, provides a powerful technical foundation for gaining deeper insights into the biology of Merkel cells, the mechanisms of skin development, and for advancing stem cell research and disease modeling.
DOI: 10.25921/q1qg-5h83
title: Dissolved inorganic carbon, total alkalinity, nutrients, and other variables collected from profile and discrete observations during NOAA Ship Henry B. Bigelow cruise HB1902 (EXPOCODE 33HH20190522) in Gulf of Maine, Georges Bank, and Mid-Atlantic Bight from 2019-05-22 to 2019-06-06 (NCEI Accession 0209045)

description: This dataset contains profile discrete measurements of dissolved inorganic carbon, total alkalinity, pH on total scale, temperature, salinity, nutrients, and oxygen collected during NOAA Ship Henry B. Bigelow cruise HB1902 (EXPOCODE 33HH20190522) in Gulf of Maine, Georges Bank, and Mid-Atlantic Bight from 2019-05-22 to 2019-06-06. Increasing amounts of atmospheric carbon dioxide from human industrial activities are causing changes in global ocean carbon chemistry ultimately resulting in a reduction in pH, a process termed ocean acidification. Studies have demonstrated adverse effects on calcifying organisms, particularly some invertebrates, corals, sea urchins, pteropods, and coccolithophores, but also on species of commercial interest such as oysters or crabs. In support of the coastal monitoring and research objectives of the NOAA Ocean Acidification Program (OAP), the Ecosystem Monitoring Cruises (ECOMON) are utilized to collect water samples to measure water column inorganic carbon and hydrographic parameters including nutrients. Samples are collected at three depths (surface, mid-depth and near bottom) at select stations on a quarterly basis in the northeastern Atlantic continental shelf region off the United States. Water samples are sent to and analyzed by scientists at the Atlantic Oceanographic and Meteorological Laboratory (AOML) for dissolved inorganic carbon, pH and total alkalinity concentrations. Additional samples are analyzed for nutrient concentrations at the University of Maine. These data are used to monitor short-long term coastal ocean acidification trends.
DOI: 10.25921/xxgq-e782
title: Dissolved inorganic carbon (DIC), total alkalinity, pH on total scale, nutrients and other variables collected from profile and discrete samples observations during NOAA Ship Gordon Gunter cruise GU1902 (EXPOCODE 33GG20190815) in the North Atlantic Ocean, Gulf of Maine, Georges Bank, and Mid-Atlantic Bight from 2019-08-15 to 2019-08-30 (NCEI Accession 0209156)

description: This dataset contains dissolved inorganic carbon, total alkalinity, pH on total scale, nutrients and other variables measured from profile discrete observations during NOAA Ship Gordon Gunter cruise GU1902 (EXPOCODE 33GG20190815) in the North Atlantic Ocean, Gulf of Maine, Georges Bank, and Mid-Atlantic Bight from 2019-08-15 to 2019-08-30. Increasing amounts of atmospheric carbon dioxide from human industrial activities are causing changes in global ocean carbon chemistry ultimately resulting in a reduction in pH, a process termed ocean acidification. Studies have demonstrated adverse effects on calcifying organisms, particularly some invertebrates, corals, sea urchins, pteropods, and coccolithophores, but also on species of commercial interest such as oysters or crabs. In support of the coastal monitoring and research objectives of the NOAA Ocean Acidification Program (OAP), the Ecosystem Monitoring Cruises (ECOMON) are utilized to collect water samples to measure water column inorganic carbon and hydrographic parameters including nutrients. Samples are collected at three depths (surface, mid-depth and near bottom) at select stations on a quarterly basis in the northeastern Atlantic continental shelf region off the United States. Water samples were sent to and analyzed by scientists at the Atlantic Oceanographic and Meteorological Laboratory (AOML) for dissolved inorganic carbon, pH and total alkalinity concentrations. Additional samples are analyzed for nutrient concentrations at the University of Maine. These data are used to monitor short-long term coastal ocean acidification trends.
DOI: 10.5281/zenodo.17937109
title: Reproduction scripts and processed data for "Targeting Pre-Existing Club-Like Cells in Prostate Cancer Potentiates Androgen Deprivation Therapy"

description: Overview

This dataset contains R scripts and processed single-cell RNA-seq data required to reproduce the computational figures in the manuscript:

"Targeting Pre-Existing Club-Like Cells in Prostate Cancer Potentiates Androgen Deprivation Therapy"

Published in EMBO Molecular Medicine (2025)

Contents

R Scripts (5 files)



Figure1.R - Analysis of LSCmed cells as surrogate of human MSPC

Figure2.R - Transcriptomic heterogeneity and clustering of LSCmed cells

Figure3.R - RNA velocity and pseudotime trajectory analysis

Figure4.R - SCENIC transcription factor regulon analysis (includes EV4)

Figure5.R - Pim1 expression analysis


Processed Data Files (6 files)

Seurat Objects:



GOF_306cells.rds (49 MB) - 306 LSCmed cells with clustering and all signature scores (AR, MSPC, ARPC, NEPC, CRPC subtypes, cell type signatures: Luminal, Basal, Club, Hillock, EMT)

seu3001_scenic.rds (62 MB) - Seurat object with SCENIC TF activity scores from 10 aggregated runs


SCENIC Results:



regulon_geneset.rds (136 KB) - Transcription factor regulon gene sets (union of 10 SCENIC runs)


Differential Expression & Count Data:



GOFcelltype.markers.csv (257 KB) - Pre-computed differential expression markers between castrated and naive conditions

gene_count_3001_4271.txt (66 MB) - Gene count matrix for velocyto cell name mapping

3001.loom (22 MB) - Velocyto loom file for RNA velocity analysis


Data Description

Experimental Design



Model: Ptenpc-/- mouse prostate cancer model

Technology: Smart-Seq2 single-cell RNA sequencing

Cell Type: LSCmed (luminal stem cell-enriched population)

Conditions: Naive (intact) vs Castrated (2 months post-castration)

Total Cells: 306 cells (192 castrated + 114 naive)

Clusters: 3 major LSCmed subpopulations identified


Software Requirements



R (>= 4.0)

Seurat (>= 4.0)

SeuratExtend

For RNA velocity: Python with scvelo (conda environment)

For pseudotime: monocle, M3Drop

For SCENIC: Nextflow + pySCENIC
DOI: 10.5281/zenodo.17937108
title: Reproduction scripts and processed data for "Targeting Pre-Existing Club-Like Cells in Prostate Cancer Potentiates Androgen Deprivation Therapy"

description: Overview

This dataset contains R scripts and processed single-cell RNA-seq data required to reproduce the computational figures in the manuscript:

"Targeting Pre-Existing Club-Like Cells in Prostate Cancer Potentiates Androgen Deprivation Therapy"

Published in EMBO Molecular Medicine (2025)

Contents

R Scripts (5 files)



Figure1.R - Analysis of LSCmed cells as surrogate of human MSPC

Figure2.R - Transcriptomic heterogeneity and clustering of LSCmed cells

Figure3.R - RNA velocity and pseudotime trajectory analysis

Figure4.R - SCENIC transcription factor regulon analysis (includes EV4)

Figure5.R - Pim1 expression analysis


Processed Data Files (6 files)

Seurat Objects:



GOF_306cells.rds (49 MB) - 306 LSCmed cells with clustering and all signature scores (AR, MSPC, ARPC, NEPC, CRPC subtypes, cell type signatures: Luminal, Basal, Club, Hillock, EMT)

seu3001_scenic.rds (62 MB) - Seurat object with SCENIC TF activity scores from 10 aggregated runs


SCENIC Results:



regulon_geneset.rds (136 KB) - Transcription factor regulon gene sets (union of 10 SCENIC runs)


Differential Expression & Count Data:



GOFcelltype.markers.csv (257 KB) - Pre-computed differential expression markers between castrated and naive conditions

gene_count_3001_4271.txt (66 MB) - Gene count matrix for velocyto cell name mapping

3001.loom (22 MB) - Velocyto loom file for RNA velocity analysis


Data Description

Experimental Design



Model: Ptenpc-/- mouse prostate cancer model

Technology: Smart-Seq2 single-cell RNA sequencing

Cell Type: LSCmed (luminal stem cell-enriched population)

Conditions: Naive (intact) vs Castrated (2 months post-castration)

Total Cells: 306 cells (192 castrated + 114 naive)

Clusters: 3 major LSCmed subpopulations identified


Software Requirements



R (>= 4.0)

Seurat (>= 4.0)

SeuratExtend

For RNA velocity: Python with scvelo (conda environment)

For pseudotime: monocle, M3Drop

For SCENIC: Nextflow + pySCENIC
DOI: 10.15468/dl.huzexz
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Odonata)",
 "Geometry MULTIPOLYGON (((-4.685723 58.03027, -4.683079 58.03076, -4.682284 58.03125, -4.68 58.03201, -4.679173 58.03208, -4.679541 58.03312, -4.679916 58.03426, -4.681428 58.03431, -4.682377 58.03458, -4.681505 58.03551, -4.682071 58.03565, -4.683127 58.03559, -4.683712 58.03566, -4.683445 58.03628, -4.683049 58.03824, -4.682714 58.03828, -4.681132 58.03778, -4.680079 58.03774, -4.67956 58.03919, -4.679684 58.03971, -4.680009 58.04017, -4.680427 58.04045, -4.680938 58.04018, -4.681675 58.03939, -4.682423 58.03891, -4.683072 58.03903, -4.684031 58.03928, -4.685212 58.03941, -4.686175 58.0397, -4.685866 58.04026, -4.685625 58.04042, -4.684734 58.04043, -4.682887 58.04027, -4.682819 58.04049, -4.683865 58.04075, -4.684398 58.04095, -4.685822 58.04079, -4.687397 58.04055, -4.689541 58.04034, -4.690853 58.04143, -4.69113 58.04158, -4.691381 58.04187, -4.691834 58.04227, -4.692456 58.0422, -4.692888 58.04169, -4.693725 58.04127, -4.694554 58.04138, -4.695598 58.04146, -4.695818 58.04133, -4.695546 58.04125, -4.693403 58.03971, -4.691746 58.03692, -4.688347 58.03536, -4.688078 58.03306, -4.687878 58.03168, -4.68661 58.03023, -4.686432 58.02948, -4.685723 58.03027)))",
 "Year is 2014"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053422-251120083545085/datasets/export for details.
DOI: 10.5281/zenodo.15782681
title: Borehole deformation data 2016-2023 from Murtèl rock glacier (Engadine, Switzerland)

description: Description

The dataset presented here is partially accompanying the publication "Multi-annual and seasonal patterns of Murtèl rock glacier borehole deformation, environmental controls and implications for kinematic monitoring". The main focus is the borehole drilled in 2015 in the lower center of the Murtèl rock glacier. For more details about its installation and the instrumentation refer to the paper publication. 

Inclinometer instrument specifications:



46.428920533˚N, 9.821705857˚E, 2718 m a.s.l.

40 m long

50 cm long rigid segments define the vertical spatial resolution (one measurement every 50 cm)

Triaxial MEMS gravity sensors every segment to measure the tilt angle relative to the vertical

Temporal resolution: 6 hours


The raw angle data is converted into useful position data via the Measurand SAASuite v3.07 software. The software provided by the manufacturer of the ShapeAccelArray (SAA) field inclinometer outputs files in a MATLAB format. So, the recommended software is MATLAB; however, a script to extract the variables and process the data in Python is provided with the import_SAA_data.py script. The MATLAB data files published here are split into three periods based on when the data was collected: 2016-17, 2017-18 and 2018-23. For those not interested in going through the data pre-processing with the import_SAA_data.py script, a CSV file with the already processed data is also provided as SAA_16_23_defo.csv, which is the output from running the processing script as instructred below.

SAA_16_23_defo.csv metadata:



Start: 2016-01-06 12:05:00

End: 2023-09-14 06:05:00

Depth fields: header is made up of a sequence of integers from 0 to 80, where 80 = 0 m depth and 0 = 40 m depth, so each column is seperated by a 0.5 m increment (see sensor_no_to_depths.csv file for complete depth index to depth conversion)

'time' column is in YYYY-MM-DD HH:MM:SS format


Instructions for creating the final CSV file:

These are the instructions to run the pre-processing import_SAA_data.py script. Download all files and place them in the same folder. To run the script complete the file paths based on where you saved the files to import the data. The script then merges the data into the complete time period from April 2016 until August 2023 and calculates cumulative deformation for all depths relative to a reference date which is adjustable. The function to calculate deformation has a smoothSwitch to toggle a 5-day moving window averageing and a dailySwitch to toggle the resampling from a 6-hour temporal resolution to daily. The resulting deformation data is exported into a CSV file.