cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18255933
title: Screening for Neurocognitive Abilities Post-COVID (SNAP-COVID): Scale Development and Validation

description:        Background. COVID-19, has drawn attention to its neurocognitive sequelae in individuals with Post-COVID Condition (PCC), yet standardized tools for screening and quantifying PCC-related cognitive impairment remain scarce. The present study aimed to develop and validate the Screening for Neurocognitive Abilities Post-COVID (SNAP-COVID) scale, a self-report instrument designed to capture current symptom burden and perceived changes in cognitive functioning relative to pre-COVID status. Methods. Data collection occurred in three phases between August 2024 and February 2025. Dataset A (N = 27) was used for test–retest reliability. Dataset B (N = 300) supported exploratory factor analysis (EFA), reliability testing, and convergent validity analyses with the Brain Fog Scale (BFS). Dataset C (N = 317) provided independent validation through confirmatory factor analysis (CFA).  Results. Initial EFA of the 30-item SNAP-COVID suggested a four-factor model, but item refinement yielded a robust three-factor, 24-item solution: (1) General Cognitive Functions (17 items, α = .948), (2) Sensory Hypersensitivity (4 items, α = .829), and (3) Language and Communication (3 items, α = .950). The total scale demonstrated excellent internal consistency (α = .95). Convergent validity was supported by significant correlations between SNAP impact scores and BFS scores (r = –.442, p < .001). CFA confirmed the three-factor structure with acceptable fit indices (χ²(249) = 677.29, p < .001; CFI = .882; TLI = .869; RMSEA = .074; SRMR = .032). Conclusions The SNAP-COVID scale is a reliable and valid instrument. Its multidimensional structure captures global and domain-specific difficulties, addressing a critical gap in post-infectious cognitive assessment.
DOI: 10.21996/4dd22bb1-e943-4510-9b20-f922fba96bd2
title: The perceived value of the academic library: a systematic review - excluded studies

description: Supplemental material – excluded studies
DOI: 10.15468/dl.7xgyrx
title: Occurrence Download

description: A dataset containing 7232 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((16.82054328259634 48.16218928968575, 16.948325144996918 48.154192843510934, 16.96019096206507 48.240309347447734, 16.83219411607169 48.248321497552425, 16.82054328259634 48.16218928968575))"
 ]
}

 The dataset includes 7232 records from 55 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002392-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zk955n
title: Occurrence Download

description: A dataset containing 2913 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Agrotis infusa",
 "Year 2015-2026"
 ]
}

 The dataset includes 2913 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002393-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18311159
title: Setaria verticillata (L.) P.Beauv. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:54
DOI: 10.5281/zenodo.18311160
title: Setaria verticillata (L.) P.Beauv. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:54
DOI: 10.5281/zenodo.17055456
title: Dataset for: Theoretical analysis of optical feedback-controlled emission of dual-wavelength lasers

description: This dataset contains the raw and processed data supporting the results presented in the article "Theoretical analysis of optical feedback-controlled emission of dual-wavelength lasers".

The data are provided as MATLAB .mat files. Each file corresponds to a figure (or a figure panel) in the manuscript.

Each .mat file contains a table with the following structure:





The first columns store the independent variables, such that each row corresponds to a specific combination of parameters.




The subsequent columns store the raw simulation results and the processed data used to generate the corresponding figure.




Each column has a header specifying the name of the corresponding variable or result.



The .mat files can be opened with MATLAB (any recent version) or compatible software such as GNU Octave.

These data are made openly available to enable verification of the results, reproduction of the figures, and reuse in related theoretical and numerical studies.
DOI: 10.5281/zenodo.17055455
title: Dataset for: Theoretical analysis of optical feedback-controlled emission of dual-wavelength lasers

description: This dataset contains the raw and processed data supporting the results presented in the article "Theoretical analysis of optical feedback-controlled emission of dual-wavelength lasers".

The data are provided as MATLAB .mat files. Each file corresponds to a figure (or a figure panel) in the manuscript.

Each .mat file contains a table with the following structure:





The first columns store the independent variables, such that each row corresponds to a specific combination of parameters.




The subsequent columns store the raw simulation results and the processed data used to generate the corresponding figure.




Each column has a header specifying the name of the corresponding variable or result.



The .mat files can be opened with MATLAB (any recent version) or compatible software such as GNU Octave.

These data are made openly available to enable verification of the results, reproduction of the figures, and reuse in related theoretical and numerical studies.
DOI: 10.15468/dl.drad7r
title: Occurrence Download

description: A dataset containing 74 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-74.926 -7.731902, -74.996904 -7.737462, -75.066068 -7.754006, -75.131794 -7.781127, -75.19246699999999 -7.818159, -75.246596 -7.864194, -75.292849 -7.9181, -75.33008700000001 -7.978553, -75.35739100000001 -8.044066000000001, -75.374083 -8.113028999999999, -75.37974800000001 -8.183745, -75.374241 -8.254473000000001, -75.357691 -8.323472000000001, -75.330501 -8.389041000000001, -75.293336 -8.449562999999999, -75.247108 -8.503546, -75.192953 -8.549658000000001, -75.13220800000001 -8.58676, -75.06636899999999 -8.613936000000001, -74.997062 -8.630515000000001, -74.926 -8.636088000000001, -74.854938 -8.630515000000001, -74.785631 -8.613936000000001, -74.719792 -8.58676, -74.659047 -8.549658000000001, -74.60489200000001 -8.503546, -74.55866399999999 -8.449562999999999, -74.52149900000001 -8.389041000000001, -74.494309 -8.323472000000001, -74.47775900000001 -8.254473000000001, -74.472252 -8.183745, -74.47791700000001 -8.113028999999999, -74.494609 -8.044066000000001, -74.521913 -7.978553, -74.559151 -7.9181, -74.60540399999999 -7.864194, -74.659533 -7.818159, -74.720206 -7.781127, -74.785932 -7.754006, -74.855096 -7.737462, -74.926 -7.731902))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 74 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002395-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e85vkj
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((105.648 -1.62482, 105.577709 -1.630386, 105.509147 -1.646946, 105.444002 -1.674092, 105.383877 -1.711158, 105.330251 -1.75723, 105.284445 -1.811175, 105.247586 -1.871666, 105.220583 -1.937212, 105.204102 -2.006202, 105.198549 -2.076936, 105.204063 -2.147673, 105.220508 -2.216671, 105.247483 -2.282232, 105.284323 -2.34274, 105.330123 -2.396704, 105.383755 -2.442796, 105.443898 -2.479879, 105.509072 -2.50704, 105.577669 -2.523609, 105.648 -2.529177, 105.718331 -2.523609, 105.786928 -2.50704, 105.852102 -2.479879, 105.912245 -2.442796, 105.965877 -2.396704, 106.011677 -2.34274, 106.048517 -2.282232, 106.075492 -2.216671, 106.091937 -2.147673, 106.097451 -2.076936, 106.091898 -2.006202, 106.075417 -1.937212, 106.048414 -1.871666, 106.011555 -1.811175, 105.965749 -1.75723, 105.912123 -1.711158, 105.851998 -1.674092, 105.786853 -1.646946, 105.718291 -1.630386, 105.648 -1.62482))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002397-260119153935675/datasets/export for details.
DOI: 10.15468/dl.bbnm33
title: Occurrence Download

description: A dataset containing 57709 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((3.3465639729340233 51.340657658051974, 3.346092615067062 51.3432840347708, 3.3448121062571503 51.345802340012376, 3.3427715426629874 51.348115772121375, 3.3400492681858474 51.35013539119345, 3.3367498750140987 51.351783542467366, 3.333000187766893 51.35299684693962, 3.328944385016093 51.35372864330098, 3.3247384463731433 51.35395078639074, 3.3205441403961964 51.35365473224803, 3.3165227870858653 51.35285186753736, 3.312829037850775 51.35157307052263, 3.309604915117269 51.34986752068139, 3.306974343263737 51.347800803276996, 3.305038382761742 51.34545238256601, 3.303871351204942 51.342912541716984, 3.303517979567099 51.34027890800006, 3.3039917111024892 51.33765269758586, 3.3052742055330735 51.33513482478653, 3.3073160644074378 51.33282202542208, 3.3100387466295973 51.330803143067236, 3.313337597935618 51.32915572032469, 3.317085876200419 51.32794302528568, 3.321139617356905 51.32721162647864, 3.3253431556527153 51.326989608540366, 3.3295350879608714 51.327285496378465, 3.333554455650396 51.3280879286468, 3.3372469096054616 51.32936609291692, 3.340470624594327 51.33107090604094, 3.3431017383139467 51.33313689491397, 3.345039107798478 51.3354847061918, 3.346208200949419 51.338024149486515, 3.3465639729340233 51.340657658051974))",
 "KingdomKey is one of (Animalia, Fungi, Plantae)",
 "Year is one of (2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026)"
 ]
}

 The dataset includes 57709 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002346-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/g79d-b108
title: "Purify-Enterprise-Ultimate: A Multi-modal Algorithmic Framework and Synthetic Dataset for Autonomous Radioactive Contamination Remediation"

description: "This dataset presents a production-grade algorithmic framework, Purify-Enterprise-Ultimate, designed for the complete resolution of radioactive contamination in complex environments. Unlike traditional methods, this system integrates high-dimensional data processing with atomic-level sensory forensics.\u200bThe framework features a 384D composite vector indexing system that fuses multi-modal data from Atomic Force Microscopy (AFM) and mass spectrometry. It is built upon a decentralized edge-swarm architecture ensuring operational continuity during extreme weather and communication blackouts. The core logic includes a parallelized Monte Carlo risk assessment engine (achieving 1-10^{-15} safety certainty) and an immutable ledger system for full-process auditing. This dataset includes the codified Python execution logic, system architecture designs, and synthetic validation data, providing a scalable engineering pathway for nuclear waste management and environmental recovery"
DOI: 10.15468/dl.s3x97y
title: Occurrence Download

description: A dataset containing 280280 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Spain, Portugal)",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "CoordinateUncertaintyInMeters is less than 1000",
 "TaxonKey is one of (Paridae, Phylloscopidae, Certhiidae, Sittidae)"
 ]
}

 The dataset includes 280280 records from 64 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002356-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18310903
title: Dataset: A Data-Centric Approach to Urban Building Footprint Extraction using Graph Neural Networks and Assessed OpenStreetMap Data (Algiers, Algeria)

description: Dataset Description

This repository contains the processed geospatial data used in the research paper "A Data-Centric Approach to Urban Building Footprint Extraction using Graph Neural Networks and Assessed OpenStreetMap Data."

The dataset focuses on a complex urban area in Algiers, Algeria, and addresses the challenge of label noise in Volunteered Geographic Information (VGI). It includes a high-confidence ground truth layer generated through a multi-source cross-validation pipeline (integrating OpenStreetMap, Google Open Buildings, and Overture Maps) and temporal NDVI stability analysis.

Contents:



Sentinel-2 Composite: A 15-channel multi-spectral composite image (resampled to 10m resolution) containing RGB, NIR, SWIR, and spectral indices (NDVI, NDBI, NDSI).

Curated Ground Truth Mask: A binary raster mask where 1 indicates a high-confidence building footprint and 0 indicates non-building. This mask was generated using the "Algorithm 1" reliability scoring method described in the paper.

Gold Standard Validation Set: A manually labeled vector dataset (GeoJSON) for a specific high-density urban patch, used for rigorous performance evaluation.

Superpixel Segments: Pre-computed segments generated using the Segment Anything Model (SAM) adapted for remote sensing.


This dataset is designed to benchmark Graph Neural Networks (GNNs) and other deep learning models on African urban morphologies.
DOI: 10.5281/zenodo.18310902
title: Dataset: A Data-Centric Approach to Urban Building Footprint Extraction using Graph Neural Networks and Assessed OpenStreetMap Data (Algiers, Algeria)

description: Dataset Description

This repository contains the processed geospatial data used in the research paper "A Data-Centric Approach to Urban Building Footprint Extraction using Graph Neural Networks and Assessed OpenStreetMap Data."

The dataset focuses on a complex urban area in Algiers, Algeria, and addresses the challenge of label noise in Volunteered Geographic Information (VGI). It includes a high-confidence ground truth layer generated through a multi-source cross-validation pipeline (integrating OpenStreetMap, Google Open Buildings, and Overture Maps) and temporal NDVI stability analysis.

Contents:



Sentinel-2 Composite: A 15-channel multi-spectral composite image (resampled to 10m resolution) containing RGB, NIR, SWIR, and spectral indices (NDVI, NDBI, NDSI).

Curated Ground Truth Mask: A binary raster mask where 1 indicates a high-confidence building footprint and 0 indicates non-building. This mask was generated using the "Algorithm 1" reliability scoring method described in the paper.

Gold Standard Validation Set: A manually labeled vector dataset (GeoJSON) for a specific high-density urban patch, used for rigorous performance evaluation.

Superpixel Segments: Pre-computed segments generated using the Segment Anything Model (SAM) adapted for remote sensing.


This dataset is designed to benchmark Graph Neural Networks (GNNs) and other deep learning models on African urban morphologies.
DOI: 10.15468/dl.9vrf6c
title: Occurrence Download

description: A dataset containing 5314 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Madagascar",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Apidae, Halictidae, Melittidae, Megachilidae, Colletidae, Andrenidae)"
 ]
}

 The dataset includes 5314 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002398-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16740964
title: Data Supporting: Optimizing LoRaWAN Throughput in Maritime Environments through ACM under Rayleigh Fading

description: This dataset supports the article “Optimizing LoRaWAN Throughput in Maritime Environments through Adaptive Coding and Modulation under Rayleigh Fading”.It includes simulation outputs and MATLAB source code for reproducing all figures and results in the study. Files include throughput, PER, energy efficiency, spectral efficiency, and an ACM algorithm function.
DOI: 10.5281/zenodo.18311167
title: Data Supporting: Optimizing LoRaWAN Throughput in Maritime Environments through ACM under Rayleigh Fading

description: This dataset supports the article “Optimizing LoRaWAN Throughput in Maritime Environments through Adaptive Coding and Modulation under Rayleigh Fading”.It includes simulation outputs and MATLAB source code for reproducing all figures and results in the study. Files include throughput, PER, energy efficiency, spectral efficiency, and an ACM algorithm function.
DOI: 10.48323/accbx-jmd73
title: Rainfall and subsurface stormflow event characteristics

description: The dataset contains rainfall and subsurface stormflow events characteristics, as well as antecedent wetness metrics used in the paper: 

"Influence of rainfall event characteristics and antecedent conditions on subsurface stormflow response of two forested hillslopes"

Authors: Emanuel Thoenes, Theresa Blume, Markus Weiler, Bernhard Kohl, Luisa Hopp, Stefan Achleitner

 

Overview

The rainfal and subsurface stormflow data were collected in the Black Forest (south of Freiburg, Germany). Subsurface stormflow was measured at two trenched hillslopes.  Rainfall and subsurface stormflow events were delineated and their characteristics computed. For more info refere to the readme file and associated study.  

 

For questions please contact:

emanuel.thoenes@uibk.ac.at

stefan.achleitner@uibk.ac.at
DOI: 10.48323/89ts5-vg424
title: Rainfall and subsurface stormflow event characteristics

description: The dataset contains rainfall and subsurface stormflow events characteristics, as well as antecedent wetness metrics used in the paper: 

"Influence of rainfall event characteristics and antecedent conditions on subsurface stormflow response of two forested hillslopes"

Authors: Emanuel Thoenes, Theresa Blume, Markus Weiler, Bernhard Kohl, Luisa Hopp, Stefan Achleitner

 

Overview

The rainfal and subsurface stormflow data were collected in the Black Forest (south of Freiburg, Germany). Subsurface stormflow was measured at two trenched hillslopes.  Rainfall and subsurface stormflow events were delineated and their characteristics computed. For more info refere to the readme file and associated study.  

 

For questions please contact:

emanuel.thoenes@uibk.ac.at

stefan.achleitner@uibk.ac.at
DOI: 10.25326/806
title: Daily IASI/Metop-A ULB-LATMOS ozone (O3) L2 product (vertical profile and columns – AC SAF/EUMETSAT processing)

description: The daily IASI/Metop-A ozone (O<sub>3</sub>) dataset is processed by EUMETSAT Application Ground Segment using the Fast Optimal Retrievals on Layers for IASI (FORLI) software [(Hurtmans et al., 2012)](https://doi.org/10.1016/j.jqsrt.2012.02.036) in the [AC SAF](https://acsaf.org/) framework. The IASI O<sub>3</sub> retrievals are performed in the 1025-1075 cm<sup>-1</sup> spectral range using the optimal estimation method and tabulated absorption cross sections at various pressures and temperatures to speed up the radiative transfer calculation. A single a priori is used in the retrieval scheme. The temperature, pressure, humidity profiles and cloud fractions used in FORLI are those from the EUMETSAT Level 2 processor. Only pixels associated with cloud fraction below 13 % are processed. The IASI O<sub>3</sub> product is a vertical profile given as partial columns in moles per square meter in 40 layers between the surface and 40 km, with an extra layer from 40 km to the top of the atmosphere. The dataset consists in 2 files per day of observation:
 
- a file containing the total, tropospheric and 0-6km partial columns along with their total error associated. 
- a file containing the retrieved profile, the pressure levels associated with retrieval layers, and other relevant information such as a general quality flag, the a priori profile, the total error profile, the air partial column profile, and the averaging kernel (AK) matrix, on the same vertical grid. Total or partial columns can be directly calculated from the retrieved vertical profile by summing ozone partial columns on the ad hoc layers.

The dataset consists of a consistent record, processed using the same version of the EUMETSAT Level 2 processor and FORLI software (FORLI v20151001):

- Climate Data Record (CDR): available from July 2007 to December 2019.

The CDR product, which provides stable, high-quality data for climate studies is built from the EUMETSAT IASI O<sub>3</sub>  Climate Data Record Release 1, processed with Version 6.5 of the processor (DOI: 10.15770/EUM&#95;SAF&#95;AC&#95;0051).
DOI: 10.24377/ljmu.d.00000256
title: Validation workflow and derived comparison data for: Evaluating the accuracy of the EcoservR toolkit for fine-resolution habitat mapping

description: This repository contains the validation workflow and derived comparison dataset used in the study Evaluating the accuracy of the EcoservR toolkit for fine-resolution habitat mapping. The materials support full reproducibility of the accuracy assessment conducted for two study areas in northern England (Merseyside and the North York Moors). The deposit includes R scripts used for systematic sampling, spatial intersection of predicted and reference datasets, construction of confusion matrices, area-weighted accuracy estimation following Olofsson et al. (2014), and generation of figures and summary tables. In addition, a non-spatial tabular dataset is provided containing, for each validation point, the predicted habitat class, the reference survey class, and the adjudicated outcome. No spatial geometries or licensed base mapping are included. Due to third-party licensing restrictions associated with Ordnance Survey MasterMap and related products, the underlying habitat basemaps and spatial layers cannot be shared. However, all analytical steps required to reproduce the validation results are fully documented and openly available.
description: Rule-based spatial analysis and statistical validation workflow implemented in R