cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.xhfvce
title: Occurrence Download

description: A dataset containing 904 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Biomonitoreo de coleópteros para la Ecoreserva ASA La Guarupaya (Acacías-Meta) - Convenio Ecoreservas"
 ]
}

 The dataset includes 904 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020395-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a9ahdj
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "ScientificName is Rupicapra pyrenaica",
 "Country is Spain",
 "Year is greater than or equal to 2010",
 "Year is less than or equal to 2022",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020398-251120083545085/datasets/export for details.
DOI: 10.15468/dl.p3mrm3
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingOrg is one of (a76f8527-23e8-4ba5-bdff-f9821bdac187)",
 "DatasetKey is one of (The Fungal Collection at the Botanische Staatssammlung München)"
 ]
 },
 "HasGeospatialIssue is false",
 "Geometry POLYGON((6.1496 50.19097,6.08368 50.20855,5.95184 50.20503,5.8255 50.12762,5.71564 49.97596,5.68268 49.86632,5.75409 49.75643,5.84747 49.64273,5.77057 49.55729,5.83099 49.49311,5.94635 49.43956,6.05621 49.42527,6.21002 49.4824,6.3913 49.43241,6.41327 49.51808,6.45721 49.60003,6.5506 49.6854,6.56158 49.81318,6.41327 49.86986,6.29242 49.91586,6.2265 49.98302,6.18256 50.05714,6.20453 50.14171,6.1496 50.19097))"
 ]
}

 The dataset includes 35 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020401-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17778171
title: Datasets for InSAR-Derived Velocity, Decomposed West-East Velocity, and Strain Rate Fields
DOI: 10.5281/zenodo.17778172
title: Datasets for InSAR-Derived Velocity, Decomposed West-East Velocity, and Strain Rate Fields
DOI: 10.48804/h11fck
title: Data for: Understanding climate-friendly gardening behaviour and its climate impact, Belgium

description: This dataset presents anonymized self-reported survey data collected via a cross-sectional online survey, and geospatial data calculated based on publicly available objective geospatial data. The aim was to understand the behavioural potential for climate-friendly gardening by homeowners in domestic gardens.
DOI: 10.15468/dl.strhus
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mammalia",
 "HasCoordinate is true",
 "Geometry POLYGON ((-117.9799 35.02773, -117.9804 35.02775, -117.9809 35.02774, -117.9813 35.02772, -117.9818 35.02767, -117.9822 35.02761, -117.9827 35.02753, -117.9832 35.02742, -117.9836 35.0273, -117.984 35.02716, -117.9845 35.027, -117.9849 35.02682, -117.9853 35.02663, -117.9857 35.02641, -117.9861 35.02619, -117.9864 35.02594, -117.9868 35.02568, -117.9871 35.02541, -117.9874 35.02512, -117.9877 35.02482, -117.988 35.02451, -117.9882 35.02418, -117.9885 35.02385, -117.9887 35.0235, -117.9888 35.02315, -117.989 35.02279, -117.9892 35.02243, -117.9893 35.02206, -117.9894 35.02168, -117.9894 35.0213, -117.9895 35.02092, -117.9896 35.02009, -117.9896 35.0197, -117.9896 35.01932, -117.9895 35.01894, -117.9895 35.01856, -117.9894 35.01818, -117.9893 35.0178, -117.9892 35.01743, -117.989 35.01707, -117.9889 35.01671, -117.9887 35.01636, -117.9885 35.01602, -117.9882 35.01569, -117.988 35.01537, -117.9877 35.01506, -117.9874 35.01476, -117.9871 35.01447, -117.9868 35.0142, -117.9864 35.01395, -117.986 35.01371, -117.9857 35.01348, -117.9853 35.01327, -117.9849 35.01308, -117.9844 35.01291, -117.984 35.01276, -117.9836 35.01262, -117.9831 35.0125, -117.9827 35.0124, -117.9822 35.01232, -117.9817 35.01226, -117.9813 35.01223, -117.9804 35.01217, -117.9799 35.01215, -117.9795 35.01215, -117.979 35.01217, -117.9785 35.01221, -117.9781 35.01227, -117.9776 35.01235, -117.9772 35.01245, -117.9767 35.01256, -117.9763 35.0127, -117.9759 35.01285, -117.9754 35.01302, -117.975 35.01321, -117.9746 35.01342, -117.9743 35.01364, -117.9739 35.01388, -117.9736 35.01413, -117.9732 35.0144, -117.9729 35.01468, -117.9726 35.01497, -117.9723 35.01528, -117.9721 35.01559, -117.9718 35.01592, -117.9716 35.01626, -117.9714 35.0166, -117.9713 35.01696, -117.9711 35.01732, -117.971 35.01769, -117.9709 35.01806, -117.9708 35.01843, -117.9708 35.01881, -117.9707 35.0196, -117.9706 35.01998, -117.9706 35.02036, -117.9706 35.02074, -117.9707 35.02112, -117.9707 35.02149, -117.9708 35.02187, -117.9709 35.02223, -117.9711 35.0226, -117.9712 35.02295, -117.9714 35.0233, -117.9716 35.02364, -117.9718 35.02398, -117.9721 35.0243, -117.9723 35.02461, -117.9726 35.02491, -117.9729 35.0252, -117.9732 35.02548, -117.9736 35.02574, -117.9739 35.02599, -117.9743 35.02622, -117.9747 35.02644, -117.975 35.02664, -117.9754 35.02682, -117.9759 35.02699, -117.9763 35.02714, -117.9767 35.02727, -117.9772 35.02738, -117.9776 35.02747, -117.9781 35.02755, -117.9785 35.0276, -117.9794 35.02769, -117.9799 35.02773))",
 "Year is greater than or equal to 2001",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020403-251120083545085/datasets/export for details.
DOI: 10.82201/ggv2i3
title: Additional Materials

description: In this document you can find a pdf with additional information to all the sessions in the training course Research Data Management for Advanced Users in 2025.
DOI: 10.15468/dl.56d2dj
title: Occurrence Download

description: A dataset containing 4179 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vipera aspis",
 "HasCoordinate is true",
 "MediaType is Image"
 ]
}

 The dataset includes 4179 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020402-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30273528.v1
title: SLUR(M)-py: a SLURM powered Pythonic pipeline for parallel processing of 3D (Epi)genomic profiles

description: Epigenomics has become multi-faceted, with researchers exploring chromatin structure, nucleosome states, and epigenetic modifications, producing large, complex multi-omic datasets. Given this shift, there is a demand for bioinformatics that leverage high-performance computing (HPC) and parallelization to quickly process data. As such, we developed SLUR(M)-py, a pythonic computational platform that leverages the Simple Linux Utility for Resource Management system (SLURM) to process sequencing data. SLUR(M)-py is multi-omic and automates calls to SLURM for processing paired-end sequences from chromatin characterization experiments, including whole-genome, ChIP-seq, ATAC-seq, and Hi-C, thereby eliminating the need for multiple analytics pipelines. To demonstrate SLUR(M)-py’s utility, we employ ATAC-seq and Hi-C data from viral infection experiments and the ENCODE project, and illustrate its processing speed and completeness, which outpaces current HPC pipelines. We explore the effect of dropping duplicate sequenced reads in ATAC-seq, demonstrate how SLUR(M)-py can be used for quality control, and how to detect artifacts in Hi-C from viral infection experiments. Finally, we show how features in SLUR(M)-py, like inter-chromosomal analysis, can be used to explore the dynamics of chromosomal contacts in mammalian cells. This multi-omic, system-agnostic platform eases the computational burden for researchers and quickly produces accurate and reliable data analytics for the epigenomics community. SLUR(M)-py (pronounced slurpy) is a computer program that makes it easier and faster to analyze DNA data on powerful supercomputers. Researchers often use DNA sequencing to study biological phenomena, like how genes are turned on or off, how proteins interact with DNA, or how viruses affect their hosts. Normally, this analysis requires juggling different tools, but SLUR(M)-py puts everything into one simple workflow. <b>What it does</b>: SLUR(M)-py takes huge DNA data files from sequencing experiments, breaks them into smaller pieces, compares them to a reference genome, filters the results, and puts everything back together. It can handle experiments with multiple samples, pick up where it left off if something goes wrong, and automatically create quality check reports. <b>Why it is useful</b>: SLUR(M)-py works for DNA data from any species, not just humans. It gives researchers one consistent, reliable way to process their data, saving time, and reducing errors. Because it is written in Python (a common coding language), it is easy to customize. <b>Bottom line</b>: SLUR(M)-py is a supercomputer-friendly tool that helps scientists quickly and reliably turn raw DNA sequencing data into usable, high-quality results.
DOI: 10.6084/m9.figshare.30273528
title: SLUR(M)-py: a SLURM powered Pythonic pipeline for parallel processing of 3D (Epi)genomic profiles

description: Epigenomics has become multi-faceted, with researchers exploring chromatin structure, nucleosome states, and epigenetic modifications, producing large, complex multi-omic datasets. Given this shift, there is a demand for bioinformatics that leverage high-performance computing (HPC) and parallelization to quickly process data. As such, we developed SLUR(M)-py, a pythonic computational platform that leverages the Simple Linux Utility for Resource Management system (SLURM) to process sequencing data. SLUR(M)-py is multi-omic and automates calls to SLURM for processing paired-end sequences from chromatin characterization experiments, including whole-genome, ChIP-seq, ATAC-seq, and Hi-C, thereby eliminating the need for multiple analytics pipelines. To demonstrate SLUR(M)-py’s utility, we employ ATAC-seq and Hi-C data from viral infection experiments and the ENCODE project, and illustrate its processing speed and completeness, which outpaces current HPC pipelines. We explore the effect of dropping duplicate sequenced reads in ATAC-seq, demonstrate how SLUR(M)-py can be used for quality control, and how to detect artifacts in Hi-C from viral infection experiments. Finally, we show how features in SLUR(M)-py, like inter-chromosomal analysis, can be used to explore the dynamics of chromosomal contacts in mammalian cells. This multi-omic, system-agnostic platform eases the computational burden for researchers and quickly produces accurate and reliable data analytics for the epigenomics community. SLUR(M)-py (pronounced slurpy) is a computer program that makes it easier and faster to analyze DNA data on powerful supercomputers. Researchers often use DNA sequencing to study biological phenomena, like how genes are turned on or off, how proteins interact with DNA, or how viruses affect their hosts. Normally, this analysis requires juggling different tools, but SLUR(M)-py puts everything into one simple workflow. <b>What it does</b>: SLUR(M)-py takes huge DNA data files from sequencing experiments, breaks them into smaller pieces, compares them to a reference genome, filters the results, and puts everything back together. It can handle experiments with multiple samples, pick up where it left off if something goes wrong, and automatically create quality check reports. <b>Why it is useful</b>: SLUR(M)-py works for DNA data from any species, not just humans. It gives researchers one consistent, reliable way to process their data, saving time, and reducing errors. Because it is written in Python (a common coding language), it is easy to customize. <b>Bottom line</b>: SLUR(M)-py is a supercomputer-friendly tool that helps scientists quickly and reliably turn raw DNA sequencing data into usable, high-quality results.
DOI: 10.6084/m9.figshare.30306168.v1
title: DNA methylation-predicted protein differences between Yakutian and Central Russian populations

description: Populations in subarctic regions, like Yakutia in the Russian Sakha Republic, have adapted to extreme environmental conditions, including intense cold, pronounced shifts in daylight, and variable food availability. However, the biological mechanisms underlying these adaptations remain poorly understood despite insights from genome-wide (GWAS) and epigenome-wide association studies (EWAS). Since protein profiles may more directly reflect functional physiology, we analyzed DNA methylation data from 245 healthy Russian participants using methylation-based estimators of circulating protein levels to investigate estimated proteomic differences between residents of Yakutia and Central Russia. We identified regional variation in 25 protein surrogates enriched in pathways, including MET receptor activation and PI3K-Akt signaling. Some proteins mapped to previously identified GWAS genes. To our knowledge, none mapped to previously identified, differentially methylated in EWAS genes, suggesting that methylation-based protein estimation may capture distinct, complementary aspects of physiological regulation. These findings align with prior -omics research by highlighting regional molecular differences possibly associated with cold adaptation. They also underscore the potential of methylation-derived proteomic proxies as a useful, indirect approach for studying proteomic variation when direct protein measurements are unavailable. While promising, this method warrants further validation, particularly with respect to potential genetic confounding. People living in subarctic regions such as Yakutia in the Russian Far East have adapted to some of the harshest environments in the world. These include extreme cold, long winter nights, and limited access to food. Scientists believe these adaptations are reflected in the body’s biology, but studies examining genes and other molecular traits have not fully explained how these changes occur. In this study, we used a new method that estimates protein levels in the body based on patterns of DNA methylation. DNA methylation is a type of chemical change on DNA that can show how the environment influences the body. We applied this method to blood samples from 245 healthy adults living in two regions of Russia, Yakutia and Central Russia. We found differences in 25 estimated protein levels between the two groups. These proteins are involved in important processes like managing energy use. To our knowledge, these proteins were not linked to genes found in previous epigenetic studies. This suggests the method may provide new insights. Our findings show that estimating protein levels from DNA can help uncover unique biological features in populations adapted to extreme environments. This approach could help guide future research on human adaptation and health.
DOI: 10.6084/m9.figshare.30306168
title: DNA methylation-predicted protein differences between Yakutian and Central Russian populations

description: Populations in subarctic regions, like Yakutia in the Russian Sakha Republic, have adapted to extreme environmental conditions, including intense cold, pronounced shifts in daylight, and variable food availability. However, the biological mechanisms underlying these adaptations remain poorly understood despite insights from genome-wide (GWAS) and epigenome-wide association studies (EWAS). Since protein profiles may more directly reflect functional physiology, we analyzed DNA methylation data from 245 healthy Russian participants using methylation-based estimators of circulating protein levels to investigate estimated proteomic differences between residents of Yakutia and Central Russia. We identified regional variation in 25 protein surrogates enriched in pathways, including MET receptor activation and PI3K-Akt signaling. Some proteins mapped to previously identified GWAS genes. To our knowledge, none mapped to previously identified, differentially methylated in EWAS genes, suggesting that methylation-based protein estimation may capture distinct, complementary aspects of physiological regulation. These findings align with prior -omics research by highlighting regional molecular differences possibly associated with cold adaptation. They also underscore the potential of methylation-derived proteomic proxies as a useful, indirect approach for studying proteomic variation when direct protein measurements are unavailable. While promising, this method warrants further validation, particularly with respect to potential genetic confounding. People living in subarctic regions such as Yakutia in the Russian Far East have adapted to some of the harshest environments in the world. These include extreme cold, long winter nights, and limited access to food. Scientists believe these adaptations are reflected in the body’s biology, but studies examining genes and other molecular traits have not fully explained how these changes occur. In this study, we used a new method that estimates protein levels in the body based on patterns of DNA methylation. DNA methylation is a type of chemical change on DNA that can show how the environment influences the body. We applied this method to blood samples from 245 healthy adults living in two regions of Russia, Yakutia and Central Russia. We found differences in 25 estimated protein levels between the two groups. These proteins are involved in important processes like managing energy use. To our knowledge, these proteins were not linked to genes found in previous epigenetic studies. This suggests the method may provide new insights. Our findings show that estimating protein levels from DNA can help uncover unique biological features in populations adapted to extreme environments. This approach could help guide future research on human adaptation and health.
DOI: 10.6084/m9.figshare.30363546
title: <i>Photinia glabra</i>-derived exosome-like nanovesicles mitigate skin inflammaging via dual regulation of inflammatory signaling and calcium homeostasis

description: Chronic low-grade inflammation accelerates skin aging, termed inflammaging. This study investigates whether <i>Photinia glabra-</i>derived exosome-like nanovesicles (PgELNs) can alleviate inflammaging by modulating inflammatory signaling and calcium homeostasis. PgELNs were isolated using tangential flow filtration and characterized by nanoparticle tracking analysis (NTA) and transmission electron microscopy (TEM). Human keratinocytes (HaCaT) were stimulated with tumor necrosis factor-α (TNF-α) and interferon-γ (IFN-γ) to model inflammaging. PgELN uptake, cell viability, senescence markers, cytokine expression, tight junction proteins, and calcium levels were assessed via flow cytometry, quantitative Real-Time Polymerase Chain Reaction (RT-PCR), immunoblotting, and transcriptomic profiling. PgELNs were efficiently internalized without cytotoxicity. In stimulated cells, PgELNs reduced proinflammatory cytokines, senescence-associated secretory phenotype (SASP) markers, and restored IL-10 and tight junction proteins. Transcriptome and pathway analyses revealed suppression of JAK/STAT signaling via STAT1 and ISG15 downregulation, reducing CXCL9/10 expression. This led to normalization of intracellular and extracellular Ca<sup>2+</sup> levels. PgELNs mitigate skin inflammaging by dual regulation of inflammation and calcium homeostasis. Targeting the STAT1–CXCL9/10 axis, PgELNs reduce senescence and preserve barrier integrity. These findings highlight PgELNs as a promising plant-derived nanotherapeutic for managing inflammation-associated skin aging.
DOI: 10.6084/m9.figshare.30363546.v1
title: <i>Photinia glabra</i>-derived exosome-like nanovesicles mitigate skin inflammaging via dual regulation of inflammatory signaling and calcium homeostasis

description: Chronic low-grade inflammation accelerates skin aging, termed inflammaging. This study investigates whether <i>Photinia glabra-</i>derived exosome-like nanovesicles (PgELNs) can alleviate inflammaging by modulating inflammatory signaling and calcium homeostasis. PgELNs were isolated using tangential flow filtration and characterized by nanoparticle tracking analysis (NTA) and transmission electron microscopy (TEM). Human keratinocytes (HaCaT) were stimulated with tumor necrosis factor-α (TNF-α) and interferon-γ (IFN-γ) to model inflammaging. PgELN uptake, cell viability, senescence markers, cytokine expression, tight junction proteins, and calcium levels were assessed via flow cytometry, quantitative Real-Time Polymerase Chain Reaction (RT-PCR), immunoblotting, and transcriptomic profiling. PgELNs were efficiently internalized without cytotoxicity. In stimulated cells, PgELNs reduced proinflammatory cytokines, senescence-associated secretory phenotype (SASP) markers, and restored IL-10 and tight junction proteins. Transcriptome and pathway analyses revealed suppression of JAK/STAT signaling via STAT1 and ISG15 downregulation, reducing CXCL9/10 expression. This led to normalization of intracellular and extracellular Ca<sup>2+</sup> levels. PgELNs mitigate skin inflammaging by dual regulation of inflammation and calcium homeostasis. Targeting the STAT1–CXCL9/10 axis, PgELNs reduce senescence and preserve barrier integrity. These findings highlight PgELNs as a promising plant-derived nanotherapeutic for managing inflammation-associated skin aging.
DOI: 10.6084/m9.figshare.30353589
title: Intranasal administration of thermoresponsive pterostilbene loaded nanoemulgel confers neuroprotection in a rat model of ischemic stroke

description: To develop and characterize Pterostilbene (PT)-loaded nanoemulgel (PNEG) and to evaluate its effect in rat model of ischemic stroke. PT-loaded nanoemulsion (PNE) was developed and further coated with chitosan and poloxamer-407 to obtain PNEG. It was characterized for particle size, zeta potential, morphology, entrapment efficiency, viscosity, stability, and ex-vivo mucoadhesive strength. Safety was assessed via in-vitro cytotoxicity assays and ex-vivo nasal mucosal compatibility. The therapeutic efficacy of PNEG was evaluated in a rat model of ischemic stroke, with assessments including neurobehavioral performances, oxidative stress, mitochondrial ultrastructure and complex activity, and pro-inflammatory cytokine levels. PNEG exhibited particle size of 65.68 ± 0.66 nm with a zeta potential of 9.77 ± 1.2. The formulation demonstrated enhanced mucoadhesive strength and thermoresponsive viscosity, promoting prolonged nasal residence time. In-vitro and ex-vivo assessments confirmed the formulation’s biocompatibility and non-toxicity. In-vivo, PNEG significantly enhanced neurological performance, including motor coordination, muscle strength, and cognition, while concurrently reducing oxidative stress, preserving mitochondrial integrity, and suppressing neuroinflammation in hippocampus and cortex of ischemic rats. Intranasal PNEG enabled sustained PT delivery with robust neuroprotection in ischemic stroke, highlighting its promise as a clinically translatable strategy for targeted brain therapy.