cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17105607
title: Codes and data regarding the article: "Neuromorphic Photonic Circuits with Nonlinear Dynamics and Memory for Time Sequence Classification".

description: This upload complements the paper "Neuromorphic Photonic Circuits with Nonlinear Dynamics and Memory for Time Sequence Classification".

Regarding Section 3.2 " Classification using Single Pixel Representations with Optical Memory":



"ML_singlePix2.py" employs the output traces (measured at the output ports of the MRR network) to train and test a linear machine learning (ML) model (logistic regression) employing as features a single time sample for each output trace.

"ML_singlePix2_expBaseline.py" and "ML_singlePix2_teoBaseline.py" are similar to "ML_singlePix2.py" and provide respectively the experimental baseline results (based on acquistion of optical data from the chip, without the processing from the MRR network, i.e. out of resonance) and the theoretical baseline results (where the linear classifier is directly applied to the original datasets, without any optical/analog convertion).

"ML_singlePix2_increasingNumberOfRpepr" is also similar to "ML_singlePix2.py", with the difference that, given a time sample, it trains and evaluates the linear ML model  considering an increasing number of output representations (i.e., an increasing number of features).

"ResultsAnalysis_singlePix2.py" analyzes the results from the previous codes, and produces the plots presented on the paper.


Regarding Section "5.2.1. Data Acquisition":



"main_parsing.py" and "parsing.py" parse the measured raw data and save them into a series of flattened images.


Regarding Section "5.4.1. Linear Classifier (LC)":



"MNIST_logistic_regression.py" performs the logistic regression on the original (non photonics processed) dataset.

"MRR_singleport.py" applies the logistic regression to a single photonic representation.

"MRR_Nports.py" applies it to N representations coming from different ports.


Regarding Section "5.4.2. Multilayer ANN in Software":



 "MNIST_multi_layers.py" trains and test a ANN with 1 hidden layer on the original (non photonics processed) dataset


The raw experimental data (i.e. the output traces of the MRR network) can be obtained from the authors by reasonable request.
DOI: 10.5281/zenodo.17105606
title: Codes and data regarding the article: "Neuromorphic Photonic Circuits with Nonlinear Dynamics and Memory for Time Sequence Classification".

description: This upload complements the paper "Neuromorphic Photonic Circuits with Nonlinear Dynamics and Memory for Time Sequence Classification".

Regarding Section 3.2 " Classification using Single Pixel Representations with Optical Memory":



"ML_singlePix2.py" employs the output traces (measured at the output ports of the MRR network) to train and test a linear machine learning (ML) model (logistic regression) employing as features a single time sample for each output trace.

"ML_singlePix2_expBaseline.py" and "ML_singlePix2_teoBaseline.py" are similar to "ML_singlePix2.py" and provide respectively the experimental baseline results (based on acquistion of optical data from the chip, without the processing from the MRR network, i.e. out of resonance) and the theoretical baseline results (where the linear classifier is directly applied to the original datasets, without any optical/analog convertion).

"ML_singlePix2_increasingNumberOfRpepr" is also similar to "ML_singlePix2.py", with the difference that, given a time sample, it trains and evaluates the linear ML model  considering an increasing number of output representations (i.e., an increasing number of features).

"ResultsAnalysis_singlePix2.py" analyzes the results from the previous codes, and produces the plots presented on the paper.


Regarding Section "5.2.1. Data Acquisition":



"main_parsing.py" and "parsing.py" parse the measured raw data and save them into a series of flattened images.


Regarding Section "5.4.1. Linear Classifier (LC)":



"MNIST_logistic_regression.py" performs the logistic regression on the original (non photonics processed) dataset.

"MRR_singleport.py" applies the logistic regression to a single photonic representation.

"MRR_Nports.py" applies it to N representations coming from different ports.


Regarding Section "5.4.2. Multilayer ANN in Software":



 "MNIST_multi_layers.py" trains and test a ANN with 1 hidden layer on the original (non photonics processed) dataset


The raw experimental data (i.e. the output traces of the MRR network) can be obtained from the authors by reasonable request.
DOI: 10.25574/25852
title: Chandra X-ray Observatory ObsId 25852

description: AN X-RAY CENSUS OF THE YOUNG STELLAR POPULATION IN THE LOWEST METALLICITY PROTOCLUSTER IN THE GALAXY
DOI: 10.25574/26259
title: Chandra X-ray Observatory ObsId 26259

description: AN X-RAY CENSUS OF THE YOUNG STELLAR POPULATION IN THE LOWEST METALLICITY PROTOCLUSTER IN THE GALAXY
DOI: 10.5281/zenodo.15282705
title: Dominant Drivers of Soil Multifunctionality Change: Interactions Among Soil, Plants, and the Atmosphere

description: This study provides two integrated datasets. The first is the data dataset, which compiles detailed observations from different soil depths in alluvial fan areas, covering multiple soil nutrients, core indicators of soil multifunctionality (SMF), and vegetation community information. The second is the response ratio dataset, derived from response ratio calculations, which reflects the differences in SMF and key nutrient contents among plant communities relative to the overall mean level. Together, these datasets offer a solid foundation for exploring the complex characteristics and underlying mechanisms of soil ecosystems under different vegetation communities.
DOI: 10.5281/zenodo.17122063
title: Dominant Drivers of Soil Multifunctionality Change: Interactions Among Soil, Plants, and the Atmosphere

description: This study provides two integrated datasets. The first is the data dataset, which compiles detailed observations from different soil depths in alluvial fan areas, covering multiple soil nutrients, core indicators of soil multifunctionality (SMF), and vegetation community information. The second is the response ratio dataset, derived from response ratio calculations, which reflects the differences in SMF and key nutrient contents among plant communities relative to the overall mean level. Together, these datasets offer a solid foundation for exploring the complex characteristics and underlying mechanisms of soil ecosystems under different vegetation communities.
DOI: 10.17632/c89n64y2x5.1
title: VitralColor-12: A Synthetic Twelve-Color Segmentation Dataset from GPT-Generated Stained-Glass Images

description: VitralColor-12, a synthetic dataset for color classification and segmentation, utilizes LLMs in specific GPT-5 and DALL·E 3 models to generate images of stained-glass. This approach simplifies labeling by using the dark steel structure supporting the glass as a guide, which provides easy regions to label with a single color per region. After that, we obtain the images and use at least one hand-labelled centroid per color to automatically cluster all pixels based on Euclidean distance and morphological operations, including erosion and dilation with two iterations per operation, and a kernel size of 2 pixels. This process enables us to label a classification dataset and generate segmentation maps automatically. Our dataset comprises 910 images, organized into 70 generated images and 12 pixel segmentation maps—one for each color, which includes 9,509,524 labeled pixels, 1,794,758 of which are unique. We include tables with pixel values in RGB, HSL, CIELAB, and YCbCr color representations, enabling detailed color analysis and training of machine learning algorithms in different color spaces. Furthermore, we also included descriptive statistics and ΔE76, ΔE94, and CIELAB a vs b Chromacity, which prove the distribution, applicability, and realistic perceptual structures, including warm, neutral, and cold colors, as well as the high contrast between black and white colors, offering meaningful perceptual clusters, reinforcing its utility for color segmentation and classification.
DOI: 10.17632/x97959dt4f
title: Adherence To Meat Hygiene Practices and Its Associated Factors Among Butchery Shop Operators at Ilala Municipality, Dar es Salaam

description: This is the SPSS Dataset from my research on Adherence to meat hygiene practices a study that was based in Tanzania. The partcipants in the data are butchery shop operators
DOI: 10.17632/x97959dt4f.1
title: Adherence To Meat Hygiene Practices and Its Associated Factors Among Butchery Shop Operators at Ilala Municipality, Dar es Salaam

description: This is the SPSS Dataset from my research on Adherence to meat hygiene practices a study that was based in Tanzania. The partcipants in the data are butchery shop operators
DOI: 10.15468/dl.u3q328
title: Occurrence Download

description: A dataset containing 23837 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tirumala"
 ]
}

 The dataset includes 23837 records from 159 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003825-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fhj9vs
title: Occurrence Download

description: A dataset containing 382 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aratinga solstitialis"
 ]
}

 The dataset includes 382 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003824-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8sth63
title: Occurrence Download

description: A dataset containing 8741 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Regina"
 ]
}

 The dataset includes 8741 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003826-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/zpr9bkbn8z
title: Adherence To Meat Hygiene Practices and Its Associated Factors Among Butchery Shop Operators at Ilala Municipality, Dar es Salaam

description: This is the SPSS Dataset from my research on Adherence to meat hygiene practices a study that was based in Tanzania. The partcipants in the data are butchery shop operators
DOI: 10.17632/zpr9bkbn8z.2
title: Adherence To Meat Hygiene Practices and Its Associated Factors Among Butchery Shop Operators at Ilala Municipality, Dar es Salaam

description: This is the SPSS Dataset from my research on Adherence to meat hygiene practices a study that was based in Tanzania. The partcipants in the data are butchery shop operators
DOI: 10.17632/7gb7mvwzxf.1
title: Bundle-Based Nursing Interventions for CLABSI Prevention in Saudi Government Hospitals: A Meta-Analysis of Compliance and Infection Outcomes Across Indexed Studies (September 2015–September 2025)

description: Background:
Central Line-Associated Bloodstream Infections (CLABSIs) are among the most preventable yet persistent healthcare-associated infections in intensive care units (ICUs). In Saudi Arabia, government hospitals have increasingly adopted bundle-based nursing protocols to mitigate CLABSI risks. This meta-analysis evaluates the effectiveness of nursing-led interventions in reducing CLABSI incidence and improving compliance across public-sector ICUs.

Methods:
A systematic search of indexed databases—including PubMed, Scopus, and the Saudi Digital Library—was conducted to identify peer-reviewed studies published between September 2015 and September 2025. Inclusion criteria focused on studies conducted exclusively in Ministry of Health, military, and university-affiliated hospitals. Eligible studies reported on nursing compliance with CLABSI prevention bundles and associated infection outcomes. Data were extracted using a standardized matrix and analyzed using a random-effects model to account for institutional variability.

Results:
Fifteen studies met inclusion criteria, encompassing 1,842 ICU patients across seven regions. Bundle components consistently included hand hygiene, maximal barrier precautions, chlorhexidine skin antisepsis, and daily line necessity review. Pooled analysis revealed a significant reduction in CLABSI rates (RR = 0.41, 95% CI: 0.30–0.55) in units with high nursing compliance (&gt;80%). Facilities with structured audits, electronic surveillance, and continuous nurse education demonstrated the most substantial improvements. Moderate heterogeneity (I² = 46%) reflected differences in staffing ratios, training intensity, and bundle fidelity.

Conclusion:
Nursing-led implementation of CLABSI prevention bundles in Saudi government hospitals is strongly associated with reduced infection rates. High compliance, supported by institutional oversight and standardized protocols, is essential to sustaining outcomes. These findings reinforce the need for national policy alignment, continuous nurse training, and robust surveillance systems to optimize infection control across public-sector ICUs.
DOI: 10.17632/rw5tvysk4r
title: Vitamin D and Depression: Systematic Review and Bayesian Meta-analysis of Mendelian Randomization Evidence

description: Background: Observational links between low 25(OH)D and depression may reflect confounding or reverse causation.
Methods: We systematically reviewed Mendelian-randomization (MR) studies through November 2024, appraising instrument strength, pleiotropy safeguards, bidirectionality, and dose–response/nonlinearity; certainty was graded using an MR-adapted GRADE framework. Because of between-study heterogeneity and scale differences, we did not perform conventional frequentist pooling. Instead, we conducted a de novo Bayesian random-effects meta-analysis of study-level MR estimates (harmonized to log-odds per 10 nmol/L where possible) and synthesized available nonlinear contrasts.
Results: Sixteen studies met inclusion criteria. Most linear MR estimates were null or near-null. Method-dependent signals appeared under correlated-instrument models and in low-quantile/nonlinear analyses. Bidirectional MR more often supported the reverse direction (depression → lower 25(OH)D). Certainty for nonlinearity was downgraded owing to missing stratum-level instrument strength and incomplete multiplicity control. In the Bayesian meta-analysis, the pooled average effect suggested a small protective association of higher 25(OH)D with depression risk (OR ≈ 0.988 per 10 nmol/L; posterior P [μ &lt; 0] = 0.997). Available nonlinear syntheses indicated stronger benefit at low 25(OH)D with flattening beyond ~50–60 nmol/L.
Conclusions: Current MR evidence does not support a large population-wide linear effect of 25(OH)D on depression. Any average effect appears small; signals confined to severely deficient strata should be considered hypothesis-generating and require replication across methods, datasets, and ancestries. Universal supplementation for depression prevention is not supported; testing and correction of marked deficiency remain reasonable.
Keywords: Vitamin D; Depression; Mendelian Randomization; Systematic Review; Threshold Effect; Causal Inference
DOI: 10.15468/dl.r582vt
title: Occurrence Download

description: A dataset containing 74010 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Cardinalis cardinalis)",
 "Country is one of (Anguilla, Antigua and Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bolivia (Plurinational State of), Bonaire, Sint Eustatius and Saba, Bouvet Island, Brazil, Cayman Islands, Chile, Colombia, Costa Rica, Cuba, Curaçao, Dominica, Dominican Republic, Ecuador, El Salvador, Falkland Islands (Malvinas), French Guiana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Montserrat, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Saint Barthélemy, Saint Kitts and Nevis, Saint Lucia, Saint Martin (French part), Saint Vincent and the Grenadines, Sint Maarten (Dutch part), South Georgia and the South Sandwich Islands, Suriname, Trinidad and Tobago, Turks and Caicos Islands, Uruguay, Venezuela (Bolivarian Republic of), Virgin Islands (British), Virgin Islands (U.S.))"
 ]
}

 The dataset includes 74010 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003770-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/rw5tvysk4r.1
title: Vitamin D and Depression: Systematic Review and Bayesian Meta-analysis of Mendelian Randomization Evidence

description: Background: Observational links between low 25(OH)D and depression may reflect confounding or reverse causation.
Methods: We systematically reviewed Mendelian-randomization (MR) studies through November 2024, appraising instrument strength, pleiotropy safeguards, bidirectionality, and dose–response/nonlinearity; certainty was graded using an MR-adapted GRADE framework. Because of between-study heterogeneity and scale differences, we did not perform conventional frequentist pooling. Instead, we conducted a de novo Bayesian random-effects meta-analysis of study-level MR estimates (harmonized to log-odds per 10 nmol/L where possible) and synthesized available nonlinear contrasts.
Results: Sixteen studies met inclusion criteria. Most linear MR estimates were null or near-null. Method-dependent signals appeared under correlated-instrument models and in low-quantile/nonlinear analyses. Bidirectional MR more often supported the reverse direction (depression → lower 25(OH)D). Certainty for nonlinearity was downgraded owing to missing stratum-level instrument strength and incomplete multiplicity control. In the Bayesian meta-analysis, the pooled average effect suggested a small protective association of higher 25(OH)D with depression risk (OR ≈ 0.988 per 10 nmol/L; posterior P [μ &lt; 0] = 0.997). Available nonlinear syntheses indicated stronger benefit at low 25(OH)D with flattening beyond ~50–60 nmol/L.
Conclusions: Current MR evidence does not support a large population-wide linear effect of 25(OH)D on depression. Any average effect appears small; signals confined to severely deficient strata should be considered hypothesis-generating and require replication across methods, datasets, and ancestries. Universal supplementation for depression prevention is not supported; testing and correction of marked deficiency remain reasonable.
Keywords: Vitamin D; Depression; Mendelian Randomization; Systematic Review; Threshold Effect; Causal Inference
DOI: 10.15468/pmsm72
title: Geochemical data on otoliths of chaetodon speculum from New Caledonia

description: &lt;p&gt;Coral reefs are one of the most diverse ecosystems on Earth. They are currently exposed to increasing levels of anthropogenic perturbations. Several recent reviews point to the lack of good indicators for these perturbations especially to monitor their effects on fish populations or fish assemblages. The SW lagoon of New Caledonia is an ideal location to test indicator species in this context as contrasting sites are present within a small geographical range. This study analysed fish from four sites, one with heavy industrial pollution, another dominated by domestic waste, a third with historic mining activities, and the fourth as a control. The butterfly fish, Chaetodon speculum, was chosen to determine C. speculum’s potential as an indicator species due to its link to coral, its sedentary behaviour and its wide geographical distribution. The size distribution, growth rate, age distribution and whole otolith composition were analysed at each site. Age and mean growth rate were analysed from daily increments of the otoliths. The concentrations of eight elements (Li, Mg, Co, Cu, Rb, Sr, and Ba) were measured by ICP-MS in the otoliths. The sites under anthropogenic impact were distinct from the control site by fish size frequencies, age distributions, and the chemical content of their otoliths. The chemical elements Mg, Co, Cu, and Rb showed differences amongst sites. Fish belonging to the sites furthest from Noume´a could be discriminated in nearly 80% of samples or 60% of the cases when otolith weight or fish age respectively were taken into account. Ni concentrations of the otoliths were also higher in the bays where water concentrations of this element were known to be higher, but these differences were no longer significant once corrected for otolith weight.&lt;/p&gt;
DOI: 10.17632/8ht6rhntfy
title: Pyoderma Gangrenosum: A Retrospective Cohort Study of Comorbidities and Therapies in over 65,000 patients.

description: Supplementary Material
DOI: 10.17632/8ht6rhntfy.1
title: Pyoderma Gangrenosum: A Retrospective Cohort Study of Comorbidities and Therapies in over 65,000 patients.

description: Supplementary Material
DOI: 10.5281/zenodo.17122178
title: Parametry gwiazd próbki, ich oszacowany wiek oraz wykresy izochron
DOI: 10.5281/zenodo.17122177
title: Parametry gwiazd próbki, ich oszacowany wiek oraz wykresy izochron
DOI: 10.7910/dvn/rvlj6u
title: Replication Data for Ephemeral Shark Nurseries in Northern Gulf dataset

description: Catch per unit effort (CPUE) data for bull sharks (Carcharhinus leucas) and blacktip sharks (C. limbatus) collected by TPWD and ADCNR gillnet monitoring from 1982-2023
description: Metadata:
Year: sampling year
Region: broad region of data collection
Site: location of data collection
Species: shark species
CPUE: catch per unit effort (sharks caught in gillnets)
DOI: 10.15468/dn7huy
title: PANGAEA - Data from Global environmental change: the Northern North Atlantic

description: This dataset contains data from the DFG Research Project SFB313 "Global Environmental Change: The Northern North Atlantic". These data are derived from PANGAEA.
DOI: 10.15468/dl.jzquhj
title: Occurrence Download

description: A dataset containing 386 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Canis lupus lupus"
 ]
}

 The dataset includes 386 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003828-250914085247600/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.30128311
title: Vehicle_High_Beam_Headlights.zip

description: (1) According to the survey, Vehicle_High_Beam_Headlights_V1is a dataset of vehicles high beam headlights in real scenes from the perspective of video surveillance cameras, providing valuable resources for researchers and developers.(2) The Vehicle_High_Beam_Headlights_V1contains images of various types of truck high beams, car high beams, SUV high beams, MPV high beams and van high beams, etc., effectively promoting the research on vehicle detection at night.
DOI: 10.6084/m9.figshare.30128311.v1
title: Vehicle_High_Beam_Headlights.zip

description: (1) According to the survey, Vehicle_High_Beam_Headlights_V1is a dataset of vehicles high beam headlights in real scenes from the perspective of video surveillance cameras, providing valuable resources for researchers and developers.(2) The Vehicle_High_Beam_Headlights_V1contains images of various types of truck high beams, car high beams, SUV high beams, MPV high beams and van high beams, etc., effectively promoting the research on vehicle detection at night.