cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/4pjnmv5f2s
title: Graph Diameter as a Topological Descriptor for Hyperbranched Polymers: Insights from Stochastic Simulation of Ring-Opening Multibranching Polymerization of Glycidol

description: The Java simulation code that also computes graph diameters, for one set of parameters.
The simulated data series, with three parallel simulations for each parameter set.
DOI: 10.17877/tudodata-2025-mcbvo32t
title: Replication Data for "A Phase Microscope for Quantum Gases"

description: Experimental images and associated analysis codes used to generate the figures from both main text and supplementary material. Data presented in arXiv preprint https://arxiv.org/abs/2410.10611.
description: See README.md and README_ANALYSIS.md for notes on how to use the analysis code
description: Modified filehandling.py
DOI: 10.17632/4pjnmv5f2s.1
title: Graph Diameter as a Topological Descriptor for Hyperbranched Polymers: Insights from Stochastic Simulation of Ring-Opening Multibranching Polymerization of Glycidol

description: The Java simulation code that also computes graph diameters, for one set of parameters.
The simulated data series, with three parallel simulations for each parameter set.
DOI: 10.6084/m9.figshare.30655800
title: Integrated machine learning-driven QSAR modelling and systems biology approach for the identification of potential SARS-CoV-2 3CLpro inhibitors

description: SARS-CoV-2 3C-like protease (3CLpro) is an essential viral enzyme responsible for processing viral polyproteins and is a validated target for small-molecule therapeutic intervention. This study presents an integrative cheminformatics and systems biology framework to identify and evaluate potential 3CLpro inhibitors. A curated dataset of 919 compounds from the CHEMBL database was used to develop predictive QSAR models based on substructure fingerprints and 1D/2D molecular descriptors. The best-performing models achieved strong correlation coefficients (0.9736 for training and 0.7413 for testing), and key molecular features were identified using feature importance analysis. A web-based tool, 3CLpro-Pred was developed to enable rapid prediction of compound bioactivity. Molecular docking and dynamics simulations further validated QSAR findings by elucidating key atomic-level interactions at the protease active site. Top hit compounds were prioritized for systems-level analysis at the host-pathogen interface, where gene ontology and KEGG pathway enrichment revealed their potential to modulate key host signalling pathways. Critical regulatory genes, including <i>TBK1, PIK3CA, IKBKB, GSK3B</i>, and <i>CASP3</i>, were identified as major nodes linking compound action to antiviral and immune processes. This study delivers a combinatorial computational pipeline to accelerate the discovery of 3CLpro-targeted antivirals, providing mechanistic insights and a shortlist of candidates for future experimental validation.
DOI: 10.6084/m9.figshare.30655800.v1
title: Integrated machine learning-driven QSAR modelling and systems biology approach for the identification of potential SARS-CoV-2 3CLpro inhibitors

description: SARS-CoV-2 3C-like protease (3CLpro) is an essential viral enzyme responsible for processing viral polyproteins and is a validated target for small-molecule therapeutic intervention. This study presents an integrative cheminformatics and systems biology framework to identify and evaluate potential 3CLpro inhibitors. A curated dataset of 919 compounds from the CHEMBL database was used to develop predictive QSAR models based on substructure fingerprints and 1D/2D molecular descriptors. The best-performing models achieved strong correlation coefficients (0.9736 for training and 0.7413 for testing), and key molecular features were identified using feature importance analysis. A web-based tool, 3CLpro-Pred was developed to enable rapid prediction of compound bioactivity. Molecular docking and dynamics simulations further validated QSAR findings by elucidating key atomic-level interactions at the protease active site. Top hit compounds were prioritized for systems-level analysis at the host-pathogen interface, where gene ontology and KEGG pathway enrichment revealed their potential to modulate key host signalling pathways. Critical regulatory genes, including <i>TBK1, PIK3CA, IKBKB, GSK3B</i>, and <i>CASP3</i>, were identified as major nodes linking compound action to antiviral and immune processes. This study delivers a combinatorial computational pipeline to accelerate the discovery of 3CLpro-targeted antivirals, providing mechanistic insights and a shortlist of candidates for future experimental validation.
DOI: 10.5281/zenodo.17647564
title: Reproducibility Package for "Quantitative Discrimination of Harzburgite Tectonic Regimes via CoDA-Transformed XGBoost Modeling and SHAP Explainability"

description: This archive contains all code, scripts, processed datasets, model artifacts, and environment files required to reproduce the workflow described in the manuscript “Quantitative Discrimination of Harzburgite Tectonic Regimes via CoDA-Transformed XGBoost Modeling and SHAP Explainability” submitted to Geochemistry, Geophysics, Geosystems. It includes the Python scripts for data ingestion, ILR transformation, feature engineering, SMOTE balancing, nested cross-validation, XGBoost model training, the trained pipeline, and example data.
DOI: 10.6084/m9.figshare.30655867
title: A computational-based search of natural product derived multi-target ligands for the management of Alzheimer’s and Parkinson’s disease using structure-based pharmacophore modelling, virtual screening, MD docking, free energy analysis, ADMET profiling and DFT studies

description: Neurodegenerative disorders, including Alzheimer’s disease (AD) and Parkinson’s disease (PD), represent major global health concerns due to multifactorial pathology and lack of effective therapeutics. A comprehensive multi-target (MT) computational strategy was applied to identify natural product-derived therapeutics against four critical targets of AD and PD: acetylcholinesterase (AChE), dopamine receptor D2, monoamine oxidase B (MAO-B) and cyclooxygenase-2 (COX-2). Structure-based pharmacophore models were constructed for each target, validated and employed to virtually screen 2,54,850 compounds from the COCONUT database, following rigorous ADME-based filtering. Compounds with an average pharmacophore fit score ≥0.6 were shortlisted, yielding 55 promising candidates. These were examined through molecular dynamics (MD) docking using the CDOCKER algorithm. Four hits (N1–N4) displayed superior binding affinities relative to reference drugs donepezil (DNP) and safinamide (SAF). Binding free energy calculations further substantiated their interaction stability. ADMET analysis predicted favourable pharmacokinetic properties, efficient blood–brain barrier penetration and non-toxic profiles, with compound N1 (CNP0388746.0) emerging as the most promising candidate. Additionally, density functional theory (DFT) studies provided insights into the electronic characteristics and reactivity of top hits. Overall, this integrated in silico approach emphasized the potential of natural scaffolds as sustainable multi-target-directed ligands (MTDLs) for AD and PD therapeutics.
DOI: 10.6084/m9.figshare.30655867.v1
title: A computational-based search of natural product derived multi-target ligands for the management of Alzheimer’s and Parkinson’s disease using structure-based pharmacophore modelling, virtual screening, MD docking, free energy analysis, ADMET profiling and DFT studies

description: Neurodegenerative disorders, including Alzheimer’s disease (AD) and Parkinson’s disease (PD), represent major global health concerns due to multifactorial pathology and lack of effective therapeutics. A comprehensive multi-target (MT) computational strategy was applied to identify natural product-derived therapeutics against four critical targets of AD and PD: acetylcholinesterase (AChE), dopamine receptor D2, monoamine oxidase B (MAO-B) and cyclooxygenase-2 (COX-2). Structure-based pharmacophore models were constructed for each target, validated and employed to virtually screen 2,54,850 compounds from the COCONUT database, following rigorous ADME-based filtering. Compounds with an average pharmacophore fit score ≥0.6 were shortlisted, yielding 55 promising candidates. These were examined through molecular dynamics (MD) docking using the CDOCKER algorithm. Four hits (N1–N4) displayed superior binding affinities relative to reference drugs donepezil (DNP) and safinamide (SAF). Binding free energy calculations further substantiated their interaction stability. ADMET analysis predicted favourable pharmacokinetic properties, efficient blood–brain barrier penetration and non-toxic profiles, with compound N1 (CNP0388746.0) emerging as the most promising candidate. Additionally, density functional theory (DFT) studies provided insights into the electronic characteristics and reactivity of top hits. Overall, this integrated in silico approach emphasized the potential of natural scaffolds as sustainable multi-target-directed ligands (MTDLs) for AD and PD therapeutics.
DOI: 10.15468/dl.xeejyf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Leopardus colocolo",
 "Country is Spain"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044742-251025141854904/datasets/export for details.
DOI: 10.25549/usctheses-ouc11399m9h6
title: Studies in the Economics of Eldercare: Theory and Evidence from HRS and ATUS
DOI: 10.5281/zenodo.17648155
title: Galactic Pressure and Aging
DOI: 10.5281/zenodo.17648154
title: Galactic Pressure and Aging
DOI: 10.25549/usctheses-ouc11399m9h7
title: Learning From Biology:End-to-End Data-Driven Learning of Dexterous Manipulation in Robotics
DOI: 10.25549/usctheses-ouc11399m9ha
title: Investigating the Effects of Movement Behaviors on Biomarkers ofEndothelial Dysfunction in Youth with Overweight and Obesity
DOI: 10.25549/usctheses-ouc11399m9hb
title: Accessible Quality Diversity Optimization
DOI: 10.15468/dl.c84buq
title: Occurrence Download

description: A dataset containing 831 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Madagascar",
 "OccurrenceStatus is Present",
 "TaxonKey is Megachilidae"
 ]
}

 The dataset includes 831 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044743-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s4ebxy
title: Occurrence Download

description: A dataset containing 53 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-110.9688 27.44913, -110.8188 27.44913, -110.8188 27.59913, -110.9688 27.59913, -110.9688 27.44913))"
 ]
}

 The dataset includes 53 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044744-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25835/jup48h1c
title: Entwicklung eines linsenfreien Fluoreszenzmikroskops zur online Bioprozessanalytik - Skripte

description: In order to comply with the regulatory quality requirements of the U.S. Food and Drug Administration (FDA), the use of Process Analytical Technologies (PAT) is essential in pharmaceutical manufacturing. The goal is to ensure end-product quality by defining, measuring, and controlling critical process parameters. A central aspect of modern PAT strategies is the continuous, non-invasive monitoring of bioprocesses. This enables the timely acquisition of relevant parameters directly within the process, without the need for manual sampling.
In this context, a universal, single-use measuring cell was developed that combines scattered light and absorption measurement in a compact system. The sensor technology is based on a pre-sterilised, biocompatible disposable design and can be flexibly integrated into various reactor systems via a bypass. Scattered light measurement allows reliable determination of cell concentration, while absorption measurement can be used to monitor product formation and cell growth. The 3D-printed measuring cell is biocompatible, autoclavable, and highly adaptable to different process requirements.
For complementary process monitoring, a lensfree microscopy technology was developed that enables real-time imaging of biological components. This optical measurement method is based on the reconstruction of holographic images using numerical algorithms and allows high-resolution visualization of cellular structures. The technology enables direct determination of cell concentration within the process and thus allows continuous monitoring without manual sampling.
Based on initial successful trials, the system was extended with an additional light source for spatially resolved fluorescence measurement. This component is intended to enable a differentiated analysis of cellular states in real time and to further enhance the informational value of the data. The combination of single-use measuring cell, multimodal sensor technology, and lensfree microscopy thus forms a powerful platform for efficient and automated bioprocess monitoring. It represents a significant advancement in the implementation of modern PAT strategies in biopharmaceutical production.
DOI: 10.15468/dl.ygw7xx
title: Occurrence Download

description: A dataset containing 1227 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hamearis lucina",
 "HasCoordinate is true",
 "Geometry POLYGON ((6.224439 46.56151, 6.224439 48.43216, 8.984551 48.43216, 8.984551 46.56151, 6.224439 46.56151))",
 "Year is greater than or equal to 2020",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 1227 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044745-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rp8dmm
title: Occurrence Download

description: A dataset containing 0 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 PRINTF('%04d-%02d', "year", "month") yearmonth,
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, PRINTF('%04d-%02d', "year", "month"))) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, PRINTF('%04d-%02d', "year", "month"))) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, PRINTF('%04d-%02d', "year", "month"))) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, PRINTF('%04d-%02d', "year", "month"))) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, PRINTF('%04d-%02d', "year", "month"))) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, PRINTF('%04d-%02d', "year", "month"))) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 GBIF_WITHIN('POLYGON((-10 -40,25 -40,25 5,-10 5,-10 -40))', occurrence.decimallatitude, occurrence.decimallongitude) = TRUE
 AND occurrence.occurrencestatus = 'PRESENT'
 AND LOWER(occurrence.waterbody) = LOWER('Ocean')
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL AND occurrence."month" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 PRINTF('%04d-%02d', "year", "month")

 The dataset's 0 records were derived from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044734-251025141854904/datasets/export for details.
DOI: 10.17632/5zhgs9p39v
title: CICIDS2017-DOS (IB)

description: CICIDS2017-DOS (IB) is an imbalanced intrusion detection dataset derived from the CICIDS2017 collection and limited to benign traffic and five Denial-of-Service attack categories: DDoS, DoS GoldenEye, DoS Hulk, DoS Slowloris, and DoS SlowHTTPTest. The distribution remains intentionally skewed, with benign instances representing approximately 80% of the total samples, while each attack class contributes a smaller proportion of the dataset. This imbalance reflects real-world network traffic patterns, where malicious activity occurs sporadically compared to normal traffic.

The dataset was generated after consolidating raw files, removing incomplete or invalid entries, eliminating non-informative attributes, and converting textual fields such as IP addresses and timestamps into numeric form. The TUNE sampling process was applied with preserved skew conditions, allowing majority traffic to remain dominant while minority attack classes were retained in limited yet meaningful frequency. The resulting dataset includes 72 processed numerical features and is suitable for evaluating intrusion detection algorithms under realistic imbalance conditions, particularly in studies involving anomaly detection, rare event learning, and imbalanced classification strategies.

Cite

Panigrahi, R., & Borah, S. (2018). A detailed analysis of CICIDS2017 dataset for designing Intrusion Detection Systems. International Journal of Engineering & Technology, 7(3.24), 479-482.

Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization”, 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018.
DOI: 10.17632/5zhgs9p39v.1
title: CICIDS2017-DOS (IB)

description: CICIDS2017-DOS (IB) is an imbalanced intrusion detection dataset derived from the CICIDS2017 collection and limited to benign traffic and five Denial-of-Service attack categories: DDoS, DoS GoldenEye, DoS Hulk, DoS Slowloris, and DoS SlowHTTPTest. The distribution remains intentionally skewed, with benign instances representing approximately 80% of the total samples, while each attack class contributes a smaller proportion of the dataset. This imbalance reflects real-world network traffic patterns, where malicious activity occurs sporadically compared to normal traffic.

The dataset was generated after consolidating raw files, removing incomplete or invalid entries, eliminating non-informative attributes, and converting textual fields such as IP addresses and timestamps into numeric form. The TUNE sampling process was applied with preserved skew conditions, allowing majority traffic to remain dominant while minority attack classes were retained in limited yet meaningful frequency. The resulting dataset includes 72 processed numerical features and is suitable for evaluating intrusion detection algorithms under realistic imbalance conditions, particularly in studies involving anomaly detection, rare event learning, and imbalanced classification strategies.

Cite

Panigrahi, R., & Borah, S. (2018). A detailed analysis of CICIDS2017 dataset for designing Intrusion Detection Systems. International Journal of Engineering & Technology, 7(3.24), 479-482.

Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization”, 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018.
DOI: 10.17632/38d2ws2265.1
title: Coastal Sea Level Data from Tide Gauge and GNSS-IR at the Onsala Space Observatory, Sweden (June 2024–July 2025)

description: This dataset contains all processed results supporting the manuscript
“Continuous Coastal Absolute Sea Level Measurement from GNSS-IR: A Potential Tool for Satellite Altimetry Validation”, submitted to Remote Sensing of Environment.

It includes one year of continuous coastal observations from the Onsala Space Observatory (OSO) in Sweden, covering the period June 2024–July 2025. The dataset integrates multiple geodetic, oceanographic, and meteorological sources used to evaluate the capability of GNSS Interferometric Reflectometry (GNSS-IR) for absolute sea level monitoring.

Contents

GNSS-IR sea level: Raw and 15-minute averaged sea surface height time series derived from a horizon-pointing antenna (ONRA) and a zenith-pointing antenna (OSOU), processed using multi-GNSS RINEX data.

Tide gauge sea level: 1-minute sea level observations from the co-located OSO tide gauge, referenced to RH2000 height datum.

GNSS positioning: Daily coordinate time series of ONRA, ONRB and eight nearby SWEPOS stations, processed in PPP mode to characterize local land motion and establish the absolute reference frame.

Weather data: 1-minute atmospheric pressure, temperature, humidity, and wind speed data from the OSO meteorological station, used for tropospheric delay modeling and environmental context.

SWOT altimetry: Extracted sea surface height (SSH) data from the Surface Water and Ocean Topography (SWOT) satellite within 100 km of OSO, used for cross-validation with GNSS-IR and tide gauge records.

Solid Earth displacements: Modeled vertical displacements due to solid Earth tides, ocean tidal loading, and pole tides, applied for reference frame consistency.

Surveying and leveling measurements: Total-station and geometric leveling observations that link the GNSS antenna reference points, tide-gauge benchmark, and local reference markers to the RH2000 vertical datum. These measurements define the geometric relationships necessary for absolute sea-level determination.

Temporal coverage: June 2024 – July 2025
Temporal resolution: 1 minute, 15 minutes or 1 day, depending on data type
Spatial coverage: Onsala Space Observatory, west coast of Sweden (57.395° N, 11.915° E)

Purpose and usage:
This dataset supports the quantitative analysis, validation, and visualization presented in the manuscript. It enables replication of the figures and tables, and provides a reference for future studies on GNSS-IR-based coastal sea level monitoring, satellite altimetry validation, and regional vertical land motion at OSO.